Atomic spectrometry update – a review of advances in environmental analysis

Contents

• Atomic Spectrometry Update (ASU) – Call for new writers

• 1 Introduction

• 2 Air analysis
  • 2.1 Review papers
  • 2.2 Sampling techniques
  • 2.3 Reference materials
  • 2.4 Sample preparation
  • 2.5 Instrumental analysis

• 3 Water analysis
  • 3.1 Reviews
  • 3.2 Certified reference materials
  • 3.3 Sample preconcentration
  • 3.4 Speciation
  • 3.5 Nano- and micromaterials
  • 3.6 Instrumental analysis

• 4 Analysis of soils, plants and related materials
  • 4.1 Review papers
  • 4.2 Reference materials
  • 4.3 Sample preparation
  • 4.4 Instrumental analysis

• 5 Analysis of geological materials
  • 5.1 Reference materials and data quality
  • 5.2 Sample preparation, dissolution and chemical separation
  • 5.3 Instrumental analysis
  • 5.4 Software and databases

• 6 Abbreviations

• Conflicts of interest

• References

---

Abstract

Highlights in the field of air analysis included: a new focus on measuring micro- and nanoplastic particles in air, the development of hyphenated ICP-MS systems for in situ sampling and measurement of airborne metallic particles and the reported use of wearable black carbon sensors for measuring exposure to diesel fumes within the workplace. Significant advancements in the analysis of waters have been made in developing novel resin materials and new protocols for existing commercially available resins, aimed at the determination and speciation of trace levels of metals and metalloids in water matrices. These developments have been validated for sample purification and pre-concentration. In addition to traditional column chemistry, on-line hyphenated techniques were employed to enhance speciation analysis, with optimized methods enabling faster analysis and facilitating a more holistic approach by allowing the simultaneous detection of multiple species or elements in a single run. Efforts have also been directed towards detecting particles in the micro- and nanometer range, broadening the analytical scope beyond the ionic fraction. This year, the focus shifted from natural and engineered nanoparticles towards the critical field of plastic pollution, with several innovative methodologies introduced. Furthermore, to achieve better precision and lower detection limits in the field of MS/MS, numerous studies explored the behaviour of gases and reactions within reaction cells, contributing to the refinement of these techniques. In the analysis of soils and plants, methods aimed at improving the efficiency of green solvents were again prominent. Developments in AES were largely driven by the desire to create small, low-cost, low-power-consumption instrumentation suitable for field deployment. The study of NPs in soil and plant systems continued to be a focus for sp-ICP-MS. The past year has again seen a large volume of publications featuring LIBS, with particular interest in methods to enhance signal intensity and thereby improve limits of detection. Of interest in XRF was the development of in-house spectrometers for underwater mercury screening and in vivo plant analysis. Developments in geological analysis include new homogeneous natural and synthetic materials that have been developed as reference materials (RMs) in the analysis of geological samples by microanalytical techniques, such as LA-ICP-MS, LIBS and SIMS. Additional information on already existing RMs has been obtained for in situ isotope ratio determinations. Attention has been paid to sample preparation and purification methods able to shorten the analysis time and to improve the accuracy. Much attention has been paid to the use of LA-ICP-MS/MS as a means for removing spectral interferences in the case of in situ localized isotopic analysis and dating of geological materials. The development of new chemometric models as well as software has continued to improve data quality. The use of artificial intelligence is growing and techniques such as machine learning have led to significant improvements in the quality of geochemical results.

---

Atomic Spectrometry Update (ASU) – Call for new writers

The Atomic Spectrometry Update (ASU) editorial board is looking to increase the size of the production team (see our recent Editorial (https://doi.org/10.1039/D4JA90044H)). We are looking for writers working in the field of atomic spectroscopy to contribute to one of the six reviews: advances in environmental analysis; advances in the analysis of clinical and biological materials, food and beverages; advances in XRF; advances in elemental speciation; advances in the analysis of metals, chemicals and materials; and advances in atomic spectroscopy and related techniques. You will work as part of a team to help produce the review on an annual cycle. Full support will be provided. Writers are allocated specific topics within the review and all abstracts for the review are provided. This is a great way to keep abreast of the current developments in the field, potentially improve your scientific writing skills and to publish annually. If you are interested, have relevant experience and a good command of written English, please contact the ASU General Editor, Prof Steve Hill for more information (steve.hill@plymouth.ac.uk).

1 Introduction

This is the 40th annual review of the application of atomic spectrometry to the chemical analysis of environmental samples. This Update refers to papers published approximately between August 2023 and June 2024 and continues the series of Atomic Spectrometry Updates (ASUs) in Environmental Analysis¹ that should be read in conjunction with other related ASUs in the series, namely: clinical and biological materials, foods and beverages;² advances in atomic spectrometry and related techniques;³ elemental speciation;⁴ X-ray spectrometry;⁵ and metals, chemicals and functional materials.⁶ This review is not intended to be a comprehensive overview but selective with the aim of providing a critical insight into developments in instrumentation, methodologies and data handling that represent significant advances in the use of atomic spectrometry in the environmental sciences.

All the ASU reviews adhere to a number of conventions. An italicised word or phrase close to the beginning of each paragraph highlights the subject area of that individual paragraph. A list of abbreviations used in this review appears at the end. It is a convention of ASUs that information given in the paper being reported on is presented in the past tense whereas the views of the ASU reviewers are presented in the present tense.

2 Air analysis

2.1 Review papers

The current knowledge of microplastics in road dust were discussed⁷ (154 references), and methodologies for their sampling, preparation and analysis summarised. In the determination of the number and mass concentration of MP, use of the μ-Raman and TOF-SIMS techniques was recommended⁸ (137 references) and the utility of atomic spectrometric techniques such as LIBS and XRF for identifying, classifying and tracing MP sources discussed⁹ (120 references).

Measuring engineered nanomaterials was a subject reviewed in two complementary IUPAC technical reports. In report one¹⁰ (115 references) suitable analytical techniques were presented and a useful listing of relevant (C)RMs tabulated. In report two¹¹ (227 references), which focused on real-world measurement applications, whilst the authors acknowledged the emergence of sp-ICP-MS as a valuable and practical measuring tool, they noted that further development of the underpinning metrological infrastructure was still required.

Other review papers of note published included: the analytical challenges and possibilities for the quantification of tyre-road wear particles¹² (113 references); the characterisation of total and speciated forms of Hg in natural gas and its condensates¹³ (245 references); chronological developments in GSR detection techniques¹⁴ (89 references); spectroscopic techniques for stable C isotope measurements in gases¹⁵ (70 references); application of emerging LA spectroscopic techniques for stable gaseous isotope measurements;¹⁶ (46 references) and the utility of MS techniques such as aerosol MS, CIMS and PTR-MS in the study of atmospheric haze chemistry¹⁷ (288 references).

2.2 Sampling techniques

To further exploit the sensitivity of the TXRF technique in measuring APM, researchers¹⁸ developed a new 5 L min⁻¹ flow-rate cascade impactor sampler thus ensuring that particles were deposited efficiently onto sampling (target) carriers for illumination under the resultant X-ray beam so enabling airborne metals to be determined at low ng m⁻³ concentrations following a short 30 min sampling interval. In a complementary endeavour, their development of a new spin-coating technique enabled a thin layer of adhesive grease to be applied to targets in a consistent and repeatable manner that facilitated the effective sampling of the required PM₁₀, PM_2.5 and PM₁ size fractions because particle bounce was now minimised. In the development of a new prototype impaction-based aerosol concentrator, the concentrating and focusing of APM onto target spots <1.5 mm in diameter was achieved¹⁹ using successive multiple smooth converging stages. The ability to concentrate particles >1000 nm in size, undertake potential spot analysis using laser-based techniques, and readily transport the device were considered advantageous. Useful chemical mass-to-size distribution data can be generated when wearable impactor samplers are deployed on workers for assessing occupational exposure to airborne metallic UFP but they can be cumbersome to prepare, so a new tutorial paper²⁰ on their practical use is most welcome.

Representative sampling of atmospheric reactive mercury species can be challenging due to their reactivity once sampled on media and because airborne concentrations are typically <ng m⁻³. In further evaluating the performance of their air sampler assembly, for Hg_P collection onto PTFE filters and GOM collection either onto cellulose ester membrane or onto nylon filters, Gustin and her team concluded²¹ that new methodologies for the effective sampling of Hg_P were still required.

2.3 Reference materials

Following a review²² (72 references) on the current status of microplastic RMs it was concluded that use of commercially produced RMs, which are typically spherical, homogenous and monodisperse in nature, was not ideal in supporting environmental measurements because real-world particles varied in size, morphology and chemical composition. The required physicochemical attributes to be considered when producing new RMs were thus outlined.

To support future nuclear safeguarding measurements, development of a new thermal sprayer device enabled new candidate isotopic reference materials particles to be produced²³ as exemplified by the preparation of new U/Th oxide materials with an average diameter of ∼1 μm and a geometric SD of <1.15 μm and which contained ²³²Th concentrations between 1 ppm and 10% when ratioed to ²³⁸U. Starting materials were assayed by TIMS, the U/Th oxide particles were generated with a vibrating orifice aerosol generator with resultant certification using MC-ICP-MS following particle dissolution. As atmospheric CO₂ concentrations increase, with a corresponding shift towards a lighter isotopic composition similar to that of fossil fuels, there is a requirement for C isotopic measurements that are accurate, precise, and reproducible in supporting source apportionment studies and subsequent abatement interventions. It is therefore illuminating to read an informative tutorial paper that chartered the development since the 1950’s of C isotopic RM²⁴ which included a description of a material derived from the calcified remains of Jurassic-era squids.

Procedural use of TOA is by nature operationally defined as codified in various standard methods but the application of such methods leads to variation in results when carbonaceous particles collected on air filter samples are analysed. The development of RM filters that contain a traceable organic and elemental carbon content is therefore desirable. Synthetic RM can be prepared by aerosolising solutions containing organics and carbon black suspensions, which once dried can simulate those organic and elemental carbon moieties in APM sampled onto filters. In order to generate an organic carbon simulant that better mimicked carbonaceous APM, use of a precursor organic solution mix that contained benzo[a]pyrene, benzoic acid, potassium hydrogen phthalate, sucrose and xylose was proposed.²⁵

2.4 Sample preparation

Methodologies for the extraction, isolation and purification of NP from environmental matrices for subsequent assays using sp-ICP-MS assays were summarised²⁶ (69 references). Pyrohydrolysis is a useful approach for liberating halogen species from environmental matrices. Reported applications conducted over the past 30 years were tabulated²⁷ (147 references) and critically assessed against alternative approaches such as alkaline extractions or fusions and combustion-based methodologies such as the traditional Schöniger oxygen flask technique or the more recently developed microwave-induced approach.

In developing a new procedure for the determination of metal isotopes in digested APM, which used a NOBIAS PA1® SPE cartridge to separate Cd, Cu, Ni, Pb and Zn from common matrix elements such as aluminium, iron, manganese and titanium prior to their separation using an AG MP-1M® AEC, the authors claimed²⁸ that their two-step approach was advantageous because high-sample throughput was possible and effective assays could be realised.

A method for the determination of Pt NP in road dust samples involved²⁹ sonication of samples in water, application of a centrifugal separation step with dilution and analysis using sp-ICP-MS. When applied to aliquots of BCR CRM 723 (road dust) it was determined that 12% of the certified Pt content was in a NP form. In road dust samples subsequently analysed, the Pt concentrations determined were 6.0–20.0 ng g⁻¹ of which 11–27% were in NP sizes of 15–75 nm.

2.5 Instrumental analysis

2.5.1 Atomic absorption and emission spectrometries. The simultaneous determination of Pd and Rh in spent automotive catalysts without recourse to a preliminary chemical separation was undertaken using³⁰ a HR CS atomic absorption spectrometer. Extractions were performed in aqua regia at 108 °C for 4 h and the LOD was 0.19 mg kg⁻¹ for Pd when measurements were performed at the 360.955 nm atomic line and 0.19 mg kg⁻¹ for Rh when measured at the 361.251 nm line. The relative measurement error against certified for Pd and Rh in samples of BAM ERM® 504 (platinum group elements in spend autocatalyst) tested was −3.1% and −12.1% with RSDs of 10% and 7%, respectively. By calibrating³¹ a real-time AA spectrometer using traceable GEM standards, prepared via the reduction of diluted NIST SRM 3133 (Mercury (Hg) standard solution) solutions, the calculated MU (k = 2) for subsequent air measurements undertaken ranged from ±8% at 40 ng m⁻³ to ±92% at concentrations <5 ng m⁻³ where contributions arising from instrumental noise and transient fluctuations in the trace Hg concentrations dominated.

Biomass feedstocks can contain large amounts of K and other ash-forming elements that lead to operational issues such as fouling, agglomeration and corrosion within industrial boilers. To gain a better understanding of the kinetics of elemental emissions when wood-based biofuels are combusted, an ETV-ICP-OES system was employed³² as a micro furnace simulator thus enabling reproducible temperature-resolved emissions by Ca, Cl, K, Na, Mg, P and S to be interrogated. It was deduced that K was released between 700 and 1400 °C either as elemental K or as KOH because inspection of concurrent ICP emission spectra did not show the presence of possible counter ions such as Cl or S.

Applications of laser and spark-based techniques for the analysis of air-related samples are summarised in Table 1.

Table 1 Selected applications of LIBS and SES for air related measurements

Analyte	Sample matrix	Study rationale	Technique	Findings	Reference
APM	Air	Evaluation of a prototype field deployable system for the online analysis and classification of individual aerosol particles	LIBS	Portability demonstrated in field trials. Optimum sampling rate was 10 particles per min and optimum concentration for analysis was 1 particle per cm^3. Mass LODs for K, Mg and Na were 70, 40 and 2 pg, respectively. Minimum detectable particle size was 300–800 nm	33
APM	Air	Evaluation of a prototype field deployable system for the online analysis and classification of aerosol particles	LIBS	Measurement of up to 20 elements at 10 min measurement intervals possible and major elements such as Al, Ca, Cl, Mg, P, S and Si detected in an Asian dust event	34
NP	Air	Evaluation of a measurement approach for the analysis of engineered NP	LIBS	Both online (direct aerosol sampling) and offline (analysis of NP collected on filters) measurement approaches tested using mono and bimetallic NP generated via electrical discharge plasma generators. Online detection threshold for particles tested was for a diameter of >60 nm and a number concentration of >10^6 cm^3	35
NP	Air	Evaluation of a measurement approach for the analysis of single NP	LIBS	Ultrafast ps pulses used to interrogate single optically trapped NP both qualitatively and quantitatively. LOD for Cu of 27 ag for single particles of 18 nm diameter	36
Various	GSR	Evaluation of a mobile on-site particle screening capability	LIBS	In-field methodology for forensic analysis demonstrated. LOD of 0.2 ng (Ba), 2.0 ng (Pb) and 2.0 ng (Sb) achieved. Particle classification accuracy of >98.8% demonstrated for datasets examined. Potential for reduced classification times using LIBS (min) vs. conventional laboratory-based SEM-EDS (h)	37
Various	Surfaces	Evaluation of a portable system to measure metallic particles sedimented from air on workplace surfaces	SES	Tape-lift sampling of dust from contaminated workplace surfaces for analysis with a spectrometer calibrated for Co, Cr, Fe and Mn using calibrants prepared by depositing known sample masses on Cu tape. LOD of 3–19 ng cm^−2	38

---

2.5.2 Mass spectrometry.
2.5.2.1 Inductively coupled plasma mass spectrometry. The utility of various ICP-MS platforms such as hyphenated-ICP-MS systems, ICP-MS/MS, LA-ICP-MS and sp-ICP-MS in supporting the toxicological assessments of metallic NP exposures was reviewed³⁹ (121 references). Coupling a rotating disk diluter (RDD) to sp-ICP-MS enabled measurements of elemental composition, particle size and particle number concentration in aerosol samples.⁴⁰ In a proof-of-concept study using 40–100 nm sized Au NP, a number LOD of <30 particles per L was achieved, and matrix effects were deemed minimal because the correct particle number and size data was realised when PM₁₀ and road dust samples spiked with NP were analysed. The presence of ionic metal salts, confounders when testing NP samples by sp-ICP-MS, can result in the incorrect determination of particle size profiles but this interference can sometimes be alleviated by diluting samples. This was effective⁴¹ in the analysis of simulated aerosol samples, wherein online sample dilution, achieved by coupling a RDD to the sp-ICP-MS instrument, decreased the ionic Au³⁺ background signal in the determination of Au NPs. Use of alternative remedies, namely a fractionation approach, achieved by either coupling a differential mobility analyser (DMA) or a centrifugal particle mass analyser (CPMA) with a sp-ICP-MS instrument, were also effective by enabling better discrimination between NP and background ionic signals. To rank the respective performances of these remedies, the authors calculated a quantitative separation factor for each approach, defined as the signal ratio between the particle number (Au NPs) and the average of ionic background intensity (Au³⁺) and demonstrated that this ratio decreased in the order DMA > RDD > CPMA.

Hyphenated GC-ICP-MS can be used to measure volatile elemental impurities in gaseous samples as demonstrated⁴² in an application paper that summarised the methods for testing speciality gases such as diborane, phosphine and silanes used in the microelectronics industries. Here the ability to perform compound-independent calibrations, because often specific calibration standards are either unavailable or unstable, coupled with use of ICP-MS/MS to minimise isobaric interferences was advantageous. A custom-made TD unit coupled to ICP-MS/MS enabled⁴³ the successful determination of Hg⁰ isotopes in air samples following collection and concentration on Au traps and subsequent desorption. The LOD was 0.01–0.03 pg, and calibration linearity up to 300 pg was achieved with analytical precision of ≤3.5% in resultant isotopic measurements.

Further ICP-MS applications for air related measurements are summarised in Table 2.

Table 2 Selected applications of ICP-MS for air related measurements

Analyte	Sample matrix	Sample preparation/introduction	Technique	Rationale and findings	Reference
C	Microplastics in facemasks	Suspension nebulisation	sp-ICP-MS	Size analysis of PS particles in range 1–6 μm performed with size LOD of ∼400 nm. Results comparable with those obtained using DLS and SEM	44
C	Microplastics	LA	sp-ICP-MS	Method for sampling and introducing MPs captured on filters into plasma. Resultant ^13C^+ signal was linear with absolute C mass over a 2–20 μm particle size range	45
C	Microplastics	Suspension nebulisation	sp-ICP-MS	Downward facing ICP system (gravity fed) for effective transportation of particles up to 90 μm. System tested using PS beads where particle diameter measurements of 2.93 ± 0.24 μm compared favourably with 2.97 ± 0.04 μm measurements derived using TEM	46
I	Air	Acid extraction/solution nebulisation	ICP-MS	Method for sampling and analysis of gaseous ^129I with sampling from air onto charcoal sorbent tube; acid extraction and clean-up followed by ICP-MS analysis. LOD of 2 mBq per sample achieved	47
Pd, Pt and Rh	Automobile catalytic converters	MAE, solution nebulisation	ICP-TOF-MS	Method for high accuracy analysis of converter materials. Exact matching ID used for Pd and Pt assays and IS used for Rh assays. MU (K = 2) <1% achieved, representing a 2 to 3-fold improvement in Rh and Pd assays and 6-fold improvement in Pt assays performed previously using sequential ICP-MS	48
Pt	NP	Suspension nebulisation	sp-ICP-TOF-MS	Comparison exercise between 9 laboratories for mass, size and isotopic analysis of 40 ± 7 nm and 59 ± 8 nm Pt NP suspensions. Precision for mass equivalent spherical size measurements and mass measurements was <16% RSD (within laboratories) and <4% RSD (between laboratories). Particle number concentration testing precision was 53% RSD (between laboratories). Accurate isotope ratios determined for ^194Pt/^195Pt with precisions <1% but >20% for low-abundance isotope ratio measurements	49
Pu and U	Surface particles	On-line extraction/solution nebulisation	ICP-MS/MS	Evaluation of an on-line method for extracting and analysing Pu/U in particles collected on cotton swabs. Tested on extraction of simulant swab samples that contained 2 pg Pu and 20–200 ng U reference particles spikes. CO2/He used as a collision gas mix to minimise U isobaric interferences on Pu. The percent relative difference between measured isotope ratios and certified isotope ratios were <±5% except for ^240Pu/^239Pu ratio measurements which were <+6.7%	50
Sr	PM10	Acid extraction/solution nebulisation	ICP-MS	Development of a single QICP-MS method to determine stable Sr isotopic signatures for potential PM10 source apportionment studies. By optimising a Sr-Spec resin-based clean-up method and the ICP-MS operating conditions, ^87Sr/^86Sr ratios were measured with an internal precision of ca. 0.2% at concentrations of 0.5–2 μg L^−1	51
Various	GSR	Suspension nebulisation	sp-ICP-MS	Use of sp-ICP-MS deemed superior to established SEM-EDS approach because more elements besides the usual Ba, Pb and Sb could be measured and so provide a better GSR classification profile	52
Various	PM10	On-line extraction, solution nebulisation	ICP-MS	Online SBET methodology developed. Simulant test samples prepared by depositing known masses of NIST SRM 2711a (Montana II soil) onto air filter samples which were then extracted and analysed. Measured bioaccessible Pb value relative to certified value was 105%	53
Various	Tyre rubber	MAE, solution nebulisation	ICP-MS	Fingerprint assay using metal markers developed to aide future source apportionment studies. Data compiled for 25 metals from 60 different tyres sampled. Mean mass fraction (m m^−1) of Zn was 1.117% consistent with previously reported values of ca. 1%	54

---

2.5.2.2 Mass spectrometry techniques other than inductively coupled plasma mass spectrometry. Measurement of elemental isotopic signatures in APM can provide valuable insights into their respective emission sources but high quality calibrants are essential for accurate determinations. Korean researchers calibrated⁵⁵ EA-IRMS by analysing in-house isotopic standards prepared by blending mixtures of various USGS and IAEA RMs, which enabled reference values with uncertainties for C, N and S isotope ratios in KRISS CRM 109-02-004 (urban particulate matter) to be determined. Calibration of aerosol MS typically involves the analysis of a given mass of particles with a known size and number concentration, but development of a new calibration approach, the heated Mo/Pt catalyst-based conversion technique, which uses nitric oxide (NO) and carbon dioxide (CO₂) gas-phase standards, enabled reactive N and TOC measurements to be made.⁵⁶ This new calibration approach was evaluated using dried particles composed of pure ammonium nitrate (AN), other ammonium salts, and nitrogen-containing organic species and it was determined that the nitrate ionisation efficiencies determined by either calibration approach agreed within experimental uncertainties of ±15%. Furthermore, it was demonstrated by using this new independent calibration protocol that the relative ionisation efficiency for the ammonium ion was essentially the same for different ammonium containing compounds (±9%), regardless of formula or the corresponding anion, thereby validating a major assumption inherent in aerosol MS calibrations.

2.5.3 X-ray spectrometry. Two of the many relevant topics discussed⁵⁷ (82 references) in a tutorial review on the applicability XRF for the analysis of airborne particulate matter were the selection of suitable sampling substrates and the implementation of appropriate calibration approaches. In the μSRXRF analysis⁵⁸ of the elemental composition of PM_2.5 collected on filters, only 5 mm diameter portions were required for testing. The LODs ranged from 0.04 to 36.6 ng m⁻³ with analysis times of <360 s per sample. Calibrants were prepared by spiking filters with multi-element solutions over a 0–16 ng mm⁻³ linear range with ICP-MS assays performed to verify the spiked values. Performance benchmarked against two other measurement approaches, a conventional approach where air filters were analysed by ICP-MS and the in situ analysis using an air sampler device equipped with an embedded EDXRF analyser, yielded elemental correlations with slope values of 0.4–1.0 with r² values of 0.62–0.89 and slope values of 0.29–0.89 with r² values of 0.39–0.80, respectively. Such diversity demonstrates the challenge in objectively comparing the performance of different measurement platforms that operate on different principles. There is a growing concern regarding the potential toxicity of APM in subway air so new studies here are welcome. By employing XAFS it was determined⁵⁹ those airborne particles, arising from high-temperature wheel and track wear processes, contained Fe₃O₄, γ-Fe₂O₃ and Cu^I species that were found to cause cellular damage when laboratory cell line studies were subsequently conducted.

2.5.4 Carbonaceous particle measurements. Tutorial reviews that summarised analytical approaches such as combustion, optical, microscopic and MS-based techniques for identifying and determining black carbon in environmental matrices (225 references),⁶⁰ and methodologies for characterising the mass, size and morphology of soot particles (600 references)⁶¹ were published. The comparative testing⁶² of different black carbon analyser systems, namely AE33® and MA20® aethalometers, a multi-angle absorption photometer, a soot particle aerosol MS and an elemental carbon TOA analyser, undertaken by challenging them to laboratory generated soot particles, yielded median concentrations that were within 25% of each other. The generation of carbonaceous test aerosols within a laboratory setting for instrumental testing purposes is typically undertaken using a soot generator that runs on gaseous fuels such as propane, but resultant emissions may not fully mimic those emitted from real-world engines, so operation⁶³ of a miniCAST® soot generator using diesel fuel enabled more realistic soot particles to be produced in the size range 18–39 nm.

The ability to distinguish between graphene, graphene oxide and reduced graphene oxide species in workplace air was possible when TOA assays were conducted⁶⁴ on filter samples collected from an advanced manufacturing facility. It was suggested that such measurements would complement other measurements undertaken by Raman and SEM in providing better worker exposure assessments. Wearable aethalometers are used to undertake black carbon measurements within ambient air settings, but their reported use⁶⁵ in harsher mine air environments was new. The ability to generate exposure data in real-time provided new insights into task-specific worker exposure dependencies that will aide future control intervention studies. The aethalometer-derived eBC measurements correlated well with those regulatory EC measurements, undertaken in parallel, with linear regression r² values of 0.911–0.999, but there was a between-method bias because regression slopes were 0.993–2.103. To reduce such bias in future studies, use of more appropriate mass absorption cross section values that are site-specific to correct eBC values was suggested. The authors also recommended installing an aerosol dilutor and an aerosol drier on the sampling inlet of the aethalometer if high fume concentrations and high RH are encountered in such underground work settings. End-of-working-shift air filter measurements conducted on-site with portable instrumentation offer the potential for exposure data to be generated more quickly than at present which involves sending samples to a laboratory for TOA analysis. The prompt return of such data could then allow swifter interventions to control emissions. In a proof-of-concept study, the analysis of air filter samples by portable NDIR yielded⁶⁶ LOD of 20, 37, and 46 μg m⁻³ for elemental, organic and total C. Incorporating the spectrometer within an air sampling device equipped with a spoolable filter tape for a near continuous in situ measurement capability was also proposed.

It is interesting to note developments in on-line carbonaceous aerosol monitors. The comparative testing⁶⁷ of a new prototype total C analyser, so-called FATCAT (fast thermal carbon totalizator), against a similar but commercially available TCA08® analyser demonstrated that non-volatile total C measurements were in agreement when challenged with either mature, fully graphitised soot (with an organic-to-total C ratio of <0.1) or young, partially formed soot (with an organic-to-total C ratio of 0.6) that was denuded at 350 °C. The FATCAT instrument possessed a catalyst to convert CO or VOC, arising from sample heating, to CO₂, which if not fully oxidised would otherwise may go undetected. It was therefore concluded that this instrument was better suited for measuring a wider range of soot and organic APM than the TCA08 instrument, which did not possess such a catalyst and so could only measure those sampled carbonaceous APM species that convert fully to CO₂ upon heating. A prototype on-line monitor for the concurrent measurement of TOC and water-soluble organic carbon (WSOC) in both PM_2.5 and PM_2.5–10 was developed.⁶⁸ The PM_2.5 air sampling line consisted of a versatile aerosol concentration enrichment system connected to an aerosol-into-liquid-sampler, whereas the system for collecting the PM_2.5–10 fraction consisted of two tandem virtual impactors connected to a BioSampler™. An embedded Sievers M9 TOC analyser was then used to determine the TOC and WSOC contents of the particles.

3 Water analysis

3.1 Reviews

Sample preservation and preparation are crucial steps for the accurate determination of concentrations in environmental samples. Given the significance of elements with multiple labile forms in the environment, a comprehensive overview⁶⁹ (88 references) of this topic focussed on mercury. The authors concluded that preserving and transporting mercury samples, particularly from remote or resource-limited areas, poses significant challenges due to the need for different preservation methods and hazardous materials. While current methods emphasize rapid transportation to laboratories, SPE methods using specialized sorbents offer a promising alternative. These methods effectively preserved Hg species without altering their chemical forms and provide a safer, more cost-effective option for field-to-laboratory transport. They eliminated the need for expensive materials (e.g., PTFE) or heavy, fragile containers (e.g., glass) and did not require additional chemicals to stabilize the Hg species. However, further research was needed on the long-term storage of adsorbed Hg species, especially MeHg, to establish SPE as a reliable preservation method fully.

Another legacy pollutant is lead contamination with severe impacts on both the environment and human health. A comprehensive review⁷⁰ (61 references) highlighted the importance of accurately determining Pb levels in different water sample types. The authors discussed the merits of various detection techniques and pointed to emerging technologies, such as precious metal nanotechnology (e.g., nanoclusters), paper-based microfluidics, and new fluorescent molecular probes, that are making Pb detection more economical, portable, and rapid.

Stable isotopes analysis is a powerful tool to understand environmental processes. A comprehensive review⁷¹ (111 references) examined various spectroscopic techniques and their potential applications in environmental sciences. This article focused on six atomic spectroscopy methods (i.e., AES, AAS, LEAFS, saturated absorption spectrometry, four-wave mixing spectrometry, and Doppler-free two-photon spectrometry), discussing both their fundamental principles and current limitations. While atomic spectrometry is commonly employed for determining the elemental composition, it generally falls short of MS in precision and accuracy for isotope analysis. However, atomic spectrometry offers notable advantages, including fewer interferences, easier correction, and the capability for rapid, in situ, and remote real-time isotope analysis, especially when combined with laser ablation.

3.2 Certified reference materials

A comprehensive investigation of RM was carried out⁷² to assess their suitability for the determination of²³⁷Np in environmental samples. This study summarized values obtained from the available literature, reporting certified and informative values for 25 RM (over 200 activity concentration data). The literature values (44 references) were further analysed by calculating statistical values (arithmetic mean). In addition, the activity concentration of the RM SRM JSAC-0471 (soil), issued by the Japan Society for Analytical Chemistry, was determined and reported for the first time.

3.3 Sample preconcentration

The most significant advances in analyte preconcentration for water analysis are summarised in Tables 3 (solid-phase extraction) and 4 (liquid-phase extraction).

Table 3 Preconcentration methods using solid-phase extraction for the analysis of water

Analytes	Matrix	Technique	Substrate	Coating or modifier	LOD in μg L^−1 (unless stated otherwise)	Method validation	Reference
As^III	Ground and tap water, food samples	ICP-MS	Metal organic framework	None	0.554	Spike recovery (water, food samples)	73
As, Cd, Pb	Groundwater	GFAAS	Magnesium oxide NPs	Chitosan	0.008 (As), 0.006 (Cd), 0.012 (Pb)	Spike recovery (water)	74
As, Cu, Pb	Sea water	ICP-OES	Manganese oxide octahedral molecular sieve	None	0.3 (As), 0.1 (Cu), 2.1 (Pb)	Spike recovery (sea water)	75
As^III, As^V, Cr^III, Cr^VI, Se^IV and Se^VI	Water and urine	ICP-MS	Polyamide porous monolith	Titanium oxide NPs	0.004 (As^III), 0.0007 (As^V), 0.0109 (Cr^III), 0.0119 (Cr^VI), 0.0323 (Se^IV) and 0.0132 (Se^VI)	NRCC CASS-4 (nearshore seawater), and SLRS-5 (river water), NIST 1643f (fresh water), Seronorm L-2 (trace elements urine) and spike recovery (seawater, river water, agriculture waste, and human urine samples)	76
Au and AuNPs	River, lake, sea water	FAAS and GFAAS	Zirconium(IV) metal organic framework	Mercaptosuccinic acid, formic acid	650 femto mol L^−1	Spike recovery (water)	77
Ba, Be, Cd, Cr, Cu, Fe, Hg, Mn, Ni, Pb, Zn	Waste water	ICP-OES	ZIF-67	Activated carbon	2.6 (Ba), 0.39 (Be), 1.5 (Cd), 1.3 (Cr), 1.2 (Cu), 9.8 (Fe), 5.1 (Hg), 0.82 (Mn), 9.9 (Ni), 21 (Pb), 0.81 (Zn)	ERM-CA713 (waste water)	78
						Spike recovery (water)
Bi	Water, food, cosmetic samples	FAAS	MgAl2O4@MoSe2 nanocomposite	None	0.012 (Bi)	NCS ZC 73028 (rice), NCS ZC 73036 (green tea)	79
Cd	Lake water	FAAS	Manganese ferrite magnetic NPs	None	1.3	Spike recovery (lake water)	80
Cd	Sea water	SAGD OES	Graphene oxide	None	0.003–0.2	Reference measurements via ICP-MS	81
Cd, Co, Ni	Spring, mineral, factory process waste water	FAAS	Magnetic pinus pinea cone powder	None	2.3 (Cd), 19.3 (Co), 12.5 (Ni)	UME CRM 1204 (elements in wastewater), spike recovery (water)	82
Cd, Cu	Tap water, mineral water, drinking water, food samples	FAAS	Magnetic polystyrene-b-polydimethyl siloxane block copolymer	None	1.7 (Cd) 0.8 (Cu)	NIST SRM 1570a (spinach leaves), ERM BCR-032 (phosphate rock) spike recovery (water, juice)	83
Cd, Cu, Pb	Drinking water	ICP-OES	Cellulose nanoparticles	Ethylenediaminetetraacetic acid-linked polyethyleneimine	0.4 (Cd, Cu, Pb)	Spike recovery (water)	84
Cd^II and Pb^II	Swimming pool water	GFAAS	Open-cell polyether-type polyurethane foam	None	0.5 (Pb^II) and 0.02 (Cd^II)	Spike recovery (water)	85
Ce, Dy, Eu, Gd, Ho, La, Lu, Nd, Pr, Sm, Tb, Tm, Tb, Y, and Yb	Water	WDXRF	NH2-MIL-53	N,N-Bisphosphono(methyl) glycine	0.4–4.7	Spike recovery (water)	86
Cr^III	Water	GFAAS	Several ion-imprinted polymers	None	0.35–0.47	NIST SRM 1643e (surface water)	87
Cr^III, Cr^VI, total Cr	Waste, tap, mineral water	FAAS	Multiwalled carbon nanotubes@CuAl2O@SiO2	None	6.2 (Cr^VI)	INCT-OBTL-5 (Oriental Basma tobacco leaves) and ECCC TMDA-64.3 (fortified water)	88
Cr^VI	Water and sea water	TXRF and EDXRF	Graphene oxide	Tetraethylenepentamine	0.053 (Cr^VI, EDXRF) and 0.0035 (Cr^VI, TXRF)	Sigma Aldrich RMs QC1088 (Cr^VI in water) and QC3016 (sea water)	89
Cu	Water, food samples, soil	FAAS	Fe3O4@SiO2	Creatine	1.50 (Cu)	Spike recovery	90
Cu	Water, food samples	FAAS	BaTiO3	None	1.6	ECCC TMDA-64.3 (water)	91
Cu^II	Mineral and sea water	ICP-MS	Several ion-imprinted polymers	None	0.0152	Spike recovery (water)	92
Cu^II, Hg^II Ni^II, Pb^II, Zn^II	Water	ICP-OES	Cobalt ferrite nanoparticle	Deep eutectic solvent (choline chloride and p-aminophenol)	0.544 (Cu), 1.33 (Hg), 1.12 (Ni), 0.622 (Pb), 0.962 (Zn)	SPS-WW2 batch 108 (water)	93
Ga^III and In^III	Water, soil, electronic scrap	GFAAS	Fe3O4 particles	None	0.02 (Ga^III) and 0.01 (In^III)	NIST SRM 2711 (soil), NCS DC 73319a (soil), ECCC TM-25.4 (water) and TMDA-62.2 (water)	94
P	Water and artificial seawater	EDXRF	Graphene oxide	Lanthanum oxide	0.4	Spike recovery (water)	95
Pb	Water	FAAS	Polysulfone fibres	Acrylic acid	50	Spike recovery (water)	96
Pb^II	Tap and mineral water, lettuce	GFAAS	Cu-benzene-1,3,5-tricarboxylic acid@Fe3O4	None	0.026	Spike recovery (water)	97
Pb^II	Tap and waste water, food samples	FAAS	Zinc oxide nanoflower	Ag	8.52	GBW07424 (soil) and GBW07425 (soil) and spike recovery	98
Pd	Sea water and river water	FAAS	Dicobalt orthosilicate NPs	Mordant red 3	0.14	Spike recovery (water)	99
Sb^III and Sb^V	Tap, river, lake, underground water and acid mine drainage	ICP-MS	Reduced graphene oxide	Fe3O4	0.006 (Sb^III)	GBW(E)080545 (standard solution of Sb)	100
						Spike recovery (water)
U	Tap, river, sea water	WDXRF	Fe3O4@COF	1,3,5-Triformyl-phloroglycerol and terephthalohydrazide	0.04	NRCC CASS-6 (near shore sea water), GBW07311 (stream sediment)	101
V^V and V^IV	Sea water	ICP-MS	Chelex-100 resin	None	0.87 nano mol kg^−1 (V^V) and 0.47 nano mol kg^−1 (V^IV)	NRCC NASS-7 (sea water)	102

---

Table 4 Preconcentration methods using liquid-phase extraction for the analysis of water

Analytes	Matrix	Technique	Method	Reagents	LOD in μg L^−1 (unless stated otherwise)	Method validation	Reference
AgNPs and TiNPs	Tap water	ICP-MS	Surfactant-assisted DLLME	Triton X-114 and 1,2-dichloroethane	55.5 nm (TiNPs) and 14.1 nm (AgNPs)	Spike recovery (reference particles in water)	103
Ag, Cu, Pd, Pt	Tap, river, sea water	ICP-OES	DLLME	1-Butyl-2-diphenylphosphino-3-methylimidazolium hexafluorophosphate and methanol	0.2 (Ag), 0.4 (Cu), 2 (Pd), 1 (Pt)	ISC science RM CRM-DW1 (drinking water) spike recovery (water)	104
As, Cd, Pb, Hg	Water and food samples	GFAAS	DLLME	l-Menthol and salicylic acid	0.0088 (As), 0.0084 (Cd), 0.0076 (Pb), 0.0043 (Hg)	Spike recovery (water)	105
δ ^13C in chlorinated phenols	Ultrapure and river water	GC-IRMS	DLLME	Acetone, tetrachloroethylene and acetic anhydride	Limit of precise isotope analysis: 100–200	Spike recovery (different reference standards)	106
Cd	Sea, river, tap, mineral water and food samples	FAAS	DLLME	Ethanol and choline chloride/4-bromophenol	0.9 (Cd)	Spike recovery (water)	107
Cd, Co, Cu, Mn, Ni and Pb	Water	ICP-OES	LLME	Benzethonium chloride and dihexyl sulfosuccinate	0.04 (Cd), 0.5 (Co), 0.2 (Cu), 0.1 (Mn), 0.1 (Ni) and 1.0 (Pb)	SPS-SW2 (surface water)	108
Co	Drinking water	FAAS	Spray-assisted fine	Ethyl-2-(((7-hydroxy-2-oxo-4-phenyl-2H-chromen-8-yl)methylene)amino)-3-(4-hydroxyphenyl) propanoate and chloroform	2.2	Spike recovery (water)	109
			Droplet formation LPME
Cr^VI	River water	GFAAS	Single-phase LLE	Water, ethanol and amyl alcohol	0.05	NIST SRM 1643e (surface water)	110
Cr^VI	Surface water and wastewater	FAAS	LLME	1-(2-(Quinolin-8-yloxy)ethyl)pyrrolidinium chloride, NH4PF6, water	0.0055	Spike recovery (water)	111
Cr^VI	Sea, tap, well, wastewater	TXRF	LLME	Cetyltrimethylammonium bromide and chloroform	0.9	Spike recovery (water)	112
Hg	Sea water	Direct mercury analyser	Vortex-assisted LLME	Ammonium pyrrolidine dithiocarbamate and hexane	0.0082	NRCC NASS-7 (sea water), SLEW-3 and ERM BCR-505 (both estuarine water)	113
V	Water and food samples	FAAS	Vortex-assisted LLME	[ChCl/p-cresol] [FeCl4] and bis(acetylpivalylmethane)ethylenediimine	0.3	NIST SRM 1643f (trace elements in water) and NIST SRM 1570a (spinach leaves)	114

---

3.4 Speciation

The development of many protocols for column-based chemistry, as shown in Tables 3 and 4, underline the importance of species-specific metal determinations in evaluating the condition of aquatic systems. A common approach for such analysis is HPLC coupled with a suitable detector. Utilizing HPLC-ICP-MS, a method¹¹⁵ was developed to simultaneously speciate five Pb, Hg and V species, of environmental concern: Pb^II, TML, Hg^II, MeHg, and V^V. To reach lower LODs the authors incorporated an on-line SPE step with a synthesized novel nanomaterial sorbent consisting of silica-coated magnetic particles and graphene oxide functionalized with methylthiosalicilate (M@GO-TS). The magnetic NPs were easily retained within a microcolumn or knotted reactor using an external magnetic field during adsorption and elution. Separation on a C18 HPLC column was achieved by a two-phase gradient method (thiourea/H₃PO₄ and TBAOH/H₃PO₄). Using ICP-MS, allowed the parallel detection of all three metals without requiring complete resolution. The method achieved low LODs: 5 ng L⁻¹ for TML, 20 ng L⁻¹ for MeHg, and 2 ng L⁻¹ for V^V, with RSDs of around 5%. The method was validated using the NRCC CRM TMDA 64.3 (fortified lake water) and was successfully applied to real-world samples, including seawater and human urine, demonstrating its potential for routine environmental monitoring and biomonitoring applications.

A similar SPE-HPLC-ICP-MS method for tin and lead species was developed¹¹⁶ for TMT, TET, TBT, TPhT, TML, TEL, and Pb^II. As above, a nanomaterial composed of GO and silicon dioxide (GO@SiO₂) was used for on-line SPE enrichment with subsequent species separation using a C18 HPLC column and sodium dodecylbenzenesulfonate as the eluent. The optimized method achieved LODs of 0.004 ng L⁻¹ for TMT, 0.002 ng L⁻¹ for TET, 0.007 ng L⁻¹ for TBT, 0.008 ng L⁻¹ for TPhT, 0.005 ng L⁻¹ for TML, 0.006 ng L⁻¹ for TEL, and 0.004 ng L⁻¹ for Pb^II. The method was validated by diluting and spiking a seawater matrix, satisfactory recoveries between 90% and 104% were obtained, with RSDs ranging from 1% to 5%. The total runtime of approximately 17 minutes per sample supports high-throughput analysis, and the low LODs in the pg L⁻¹ range make it suitable for analysing large environmental sample sets. The authors applied their method to investigate the occurrence of Sn and Pb species in the coastal seawater of Hangzhou Bay and reported the presence of these species in some samples.

Antimony, a toxic metalloid whose occurrence is significantly influenced by human activities, such as mining, is classified as a priority pollutant with regulated limits in drinking water. To effectively monitor the presence of Sb species (Sb^IIIand Sb^V) in drinking water, a fast and reliable method was developed¹¹⁷ using frontal chromatography coupled with ICP-MS (FC-ICP-MS). The method employed a strong cation-exchange resin and diluted HNO₃ (0.1, 0.5 or 1 M) as the eluent to achieve a separation where Sb^III interacts with the resin while Sb^V does not. This approach was validated using spiked drinking water samples, as no suitable RM were available. The method demonstrated good recoveries; however, the authors noted that when there was a high ratio between the two species (ca. 100 : 1), the concentration of the less abundant species might be overestimated. Despite this, the authors concluded that this issue is unlikely to be significant in natural surface waters. The optimised method achieved low LODs of 0.9 ng L⁻¹ for Sb^III and 0.4 ng L⁻¹ for Sb^V, and high-throughput (3 min per sample).

To reduce the risks from handling Hg species in the environment and in laboratory settings a Me₂Hg generator that produced¹¹⁸ high-purity Me₂Hg stock solutions on demand by reacting methylcobalamin with Hg²⁺ under optimized conditions has been developed. The system was designed so that freshly formed Me₂Hg was volatilized and diffused into an outer buffer solution. The authors then used this high-purity Me₂Hg as standard to develop a purge-and-trap GC-CV-AFS method for Me₂Hg determination in water samples. In this method, nitrogen gas was used as a purge, with a gold trap that captured Hg⁰ followed by a Tenax® trap that captured the other Hg species. Thermal desorption with Ar as a carrier gas was performed at 500 °C to liberate Hg⁰ prior to AFS detection. The other Hg species were desorbed at 200 °C and then separated and detected by GC-AFS. The method was validated through spike recovery experiments (spike levels: 0.05, 0.2, 1 ng L⁻¹), achieving recoveries of 93% to 102% for Me₂Hg in water samples, with RSDs of 4% to 6%.

3.5 Nano- and micromaterials

In recent decades, awareness and concern about small-scale particles in aquatic environments has significantly increased. While metal-based particles have been studied for a long time, attention has now shifted to investigating nano- and microplastics and their impact on aquatic ecosystems. Detecting carbon-based NPs is challenging, so Pirade et al. developed¹¹⁹ an indirect spICP-MS method using gelatin-coated gold NPs to measure the number concentration of plastic nanoparticles. The gold NPs conjugate with the target plastic particles, enabling the measurement of the plastic particle number concentration by detecting the presence of Au. Using this method they successfully detected 390 nm diameter polystyrene particles in both tap and canal water. Hendriks and Mitrano¹²⁰ introduced a direct method for detecting polystyrene microparticles (PS-MP) by measuring the ¹²C signal using spICP-MS, even in the presence of up to 20 mg L⁻¹ DOC in freshwater. Their approach utilised the multi-elemental capability of ICP-TOF-MS to differentiate PS-MP from other carbon sources, such as algae (by using their multi-elemental fingerprint), achieving a size detection limit of 1.56 μm. A microdroplet generator (MDG) for calibration purposes was used¹²¹ together with spICP-TOF-MS to enable the direct detection of 3.4 μm PS-MP in seawater via the ¹²C signal. Vonderach et al. aligned¹²² their MDG with a custom-built ICP-TOF-MS with a downward-pointing plasma using the force of gravity to improve particle transport efficiency when detecting 3 μm polystyrene particles using the ¹²C signal. An optimised¹²³ ICP-MS method for detecting PS-MP in river water utilized the ¹³C signal and achieved a size LOD of approximately 1 μm. To avoid false positive events from other OM, e.g., algae, they developed a premeasurement acid digestion procedure. A common challenge with all these methods for the microplastic detection is the inability to differentiate between plastic types. To address this, a comprehensive approach¹²⁴ was developed that combined optofluidic force induction with Raman spectroscopy and ICP-TOF-MS, enabling detailed particle characterization.

In addition to determining the size of plastic particles, the investigation of the ‘Trojan horse’ effect is another important field of research. For example, Patidar et al.¹²⁵ studied microplastics as vectors for metals in rivers, using ED-XRF to detect several elements. They found that microplastics were contaminated with various metals, posing potential risks to aquatic species that ingest the microplastics. A study on the potential of nanoplastics to carry¹²⁶ toxic tin species was conducted, demonstrating that these particles served as vectors for the remobilization of TBT from sediments. This was particularly concerning because these NPs exhibited higher mobility than sediment particles. A study by Baalousha’s research group¹²⁷ explored the use of fingerprint elements to track the fate of released nanoplastics in the environment. Additionally, two studies^128,129 investigated microplastics as carriers for NPs, including iron, copper, and zinc oxide NPs. Both confirmed that the adsorption of NPs onto microplastics took place and suggested that this may lead to harmful effects on aquatic ecosystems.

The combination of fractionation and speciation approaches commonly relies on multiple techniques, e.g., AF₄ and HPLC, but to streamline the process and eliminate the need for costly and complex hyphenated methods prior to speciation, a procedure based¹³⁰ on membrane filtration was developed. This approach utilized a PVDF membrane for the offline fractionation of ionic Se species and Se NPs. The sample volume was first reduced from 1 L to 1 mL, achieving an EF of 1000. The NP fraction was then digested and directly analysed by ICP-MS, while the ionic fraction was analysed via HPLC-ICP-MS. Separation of Se species was performed using an anion-exchange column with NH₄HCO₃ as the eluent. The method was validated through spike recovery experiments to assess each step of the process. The optimized method was successfully applied to various spiked sample types, including tap water, groundwater, spring water, artificial lake water, and river water, demonstrating its effectiveness. The LOD was 1.8 ng L⁻¹ for Se NPs.

3.6 Instrumental analysis

3.6.1 Atomic absorption spectrometry. High-resolution CS-AAS offers a sensitive method for determining F concentrations by measuring in situ generated fluorine-containing molecular surrogates, e.g., BaF, GaF rather than fluorine directly. However, this technique currently only enables the determination of fluorine species as a sum parameter. In the case of CS-AAS, the most commonly used parameter is EOF, which represents the extractable organic fluorine fraction. To address¹³¹ this limitation and provide a more comprehensive assessment of organic fluorine species, a study was conducted using both HPLC-ESI-MS/MS and HR-CS-GFAAS. Samples were taken from Berlin rivers impacted by sewage treatment effluents. The study targeted 24 specific PFASs using HPLC-ESI-MS/MS, while the EOF sum parameter was measured via HR-CS-GFAAS. Before AAS measurement, the inorganic fluorine was removed and the organic fluorine was pre-concentrated using a SPE step, enabling PFAS detection in the ng L⁻¹ range. The method’s accuracy was validated using the EC CRM ION-96.4 (water from the Grand River, Ontario) and an in-house QC standard solution, as no EOF RMs were available. The study revealed that targeted analysis accounted for a maximum of 14% of the EOF total, underlining the need for complementary approaches. Further, the authors showed that the organic fluorine concentration was strongly influenced by inputs from wastewater treatment plants that were potential point sources. This study highlights that multiple techniques are essential for a comprehensive assessment and interpretation of the organic fluorine burden in the aquatic environment.

Thallium in Brazilian river waters was determined ¹³² using a newly developed solid sampling HR-CS-GFAAS method that employed chromatographic filter paper for analyte enrichment and purification. This was achieved by placing the paper in a vial containing 9 mL of pH-adjusted sample together with EDTA. The paper was then directly analysed using the solid sampling HR-CS-GFAAS method. The LOD was 0.018 μg L⁻¹. The results from this fast and simple method demonstrated good agreement with comparative measurements using ICP-MS.

3.6.2 Vapor generation. Hydride generation remains one of the most common techniques for the determination of vapor-forming elements due to its excellent matrix separation capabilities and improved instrument sensitivity. To maximize the amount of information obtained by CVG AFS in a short time, a setup was developed¹³³ to simultaneously measure four elements (As, Bi, Hg and Sb) in coastal seawater. In many CF HG systems with a hydrogen diffusion flame atomizer, the H₂ required to sustain the flame is produced by the reaction of potassium borohydride under acidic conditions. This procedure has the drawback that the reaction conditions needed to generate the volatile derivatives cannot be decoupled from the need to ensure an adequate supply of H₂ for the flame. To address this issue, the authors developed an electrochemical H₂ generator, that allowed independent optimization of the spectrometer flame, enabling the simultaneous measurement of the four elements. The custom H₂ generator was connected to a specialized four-channel AFS system equipped with four hollow cathode lamps (HCLs), a photomultiplier as a detector, and an Ar–H₂ flame atomizer. With this setup, LODs of 0.015 μg L⁻¹ for As, 0.005 μg L⁻¹ for Bi, 0.001 μg L⁻¹ for Hg and 0.010 μg L⁻¹ for Sb were achieved, along with RSDs ranging from 1% to 4%. Additionally, when a KBr/HCl mixture was tested for the preservation of coastal seawater samples, the concentration of all four elements remained stable in the μg L⁻¹ range for at least 15 days. The method was validated against spiked seawater samples and was successfully applied to seawater samples collected from Xiamen Bay in Southeast China.

The precise determination of ultra-trace levels of chromium in seawater is challenging but essential. To address this,¹³⁴ a USN-DBD-VG system was developed and interfaced with an ICP-MS instrument. This allowed the detection of total Cr with a LOD of 4 ng L⁻¹, using only 40 μL of sample. The system combined a commercially available USN with a custom-built DBD reactor, coupled at a 90° angle to the USN. Various parameters, including spray distance, carrier gas flow, and pH of the solution, were optimized to enhance performance and reduce matrix effects. The method was validated against two seawater CRMs: the Chinese RM BWB2511-2016 (5 metals in natural seawater) and the NRCC CRM NASS-7 (seawater), which were in good agreement. Spike experiments demonstrated that the method maintained high accuracy (89–107%) even in the presence of high matrix concentrations. The optimized method was successfully applied to seawater samples from Qingdao and Weihai, both located in Shandong, China.

For the highly precise Hg isotopic ratio measurements at low concentrations, a method¹³⁵ was developed using a cold vapor generator-MC-ICP-MS equipped with 10¹³ Ω FC amplifiers and a high-transmission Jet interface. The authors tested changing the cone setup (standard versus Jet), plasma conditions (wet versus dry), and FC amplifier types (10¹¹ Ω versus 10¹³ Ω). For data evaluation under wet plasma conditions, a combined fractionation correction approach was utilized, incorporating both the Russell law, an internal correction approach using NIST SRM 997 (isotopic standard for thallium), and an SSB approach against the NIST SRM 3133 (mercury (Hg) standard solution) as external correction. Under dry plasma conditions, only the external correction method was applied. The study found that the optimal setup involved using Jet cones, dry plasma conditions, and all four available 10¹³ Ω amplifiers. This instrumental setup allowed for precise Hg isotopic ratio analysis at significantly lower concentrations in water samples (0.25 μg L⁻¹) that were comparable to those previously reported at 10 μg L⁻¹.

A single-vial sample preparation method for determining Hg²⁺ in water was developed¹³⁶ and evaluated to minimise errors during sample preparation. This method involved binding Hg²⁺ in the sample with MNPs modified with L-cystine after the addition of an acetate buffer. The NPs were isolated using a magnet and desorbed with an HCl solution before analysis by CV-AAS. The reducing agent (sodium borohydride) was introduced together with an argon gas stream directly into the vial. Under optimized parameters, the method LOD was 0.4 μg L⁻¹ with an effective EF of 24, offered a fast and straightforward approach for determining Hg in the sub-μg L⁻¹ range.

To enhance the LOD for As determination, synergistic enhancement effects with antimony and cadmium were investigated¹³⁷ using PVG coupled with ICP-MS. The highest sensitivity was obtained by mixing the sample with 10% v/v acetic acid, 5.0 mg L⁻¹ Sb^III, and 20.0 mg L⁻¹ Cd^II, achieving a LOD of 2.1 ng L⁻¹, which represented a 50-fold improvement compared to that obtained with pneumatic nebulisation. The method was validated by spike recovery from various (tap, lake, and river water) water samples and Chinese CRMs GBW07303a and GBW07305a (sediment), with relative measurement errors between −4% and +8%.

3.6.3 Inductively coupled plasma mass spectrometry. ICP-MS instruments equipped with triple-quadrupole-based mass separators are currently among the most versatile systems for interference removal using gases in CRCs. The use of different cell gases and their reactions with analytes in the CRC is still under active investigation. One promising gas is nitric oxide (NO), although its reactions with elements in the CRC are not yet fully understood. So, a study¹³⁸ was conducted on the applicability of NO in the determination of 50 different elements, with a particular focus on ²³⁹Pu for nuclear forensic applications. This involved experimental work and complementary theoretical (density functional theory) calculations of reaction enthalpies. The primary reaction product observed across the elements was MO⁺, which aligned well with the predicted MO⁺ reactivity from the theoretical models. Besides showing that NO is a suitable gas for the determination of Pu in challenging matrices, this study demonstrated that theoretical predictions of reactivity can effectively guide method development for the removal of isobaric and polyatomic interferences, offering valuable insights for a range of practical applications. A second study investigated¹³⁹ a broad range of elements (48 in total, with a focus on Pu) using different reaction gases (CO₂, N₂O, and O₂) and examined the influence of ion kinetic energy and cell gas flow rate on the reactions. In the ICP-MS/MS system used, these parameters correspond to the octupole voltage (V_Oct) and the cell gas pressure. The authors demonstrated that by selecting the appropriate energy and gas flow rates one could effectively remove isobaric interferences and cluster ions. These studies highlighted the significant potential and ongoing need for research to fully exploit triple-quadrupole technology because by removing more and more interferences in the CRC, sample preparation can be drastically simplified.

The capabilities of the triple-quadrupole mass separator for water analysis were demonstrated¹⁴⁰ in a river water study where three different CRC gases (He, H₂, O₂) were evaluated in the determination of 68 elements within a single ICP-MS/MS run. To validate and optimize the method, the authors used a wide range of water (C)RMs encompassing various water matrices and concentrations, alongside spike experiments. The BEC and the sensitivity were assessed for all investigated elements alongside monitoring for known isobaric interferences. With the optimized method, 68 out of 71 tested elements were accurately determined (excluding Au, I and Zr). The resultant best-practice method was further tested on samples from 12 different German rivers, confirming its suitability as a monitoring tool.

An automated¹⁴¹preconcentration method for determining Pu by ICP-MS/MS in the presence of uranium was developed. This method enabled the detection of Pu in seawater samples of up to 1 L. The procedure used the TK200 resin to isolate Pu from the acidified sample. The sample was treated with NH₂OH·HCl and NaNO₂, to adjust the valence of Pu, and loaded onto the column at a flow rate of up to 15 mL min⁻¹. The Pu was then eluted in 2 mL 0.5 mol L⁻¹ HCl and 0.1 mol L⁻¹ HF mixture, achieving and maintaining a recovery rate of 65%. While most of the uranium interference was removed during the isolation process, any remaining signal was effectively eliminated by use of O₂ in mass shift mode. A LOD of 2.32 μBq L⁻¹ was achieved and the method was validated using the RM IAEA-443 (Irish Sea water), yielding satisfactory results for both the Pu recovery and the ²³⁹Pu/²⁴⁰Pu isotope ratio. This optimized method was applied to glacier samples, with measured Pu concentrations ranging from 6 μBq L⁻¹ to 23 μBq L⁻¹.

One of the challenges of spICP-MS is accurately determining NPs in the presence of the ionic form of the same element. To address this, 3D-printed scavengers were developed¹⁴² that selectively retained ionic Ag but allowed AgNPs to pass through. The scavenger, which could be inserted into a standard syringe, was a 5 mm high and 16.5 mm diameter cylinder, cast from a 1 + 9 mixture of PS and an ion-exchange material, with a production cost of approximately 0.25 € per unit. The Ag NPs remained intact during the process, while the removal rate of ionic Ag exceeded 98%. Additionally, if needed, the ionic Ag could be desorbed with over 99% efficiency with sodium thiosulfate for subsequent analysis. The method was validated with water samples spiked with both ionic Ag and 30 nm and 50 nm Ag NPs. The results demonstrated that removing the ionic fraction improved the precision when determining both the size and number of nanoparticles. This optimized method was successfully applied to analyse both Ag fractions in ultra-pure water and spring water.

A ¹¹⁰Cd–¹¹¹Cd double-spike method for Cd isotopic measurement by MC-ICP-MS in sea water samples was developed.¹⁴³ One of the most critical steps for accurate isotopic ratio determination is the removal of the matrix, as matrix elements often introduce interferences that can hinder precise measurement. Given the high ionic load of seawater, a two-column separation process was optimized. The first column, Chelex® 100 resin, separated major seawater constituents such as sodium and magnesium from the Cd, while the second AG-MP-1M resin-filled column, eliminated residual sodium and magnesium, as well as isobaric interferences from elements like zinc, indium, and tin. The recovery for Cd from the first column was 98.3% ± 3.5%, and from the second column was 97% ± 3%. The method was validated with NIST SRM 3108 (cadmium (Cd) standard solution) spiked into synthetic sea water. The optimized method yielded δ¹¹⁴Cd/¹¹⁰Cd values ranging from −0.01‰ to 0.02‰, which are consistent with reference values. Analysis of three deep-water samples resulted in δ¹¹⁴Cd/¹¹⁰Cd values between 0.35‰ and 1.05‰.

The typically low concentrations make precise isotopic ratio determination by MC-ICP-MS of Sb in surface waters challenging. To address this, a new preparation method was developed that combined preconcentration and matrix separation to enable accurate ratio measurements. The procedure included¹⁴⁴ a digestion step designed to remove DOM, oxidise metals to a single high-valence state, and eliminate other interfering ions. This study highlighted the effectiveness of a BrCl-based digestion procedure, which achieved recovery rates for Sb above 95%. After digestion, the Sb^V present was reduced to Sb^III. For preconcentration and purification, the digested and reduced sample was loaded onto a thiol resin column. Elements, such as Sn, were washed from the resin with 2.5 M HCl. The Sb was eluted from the column using 6 M HCl, and H₂O₂ was subsequently added to the eluate. The solution was then heated in a closed vessel for 4 hours before being evaporated to near dryness and being redissolved into 3 M HCl + 0.5% (w/v) KI-ascorbic acid. The method was tested and optimized using NIST SRM 3102a (standard solution antimony), achieving sufficient precision for the entire procedure. The optimized method was successfully applied to various water sample types.

3.6.4 X-ray fluorescence spectrometry. There is a need for cost-effective and rapid elemental speciation methods to assess the potential (eco)toxicological relevance of elements. WDXRF can address elemental speciation by utilizing valence-to-core (VtC) electronic transitions. In this study,¹⁴⁵ the authors used the As Kβ_2,5 fluorescence line to quantify As^III and As^V in drinking water after preconcentration onto an alumina pellet. The As Kα_1,2 analytical lines were employed for total As determination. The LOD was 0.23 μg L⁻¹ for total As and 50 μg L⁻¹ for the As species. Validation was performed using spiked water samples with varying concentrations and species ratios. This study demonstrated the potential of WDXRF for direct speciation without extensive sample preparation (pre-concentration onto activated alumina, drying of the adsorbent, and press pellet) offering a fast and precise method for As speciation that requires further work to achieve realistic LODs for real samples.

To enable fast and sensitive detection of U in water samples, a direct preconcentration method¹⁴⁶ based on TXRF quartz sample holders functionalized with 3-amidoxytriethoxysilane was developed. The functionalization process involved cleaning and oxidizing the quartz sample holder surfaces by heating to 80 °C in a H₂SO₄/H₂O₂ (3 : 1) solution for 30 min, followed by rinsing. A 1 μL drop of the 3-amidoxytriethoxysilane solution was then deposited on the sample holder and dried. The prepared sample holders were dipped into a fixed volume of the sample solution and were subsequently rinsed with water to remove U from the non-functionalized areas. The method was validated by spike recoveries, achieving recoveries of 99–102%, and a LOD of 1 μg L⁻¹. This optimized method was applied to determine U concentrations in various water types, including groundwater, river water, and seawater.

Rare earth elements are widely used, and their release into water bodies due to mining and other human activities has become a concern. Therefore, efficient methods⁸⁶ for the preconcentration, storage, and determination of REEs in water samples are much needed. For this purpose, a resin based on N,N-bisphosphono(methyl)glycine-modified MIL-53(Al) was developed. This resin exhibited high selectivity towards REEs, enabling their separation from water and allowing storage of the samples as SPE discs. The separation process involved adding a small portion of the prepared resin to the sample, stirring for 5 min, filtering, drying, and then storing or analysing the sample, resulting in a fast and straightforward measurement procedure. The researchers demonstrated that the REE concentrations on the discs remained stable for at least six months. The XRF method was optimized for the simultaneous determination of 15 REEs (Ce, Dy, Er, Eu, Gd, Ho, La, Lu, Nd, Pr, Sm, Tb, Tm, Y and Yb) achieving LODs between 0.4 μg L⁻¹ and 4.7 μg L⁻¹. Spike recoveries for the investigated elements ranged from 80% to 125%. The method’s suitability was demonstrated by preparing on-site, water samples from the Gawu and Jinsha Rivers in China, as well as drinking and tap water samples. Another study focused¹⁴⁷ on the determination of lanthanides using TXRF. For this purpose, a CPE method was developed and optimized. The CPE utilized a mixture of N,N,N′,N′-tetra-octyl-diglycolamide, room-temperature IL and Triton X-114, which demonstrated selectivity towards lanthanides at pH 4 and exhibited high robustness against matrix interferences. The procedure was validated and optimized by spiking three RMs: NRCC NASS-7 (sea water) and SLRS-6 (river water), and NIST SRM 1640a (natural water). For the investigated elements (Dy, Eu, Gd, Ho, La, Lu, Nd, Sm, Tb and Tm) recoveries of 98–99% were achieved across the different matrices.

4 Analysis of soils, plants and related materials

4.1 Review papers

Several reviews focussed on the determination of certain elements. Liu et al.¹⁴⁸ (140 references) summarised methods for measuring Pu isotopes in a variety of environmental samples, whilst Dowell et al.¹⁴⁹ (112 references) focussed specifically on Pu in soil. Thallium was the element of interest for Shi et al.¹⁵⁰ (205 references) whilst Sager and Wiche¹⁵¹ (340 references) provided a comprehensive overview of the measurement and geochemistry of REE. De Almeida et al.¹⁵² (69 references) discussed analytical approaches for the determination of Sr isotopes and proposed a standard protocol for application of ⁸⁷Sr/⁸⁶Sr ratios in wine provenance studies.

The analysis of traditional Chinese medicines has proved a popular topic. Guo et al.¹⁵³ (110 references) reviewed the sources, effects, measurement, and risks of PTEs in medicines of marine origin, including marine plants. They recommended that improved quality systems be developed to ensure product safety. Two further reviews discussed research progress in the speciation analysis of As¹⁵⁴ (79 references) in animal- and plant-derived medicines, and of Hg¹⁵⁵ (73 references) in medicinal herbs.

Also of potential interest to ASU readers is a review¹⁵⁶ (133 references) on research progress in nano-scale SIMS in soil science. This technique can be used to study the behaviour and distribution of trace elements and is particularly suited to investigation of the interactions between PTE and soil components.

4.2 Reference materials

An evaluation⁷² of the²³⁷Np activity concentrations reported in literature for a suite of 25 SRMs and CRMs – including sediment, soil, seawater, airborne particulates, and plants – concluded that data obtained by ICP-MS were more accurate than those from radiometric techniques or AMS. A lack of soil CRMs containing low levels of ²³⁷Np was noted, so this nuclide was measured for the first time in CRM JSAC 0471, a soil from the area affected by the 2011 Fukushima Daiichi Nuclear Power Plant accident. The indicative value obtained was 5.8 ± 0.5 × 10⁻⁵ Bq kg⁻¹.

Improvements to analyte separation procedures prior to analysis by MC-ICP-MS have facilitated the measurement of new isotopic data for a variety of RMs. These included:

• A single-column separation¹⁵⁷ for Cd that allowed determination of δ^114/110Cd_{NIST SRM 3018} in IGGE GSS-1a (soil) and GSD-4a (stream sediment);

• A single-column separation¹⁵⁸ for Zn that provided δ⁶⁶Zn and δ⁶⁸Zn values in GSS-11 and GSS-13 (both soils) and GSS-33 (sediment);

• A single-column separation¹⁵⁹ for K that gave δ⁴¹K values for the CRMs BCR 679 (white cabbage) and IGGE GSV-2 (bush twin leaf), GSB-2a (wheat), GSB-3 (corn), and GSB-6a (spinach);

• A single-column separation¹⁶⁰ for Cu, Fe and Zn that gave δ⁵⁶Fe, δ⁶⁵Cu and δ⁶⁶Zn values in GBW10055 (whole soybean) and GBW(E)100199 (pumpkin);

• A two-stage (cation-exchange followed by anion-exchange) procedure¹⁶¹ for V that produced δ⁵¹V results for three sediments, one loss, and three soils; and

• A two-stage (both anion-exchange) procedure¹⁶² for Zn that provided the first δ⁶⁶Zn values for 20 Chinese reference soils.

4.3 Sample preparation

4.3.1 Sample dissolution and extraction. Reviews on digestion and extraction methods included that of Oliveira et al.²⁷ (147 references) on pyrohydrolysis methods for the determination of halogens. Experimental parameters reviewed included sample mass, organic matter content, absorbing solutions, additives to improve method accuracy, and LODs. System automation and compatibility with techniques including IC, AES, ICP-MS, and XRD were also discussed. Sequential extraction methods for the determination of metal(loids) in plants were compared by Jahangir et al.¹⁶³ (188 references). Difficulties in the extraction of Gd and Cr phosphates and oxalates in the appropriate stages by designated extractants such as 2% acetic acid or 0.06 M HCl were highlighted. A review¹⁴⁸ (140 references) of methods for the determination of Pu covered pretreatment procedures, such as ashing, acid digestion and fusion, separation and purification methods, including LLE and SPE, as well as LODs of MS detection techniques such as TIMS, AMS, ICP-MS and SIMS in environmental matrices. Uses and environmental levels of Pu were also reviewed, and the establishment of a Pu database for environmental monitoring of the radionuclide proposed.

A ‘new generation’ microwave applicator for high-pressure flow digestion was developed by Hallwirth et al.¹⁶⁴ The digestor was composed of a microwave-heated applicator that housed a 2 mm internal diameter, coiled, pressurised, digestion tube, the geometry of which was optimised using computer simulation to achieve uniform heating. The digestion coil and all fittings were made of PFA, thus allowing the use of all acids including HF. The volume capacity of 22 mL and digestion temperature of 230 °C, were reported to be an improvement on those of similar systems that operated at ambient pressure. The accuracy of the method was evaluated by the analysis of NIST SRM 1547 (peach leaves), IAEA-A-13 (freeze dried animal blood), and BCR CRM 185R (bovine liver). Digestion was performed with a 5 mL mixture of 6 M HNO₃ and 3 M HCl, 500 W microwave power, 40 bar pressure and a carrier flow rate of 5 mL min⁻¹. Relative measurement errors in analyte concentrations, determined by ICP-AES and ICP-MS, were within ±10% for Al, As, B, Ba, Ca, Cu, Fe, K, Mg, Mn, Na, Rb, Sr, and Zn, but for Cd in BCR-185R and Fe in SRM 1547 ranged from −14% to +21% (t-distribution, p < 0.05). Residual carbon concentrations in these digests were <50 mg L⁻¹. Direct coupling of the system with ICP-MS was also being explored.

In methods for extraction of PTE from soils, the optimisation of DESs continues to be explored. The extraction efficiency of choline chloride with oxalic acid (1 + 2 mol mol⁻¹) was found to be superior¹⁶⁵ to combinations of choline chloride with ethylene glycol, 1,2-propanediol, xylitol, malic acid, or citric acid. Under optimised conditions (0.1 g sample, 1 g solvent, 40 min UAE, T = 80 °C), no significant difference was observed between concentrations of target analytes Al, Be, Ca, Co, Cr, Cu, Mn, P, Pb, V and Zn in 24 soil RMs, following extraction with the DES, and certified values, as determined by ICP-AES, or between concentrations determined in real soil samples following digestion with HCl–HNO₃–HF–HClO₄ and following extraction with the less toxic DESs (Student’s t-test, 95% CI). A mandelic acid dimer was proposed¹⁶⁶ for the extraction of Cd and Cu. The optimised procedure involved preparation of the dimer by heating 100 mg of the solid at 120 °C for 30 min in an ‘antifreezing/anti-boiling bath’, followed by the addition of 0.2 g of soil and 10 mg of sodium diethyldithiocarbamate complexing agent. After microwave irradiation (3 min, 150 W), the mixture was vortexed (750 μL, 5% v/v HNO₃), centrifuged (6 min, 7000 rpm) and analysed (FAAS). The LODs were 0.16 and 0.17 mg kg⁻¹ for Cd and Cu, respectively. Spike recoveries were 88–93% for Cu and 93–97% for Cd (10 and 25 mg kg⁻¹ additions). The relative measurement error for Cu in BCR CRM-142R (light sandy soil) was <5%. For Cd, the method LOQ of 0.5 mg kg⁻¹ was above both the certified content in the CRM and median soil background levels (EA 2007) for Cd, preventing verification of accuracy and limiting the application of this method to contaminated soils. Extraction of bioavailable As with 0.5 M NaHCO₃ is a common procedure, however, the high concentration of dissolved solids prevents analysis of the extractant with ICP-MS. In a quest for ICP-MS compatible extractants, Mishra et al.¹⁶⁷ compared 0.5 M NaHCO₃ to 0.1 N and 0.5 N H₃PO₄, 0.05 M and 0.25 M H₂SO₄, 0.1, 0.5, 1.0 and 1.5 M HNO₃, and 0.01 M CaCl₂ for the estimation of bioavailable As in 201 paddy field soils in West Bengal, India. Highest correlation was observed between 0.5 N NaHCO₃-extractable As and 1.5 N HNO₃-extractable As (r = 0.47 and r = 0.64 in case of grain and straw, respectively, significant at the 0.01 level), making this last extractant a potential alternative that is more compatible with ICP-MS.

In microwave-assisted extraction methods for PTE in plants, 216 modifications were studied¹⁶⁸ in the optimisation of a tunnel-type microwave digestion method for simultaneous determination of 18 elements by ICP-AES. Optimum conditions were found to be a sample size of 0.5 g, sequential addition of 4 mL HNO₃, 1.5 mL H₂O₂, 1 mL HCl, and 0.05 mL HF, and a three-stage vessel heating procedure with an initial rate of 2.76 K min⁻¹, maximum T of 180 °C and hold time of 15 min. Relative measurement errors in concentrations of Al, B, Ba, Ca, Cu, Fe, K, Na, Mg, Mn, Ni, P, Rb, Si, Sr, Ti, V, and Zn in Russian CRMs GSO 8921-2007/SO KOOMET 0065-2009-RU EC-1 (Elodea canadensis), GSO 8922-2007/SO KOOMET 0066-2008-RU (Gr-1) (grass mixtures), GSO 8923-2007/SO KOOMET 0067-2008-RU BL-1 (birch leaf), and GSO 11961-2022. PSN-1 (Pinus sibirica needles) were within ±10%, indicating wide-ranging suitability of the method with regard to species and elements.

Methods involving digestion with heating blocks, included a technical note¹⁶⁹ on a closed-vessel, convective heated aluminium block digestion system for the preparation of plant samples. The block was housed inside an insulated heating chamber thus ensuring that surfaces did not reach high temperatures for operator safety. Digestion vessels were composed of 45 mL quartz tubes and PTFE vessel lids with built-in safety disks designed for 28 bar maximum pressure, attached via a PVC connector. Experimental parameters in the 24-slot heater were optimised at 250 mg sample mass, 2.0 mL HNO₃, 1.5 mL of H₂O₂ and a block temperature of 240 °C, corresponding to a liquid-phase temperature of 190 °C. Sample digestion required about 50 min including cooling step. No significant differences (unpaired t-test, 95% CI) were found between the certified values and concentrations of macronutrients Ca, K, Mg, P, and S and micronutrients B, Cu, Fe, Mn and Zn in plant standard reference materials NIST RMs 1547 (peach leaves), 1515 (apple leaves), and 1570a (spinach leaves) as determined by ICP-AES, indicating the closed-vessel digestion block method can provide a fast, safer, alternative to open-vessel acid digestion. Periera et al.¹⁷⁰ optimized a heating block digestion method by applying fractional factorial design. Vessels and lids were composed of PTFE, optimum parameters were found to be a digestion mixture composed of 1.38 mL HNO₃, 1 mL H₂O₂ and 2.62 mL deionized water, a heating temperature of 180 °C and digestion time of 120 min. In the quantification of 14 PTE in medicinal herbs by ICP-AES, LODs ranged from 0.06 (Cd) to 1.9 (P) mg kg⁻¹. Accuracy was validated with CENA-USP CRM Agro C1003a (tomato leaves), CRM-Agro C1005a (sugar cane leaves) and CNACIS NCS DC 73351 (tea) with relative measurement errors of −17% to +18%.

In the determinations of trace levels of Si in plants by ICP-MS, Arslan et al.¹⁷¹ found that hexafluorosilicates, formed in HF-based digestions, were highly volatile, resulting in losses when digestates were heated to eliminate HF and other acids. To overcome this, the authors developed a closed-vessel block digestion for plant samples based on two steps: an initial digestion of 0.1 g of sample with 4 mL HNO₃ and 1 mL HCl for 5 h at 140 °C with subsequent evaporation to incipient dryness, followed by a second step with 0.5 mL HNO₃ and 0.5 mL HF at 130 °C for 2 h to dissolve silicates. After dilution to 15 mL with deionised water, digests were directly analysed using an ICP-MS instrumentation equipped with an HF-inert sample introduction system. Relative measurement errors varied, depending on both the reference material matrix (NIST SRM 1573a (tomato leaves), SRM 1575 (pine needles), CRM 1547 (peach leaves) and SRM 1572 (citrus leaves)) and the ICP-MS measurement mode (standard or kinetic energy discrimination). The authors aim to improve Si stabilization prior to acid evaporation to reduce loss of the analyte in future work.

4.3.2 Analyte separation and preconcentration. Separation and preconcentration methods with solid sorbents included a DGT method by Han et al.¹⁷² for determination of labile As^III and As^V in soil using a Fe₂O₃·xH₂O binding gel. Uptake of As species, elution conditions, and applicability to natural soils were tested and optimised. In solution, accumulation of As species (approx. 490 ng) on the binding gel was achieved in 90 min. In soils, samplers were directly inserted into soil paste (100 g) at 25 °C, with an accumulation time of 48 h. Optimum elution of the binding gel was achieved by heating with 0.8% H₃PO₄ in a water bath at 90 °C for 80 min. Concentrations were measured with HPLC-ICP-MS. The LODs were 0.01 and 0.005 μg L⁻¹ for As^III and As^V, respectively. Moens et al.¹⁷³ developed a hydrogel-free DGT binding layer for the mapping of labile P in soils. Unlike conventional hydrogel-based binding layers, the polyimide-based layer contained a finely powdered titanium dioxide P binding agent for superficial binding of P, enabling its detection by synchrotron-based XFM. The novel method was applied to study the diffusion of P from three different fertilizers in soil-fertilizer incubation experiments. Results of selected XFM analyses were confirmed by LA-ICP-MS. Data harvested regarding fertiliser P diffusion radii and concentrations can potentially be used to improve fertilizer efficiency. Determination of Be in plants by ICP-MS is challenging since, in addition to the complex organic matrices, concentrations are lower relative to other essential plant nutrients. To remove matrix effects and achieve preconcentration, a single-step separation-purification method was developed¹⁷⁴ based on cation-exchange with an AG® 50W-X12 resin (BioRadTM, USA). Between 100 and 500 mg of plant sample were digested using a multi-step digestion procedure with HNO₃, H₂O₂, HF. The digests were then loaded onto cartridges made from Pasteur pipettes, equipped with frits, which were packed with the resin. Elution of Be was achieved with 5.5 mL of 0.3 M HF, the first 1.5 mL of which were added incrementally in 0.5 mL aliquots. The eluate was evaporated to dryness and re-dissolved in 0.3 M HNO₃ (4 mL) for ICP-MS analysis with Li (5 ng g⁻¹) as an IS. Seven RMs were analysed with, and without, the purification process: WEPAL IPE 100 (grass), WEPAL IPE 151 (grass), WEPAL IPE 176 (reed), and WEPAL IPE 220 (willow wood), NIST SRM 1515 (apple leaves) (not certified for Be) and SRM 1573a (tomato leaves), and ERM-CD 281 (rye grass) (not certified for Be). Concentrations of Be determined following purification were consistently significantly lower than certified values, indicating further research is necessary.

In the determination of radionuclides, a novel method was developed¹⁷⁵ for the simultaneous determination of Pu and Np in soil samples using a TK200 resin. Optimised loading was obtained with 6–12 M HNO₃ with addition of 0.01–0.12 M NaNO₂ for valence adjustment. Elution was achieved with a solution of 0.1 M HCl, 0.05 M HF, and 0.01 M NH₂OH·HCl (40 mL). The method was validated by analysing environmental soil samples (5 g) spiked with 0.2 g of IAEA-384 (Fangataufa Lagoon sediment) or 1 g of IAEA-385 (Irish Sea sediment). Measured values of ²³⁷Np, ²³⁹Pu, and ²⁴⁰Pu, obtained by ICP-MS/MS, were consistent with their certified values within a 95% CI, and method precision was <6%. Results were superior to those obtained with TEVA and AGMP-1M resins. Method LODs (10 g samples) were 0.13 fg g⁻¹ for ²³⁹Pu, 0.07 fg g⁻¹ for ²⁴⁰Pu, and 0.27 fg g⁻¹ for ²³⁷Np, adequate for monitoring of the radionuclides in soil and sediment samples.

Other separation and preconcentration methods for the analysis of soils, plants or related materials, or those developed for other sample matrices that used soil or plant CRMs for validation, are summarised in Tables 5 (LPE methods) and 6 (SPE methods).

Table 5 Preconcentration methods involving liquid-phase (micro)extraction used in the analysis of soils, plants and related materials

Analyte(s)	Matrix	Technique	Extraction mode/reagent(s)	Findings/comments	LOD	Validation	Reference
Cd, Pb	Sediment, soils (agricultural, tea-planted, basalt, forestry), livestock feed, sea salt	LLE FAAS	Sodium diethyldithiocarbamate and ammonium pyrrolidine dithiocarbamate chelating solution, 8-hydroxyquinoline masking agent to chloroform (5 mL)	0.5–3 g sample for sediment, soil and livestock feed, 35–40 g sample for sea salt, re-extraction in HNO3 (5–10 mL, 2 M)	Soil 6.9 (Cd), 72 (Pb) μg kg^−1, live feed 8.5 (Cd), 82 (Pb) μg kg^−1, sea salt 7.5 (Cd), 76 (Pb) μg kg^−1	Spike recovery (0.15 μg g^−1 Cd, 5 μg g^−1 Pb in soil, and 0.1 μg g^−1 Cd, 1 μg g^−1 Pb in salt)	176
Cr^VI	Spinach	LLME FAAS	DES DL-menthol and formic acid	0.25 g sample, 80 μL DES	0.63 μg L^−1	Spike recovery (25, 50 and 100 μg kg^−1)	177
Cu	Rose bud samples	Emulsification LLME FAAS	DES choline chloride + phenol (1 + 2), THF emulsification agent	2.2 g sample, 0.4 mL DES	2.5 μg kg^−1	Spike recovery (50, 76, 99 and 172 μg kg^−1 in rose tea brand 1, and 27, 50, 101 and 118 μg kg^−1 in rose tea brand 2)	178
Cu, Pb, Sn	Corn, soil	CPE ICP-AES	2-(4-Sulfonylamidebenzo)hydrazide-1-dithiocarbamate ligand, polyethylene glycol tert-octylphenyl ether (Triton X-114) surfactant	1 g for corn, 0.25 g for soil, 0.01 M dithiocarbamate (1 mL) and 5% (v/v) Triton X-114 (0.1 mL) for CPE	0.00021 (Cu), 0.0024 (Pb), 0.0004 (Sn) μg L^−1	Chinese CRM GBW10011 (wheat flour) and GBW10012 (corn flour), spike recovery (0.5 mg kg^−1 of Cu, Pb and Sn in 3 corn and 3 soil samples)	179
Pb	Canned vegetables (corn, cucumber, green lettuce, mustard), chili pepper, garlic, onion, tomato paste, water (drinking, tap)	Electromembrane-assisted hollow-fibre LPME ETAAS	1-Octanol on supported liquid membrane hollow fibre with DES choline chloride + phenol (1 + 2)	0.5 g sample, hollow fibre and electric system applied in 30 mL sample volume, 30 V applied for 25 min throughout extraction	0.011 μg L^−1	NRCC CRM SLRS-6 (river water) and NIST SRM 1515 (apple leaves), spike recovery (0.3 and 0.5 μg L^−1 in all sample matrices)	180
Se	Black tea leaves	Air-assisted CPE HG AAS	1-(2-Hydroxy-5-ptolylazo-phenyl)-ethan-one ligand, Triton X-100 surfactant	Air agitation performed at room temperature, 0.2 g sample, 1 mL ligand, 0.5 mL surfactant. Cloud point layer dissolved in 1 mL ethanol prior to analysis	0.02 μg L^−1	Chinese CRM GBW08513 (tea leaves), spike recovery	181

---

Table 6 Preconcentration methods involving solid-phase (micro)extraction used in the analysis of soils, plants and related materials

Analyte(s)	Matrix	Technique	Extraction mode/reagent(s)	Findings/comments	LOD	Validation	Reference
As	Rice	Magnetic SPE ICP-AES	Fe3O4@DTPMP	0.5 g sample in 50 mL, 20 mg sorbent, 10% HNO3 for elution (2 mL)	1.08 μg kg^−1	NIST SRM 1568b (rice flour)	182
Cd	Chocolate, spice, tea, tobacco, wastewater, water (dam, river, sea, spring, tap)	Magnetic dispersive μSPE HR-CS-FAAS	Fe3O4–SiO2-MIL-53 magnetic MOF nanomaterial	0.25 g sample in 20 mL, 20 mg sorbent, 1.0 M HNO3 for elution (4 mL)	1.3 μg L^−1	NIST SRM 1573a (tomato leaves), NIST SRM 1570a (spinach leaves), ECCC RM TMDA – 64.3 (fortified water)	183
Cd, Cu	Cocoa powder, juice (apple, cherry), plants (carrot, garlic, leek, lettuce, parsley, radish, rice, scallion, spinach, potato, pepper, tomato, walnut), water (drinking, mineral, tap)	Vortex-assisted dispersive SPME FAAS	Magnetic polystyrene-b-poly dimethyl siloxane block copolymer sorbent	1–2 g sample in 50 mL, 250 mg sorbent, 0.5 M HCl for elution (1 mL)	1.7 (Cd), 0.8 (Cu) μg L^−1	NIST SRM 1570a (spinach leaves) and IRMM CRM BCR M032 (phosphate rock), spike recovery (20 and 40 μg L^−1)	83
Cu	Plants (lettuce, mint, rice), soil, water (bottled mineral and tap)	Dispersive magnetic SPME FAAS	Fe3O4 MNPs modified with an SiO2 shell and creatine to obtain inorganic–organic nanosorbent (Fe3O4@SiO2-CRT)	1 g for lettuce, mint and rice in 10 mL, 0.2 g for soil in 100 mL, 20 mg sorbent, 0.5 M HNO3 for elution (0.5 mL)	1.5 μg L^−1	Spike recovery (5, 20 and 100 μg L^−1 for aqueous samples; 5, 20 and 100 μg kg^−1 for solid samples)	90
Cu, Ni	Soil, water (natural)	μSPE FAAS	ZnMnAl layer double hydroxide NP sorbent	1 g soil in 30 mL, 5 mg sorbent, 0.1 M HNO3 for elution (2 mL)	0.75 (Cu), 0.57 (Ni) μg kg^−1	IRMM CRM BCR 505 (estuarine water) and Chinese CRM GBW GSS-15 07429 (soil)	184
Cu, Pb	Plants (celery, lettuce radish, spinach), wastewater, water (lake, sea)	Magnetic dispersive SPME FAAS	Magnetic mesoporous carbon (Fe3O4@C) sorbent	0.5 g for solid samples in 20 mL, 100 mg sorbent, 2 M HCl for elution (3 mL)	0.87 (Cu), 2.8 (Pb) μg L^−1	ECCC RM TMDA-53.3 (fortified lake water) and NIST SRM 1573a (tomato leaves), spike recovery (100 and 200 μg L^−1 for lake water and wastewater, 75 and 150 μg L^−1 for sea water and waste water, 3 and 6 μg g^−1 for celery, radish and spinach, 6 and 12 μg g^−1 for lettuce)	185
Hg	Soil	Mini lithium-battery-powered Headspace SPME, miniature point discharge AES	Gold-coated tungsten (Au@W) SPME fiber	0.5 g in 50 mL, 20 s desorption time, 2.5 W power consumption	0.008 mg kg^−1	Chinese CRM GBW07980	186
Mn, Pb	Plants (cinnamon, tea), wastewater, water (dam, sea)	Dispersive SPME FAAS	NiCo2O4@ZnCo2O4 ternary nanocomposite sorbent	0.5 g in 50 mL for Mn, 0.5 g in 100 mL for Pb, 50 mg sorbent, 2 M HCl for elution (3 mL)	1.7 (Mn), 4.0 (Pb) μg L^−1	IRMM CRM BCR 482 (lichen), ECCC RM TMDA-70.2 (lake water), and NIST SRM 8704 (Buffalo river sediment), spike recovery (75 and 150 μg L^−1 for waters, 120 and 240 μg g^−1 for tea, 300 and 600 μg g^−1 for cinnamon)	187
Ni	Sediments, water	SPE FAAS	Mini-column of bamboo fibres modified with 2-(5-bromo-2-pyridylazo)-5-(diethylamino) phenol sorbent	1 g sediment, sample vol. 100 mL, 0.4 g sorbent, 1 M HCl for elution (2 mL)	0.75 μg L^−1	NIST SRM 1944 (New York/New Jersey waterway sediment), spike recovery (20 and 50 μg L^−1)	188
Pb	Baby food, cocoa powder, plants (coriander, carrot, dill, starch, tea, tobacco), water, wastewater	Magnetic SPE FAAS	Magnetic Luffa@TiO2 sorbent	0.25 and 1 g for solid samples in 50 mL dep. on matrix, 5 mg sorbent, sample volume, 3 M HNO3 for elution (1 mL)	0.04 μg L^−1 for liquid samples, 0.159 μg kg^−1 for solid samples	NIST SRM 1577b (bovine liver), ECCC RM TMDA-53.3 and TMDA-64.3 (fortified water), spike recovery (0.1 and 0.2 mg L^−1 for water and 0.1 and 0.2 μg kg^−1 for starch and tea)	189
Pb	Automobile battery water, water (river, sea), cigarettes and soil	Vortex-assisted SPE HR-CS-FAAS	Magnetic adsorbent with poly(N-isopropylacrylamide)	15 mg sorbent, 20 mL samples, mass of solid samples not reported, 3.0 M HNO3 for elution (1.0 mL)	0.05 μg L^−1	ECCC RM TMDA 64.3 (fortified water) and polish certified RM e INCT-OBTL-5 (oriental basma tobacco leaves), spike recovery (between 2 and 9 μg g^−1 depending in matrix)	190
Pb	Chicken, plants (rice, tea powder tomato)	CPE, μSPE ETAAS	Montmorillonite sorbent, 4-(2-pyridylazo) resorcinol monosodium salt (PAR) as chelating agent and triton X-114 surfactant	0.2 g in 50 mL, 2 mg sorbent, 0.5 M HNO3 for elution (1 mL)	0.006 μg L^−1	NIST SRM 1643 f (trace element in water) and SRM 3255 (green tea extract), spike recovery (0.2 and 0.3 μg g^−1 for chicken, rice and tomato, 3 and 6 μg g^−1 for tea)	191

---

4.4 Instrumental analysis

4.4.1 Atomic absorption spectrometry. There has been considerable interest in the development of new analytical methods based on HR-CS-AAS. A procedure for the determination¹⁹² of Tl in plant leaves involved MAD in HNO₃, CPE, and the use of 100 μg Zr + 10 μg Ir as a permanent chemical modifier. The LOD was 0.05 ng mL⁻¹ and the LOQ 0.17 ng mL⁻¹. Accurate results were obtained for BCR CRM 060 (aquatic plant) and NIST SRM 1572 (citrus leaves) according to a Student’s t-test at 95% CI. A cold vapor HR-CS-QT-AAS method¹⁹³ for Hg proved applicable to a wide range of sample types – not only soil, sediment and mushrooms but also fish and (bio)polymers – with an LOD of 0.014 ± 0.001 mg kg⁻¹ for environmental samples. The same group of researchers also developed¹⁹⁴ a HG-HR-CS-QT-AAS method for total and inorganic As, a key advantage of which was avoidance of an LLE step in the sample treatment protocol for measurement of the inorganic As fraction. The LOD was 0.006 mg kg⁻¹ for both species; the results obtained for total As in soil and sediment CRMs were accurate; and spike recovery for inorganic As was 95 ± 10%.

Aramendia et al.¹⁹⁵ optimised a new HR-CS method for the direct determination of B in solid samples based on molecular, as opposed to atomic absorption. The analyte was quantified by detection of the BF molecule created by a gas-phase reaction with methyl fluoride. Both a W permanent modifier (250 μL, 1000 mg L⁻¹) and a mixed liquid modifier (10 μL of a 15 g L⁻¹ citric acid solution + 10 μL of a 1000 mg L⁻¹ Ca solution) were required, giving a LOD of 0.6 mg kg⁻¹. The result obtained for NIST SRM 1570a (spinach leaves) was 35.6 ± 9.4 mg kg⁻¹, cf. certified value 37.6 ± 1.0 mg kg⁻¹, and for SRM 1573a (tomato leaves) 28.5 ± 6.8 mg kg⁻¹, cf. 33.3 ± 0.7 mg kg⁻¹. The interesting possibility of measuring B isotope ratios based on wavelength shifts in the ¹¹BF and ¹⁰BF molecular signals was also explored.

4.4.2 Atomic emission spectrometry. A key requirement for the successful development of portable AES instrumentation is low power consumption. Two approaches with potential for use in the field determination of Hg in soil were described. The first method used¹⁸⁶ a gold-coated tungsten fibre for SPME of Hg⁰ – produced by NaBH₄ reduction of Hg²⁺ in aqua regia digests of soil samples – as a Au/Hg amalgam. The fibre was then heated using a 3.7 V lithium battery to desorb the analyte vapor into a point discharge microplasma. Overall power consumption was reduced from 60 W to 2.5 W compared to conventional external heating desorption devices and the instrument was more compact. The second method incorporated¹⁹⁶ a novel piezoelectric transformer driven microplasma, thereby eliminating the need for a high voltage power supply, and HG for analyte separation and interference elimination. A LOD of 2.8 μg L⁻¹ was obtained and the result for NRCC CRM GBW07405 (soil) was identical to the certified value. Although the spectroscopic aspects of these works show great promise, the need to acid digest the samples prior to analysis remains a challenge for actual field deployment.

A rapid HPLC-ICP-AES method for Fe speciation analysis in soil, sediment and archaeological pottery¹⁹⁷ featured a short (50 mm) cation-exchange column (Dionex IonPac CG5A) in place of the standard (250 mm) length column (Dionex IonPac CS5A). Adequate separate of Fe^II and Fe^III was achieved in 240 s (as opposed to 480 s) and both mobile-phase consumption and power use were significantly lowered. A method for the determination of total and labile Cd, Cu, Pb and Zn in soil based on a miniature ETV-μCCP-AES system in accordance with the principles of green analytical chemistry was developed.¹⁹⁸ The accumulation of analytes by DGT eliminated previously challenging non-spectral interferences, and improved LODs by at least one order of magnitude. Relative measurement errors for the analysis of four soil CRMs ranged from −15 to +23% and the LODs ranged from 0.03 (Zn) to 0.4 (Pb) mg kg⁻¹.

Microwave-induced plasmas continued to be of interest. Stoitsov et al.¹⁹⁹ modified the sample introduction system (specifically, widened the inlet to the GLS) to improve sample mass transfer in their PVG-MP-AES method for the determination of Hg. Following optimisation, the LOD was 0.25 ppb and there was no significant difference between measured and certified values for two soil CRMs (at 95% confidence). Serrano et al.²⁰⁰ demonstrated the capability of MICAP-AES for the analysis of complex samples by measuring 12 elements in a range of CRMs, including biological materials, polyethylene, and BCR 483 (sewage sludge amended soil). A detailed evaluation of spectral and non-spectral interferences was also performed.

4.4.3 Inductively coupled plasma mass spectrometry. Several review articles have focussed specifically on ICP-MS. Naozuka et al.²⁰¹ (101 references) summarised the contribution of the technique to improved understanding of the behaviour and impact of NPs in edible plants; Ermolin and Fedotov²⁶ (69 references) discussed methods for the study of NPs in soil and dust, with particular emphasis on sp-ICP-MS; and Nguyen et al.²⁰² (136 references) proposed that ICP-MS with data analysis by PCA could be a standard approach for authentication of the geographical origin of plant-based foods. Kierulf and Beauchemin²⁰³ (46 references) wrote a useful tutorial review on the ability of continuous on-line leaching coupled with ICP-MS to improve PTE bioaccessibility testing in food safety assessment. As well as being faster than batch extraction methods, the dynamic nature of the information obtained provides clues to the origins of any contaminants present. For example, two peaks observed in a leaching profile of a corn bran RM were found to have different Pb isotope ratios, indicating the presence of Pb from two distinct sources.

A high-efficiency miniaturised USN²⁰⁴ offered improved sample introduction efficiency in ICP-MS by decreasing the mean aerosol particle size. The LODs for REE ranged from 0.03 (Tm and Lu) to 1.07 (Sc) ng L⁻¹ (similar to those for pneumatic nebulisation but with a ten-fold reduction in sample consumption), and results for soil and sediment CRMs were predominantly within 5% of certified or indicative values. A novel slurry sampling apparatus²⁰⁵ reduced the problem of sample deposition during transport to the plasma in ETV-ICP-MS. It incorporated a “gas turbulator line” – a second stream of Ar gas introduced at right angles to the carrier gas via an annular aperture to create turbulence – and a signal delay device that meant an increased number of points could be recorded over the course of the transient signal. Precision for the direct determination of As, Cd, Pb and Se in plant-based foods was improved from 15–16% to 1–9%, with LODs in the range 0.3 to 0.6 ng g⁻¹ for 10 μL slurry samples.

The ability of transition metals to improve sensitivity in PVG-ICP-MS has been exploited for the determination of As in sediment.¹³⁷ Optimal generation efficiency was obtained with a 100 s UV irradiation in the presence of 10% (v/v) acetic acid + 5.0 mg L⁻¹ Sb^III + 20 mg L⁻¹ Cd^II. This gave a 50-fold increase in sensitivity relative to that of conventional pneumatic nebulisation and a LOD of 2.1 ng L⁻¹. Similarly, a method²⁰⁶ for simultaneous determination of Se and Te involved a 30 s irradiation with 15% (v/v) formic acid + 15% (v/v) acetic acid + 50 mg L⁻¹ Co^II sensitizer. The LODs were 0.5 ng L⁻¹ for Se and 0.6 ng L⁻¹ for Te. Results for analysis of 11 geological CRMs, including two soils, were consistent with certified and literature values.

New analytical methods coupling chromatographic separation with ICP-MS continued to be developed. Examples included a fast HPLC-ICP-MS procedure²⁰⁷ for the quantification of inorganic As in seaweed that used HNO₃ sample extraction to remove potential interference from arsenosugars, and an IC-ICP-MS method²⁰⁸ that was used to study the distribution of Ge^IV, monomethylGe and dimethylGe in soil from the vicinity of an e-waste processing plant. An important contribution to analytical capabilities for study of trace element speciation in soil solution was an HPLC-ICP-MS method²⁰⁹ that could separate metal–organic species in DOM. A key feature was the use of a second HPLC pump to deliver a post-column compensation gradient that inversely mirrored the separation gradient, meaning the mobile phase was of constant composition on entering the ICP. Application to samples from saturated and unsaturated soil horizons revealed differences in Cu, Fe and Ni speciation in different redox environments.

A multi-facetted ICP-MS approach²¹⁰ for Se speciation analysis in soil involved simultaneous extraction of Se NPs, inorganic Se^IV, Se^VI, and organic selenides by use of 5 mM Na₄P₂O₇ + 1.2 μM KH₂PO₄ followed by filtration through a 0.45 μm pore size nylon membrane filter. The NPs were retained on the filter then digested in HNO₃ for determination of their Se content, whilst species in the filtrate were separated and quantified by HPLC-ICP-MS. Total Se was determined following MAD and the concentration of non-extractable metal selenides calculated by difference.

In vitro fossilisation²¹¹ was a novel means to improve the analysis of plant tissues by LA-ICP-TOF-MS. Replacing water with a silicate matrix by treating samples with sodium metasilicate at pH 13–14 for 24 hours not only preserved the complex 3D-structure of the biological material but also meant that the laser ablation craters had clean-cut edges (in contrast, ablation of dried leaves caused uneven removal of material outside the crater) and that Si could be used as an IS, allowing quantitative element imagining to be carried out at high spatial resolution. Mapping was performed on leaves of plants grown in unamended and Cd-spiked soil for Cd and Cu (soybean) and Cd and Zn (sunflower).

Several articles addressed the preparation of improved matrix-matched calibration standards for LA-ICP-MS analysis of plants or fungi. Standards based on a (8 + 2) mixture of gelatin and hydroxypropyl methyl cellulose²¹² were stable, homogenous, and proved suitable for use in the quantitative mapping of Cu, Mn, Sr and Zn in leaves of Trigonotis peduncularis. The incorporation²¹³ of 5 mmol L⁻¹L-cysteine improved the stability and element distribution in gelatin-based droplet standards, allowing the distribution of Hg and Se to be studied in the fruit bodies of porcini mushrooms. Incorporation of chitosan was also investigated but, despite being a better match to the composition of mushroom tissue, it gave poorer precision. Finally, a method²¹⁴ for wood analysis used standards prepared from finely ground Pinus taeda. Both conventional and one-point calibrations were used to quantify Ba, Cd and Pb in samples of Tipuana tipu.

Understanding the uptake and distribution of NPs in plants is important to assess their environmental fate and impact. A high throughput LA-ICP-MS method²¹⁵ developed using animal (woodlice) tissue was shown to be applicable for the mapping of Ag to a wide range of aquatic and terrestrial biota, including wheat, following exposure to Ag₂S NPs or AgNO₃ spiked soil. By doping²¹⁶ polystyrene NPs with Eu it was possible to follow their uptake in cucumber root, stem and leaf by LA-ICP-MS. A cryogenic chamber suppressed the evaporation of water and maintained the structure of the fresh sample.

Optimisation of the reaction cell gas mixture is key to the successful determination of analytes by ICP-MS/MS. A detailed assessment¹³⁸ of the reactivity of NO with 50 elements revealed MO⁺ as the dominant product, with a few elements also forming a small amount (<4%) of MN⁺. In the first application of this gas in actinide analysis, a NO flow rate of 0.37 mL min⁻¹ was found to be optimal for the determination of Pu in mass-shift mode as PuO⁺. Impressive results within 6% of target values were found when digests of NIST SRMs 4250b (river sediment), 4354 (lake sediment) and 2711a (Montana soil) were spiked with 0.05 pg g^{−1 239}Pu, even when 1 μg g^{−1 238}U was also added, with no need for a complex matrix separation procedure prior to analysis. A method²¹⁷ for the determination of ²³⁷Np, ²³⁹Pu and ²⁴⁰Pu involved optimisation of an analyte separation based on TK200 resin and use of He (7.5 mL min⁻¹) + CO₂ (1.1. mL min⁻¹) to eliminate interference from UH⁺ and peak tailing from U⁺. The LODs were 0.14, 0.51, and 0.08 fg mL⁻¹ for ²³⁷Np, ²³⁹Pu, and ²⁴⁰Pu, respectively. A new ICP-MS/MS method was also reported²¹⁸ for the determination of Se in soil and maize.

New sp-ICP-MS methods for the study of NPs in soils continue to be developed and applied. Systematic investigation²¹⁹ and optimisation of MAE-based procedures for recovery of Ag-, Au-, Se- and Pt NPs from spiked soils with a range of physicochemical properties, showed that Au NPs and Pt NPs could be recovered intact with 66–95% efficiency in 6 min using 0.1 M NaOH and 800 W (except for clay-rich soil where the recovery was <10%). Although 75% of added Ag NPs were also successfully retrieved, they had decreased in size, indicating partial dissolution, whilst the Se NPs degraded almost entirely (recovery <2%). The authors highlighted that there is a trade-off to consider between extraction efficiency and NP preservation. Other workers studied²²⁰ naturally occurring Si NPs in soil. First, sp-SF-ICP-MS was used to optimise a NP extraction procedure (the double-focussing magnetic sector analyser was used to overcome polyatomic ion interference from isotopes of C, N and O). A solution of 40 mM Na₄P₂O₇ gave highest extraction efficiency, with less particle agglomeration than Ca(NO₃)₂, Mg(NO₃)₂ or BaCl₂. Then sp-TOF-ICP-MS was used to study the composition of the NPs. Over 46% were found to contained both Al and Si, and were therefore classified as aluminosilicates. Only Si was detected in almost 35% of cases, but this was considered an upper limit for SiO₂ NPs since secondary elements may have been present at concentrations too low to be detected.

In applications of sp-ICP-MS in plant analysis, a review²²¹ (83 references) on the use of the technique for the determination of inorganic NPs in food and food additives included studies on leafy vegetables, radish and edible seaweed. A 48 h multi-enzyme extraction procedure²²² was recommended to study concentrations and distribution of ZnO NPs – commonly applied to crops as a foliar spray – in the roots, leaves, stems and grains of rice. Recoveries were over 85%, and the size distribution of ZnO NPs was unaffected by the digestion procedure. The sp-ICP-MS variant single-cell ICP-MS was used²²³ to analyse individual pollen grains of Arabidopsis thaliana. The effects of soil amendment with iron and manganese were investigated.

4.4.4 Laser-induced breakdown spectroscopy. In sample preparation procedures for the determination of trace elements in soil, a ‘solid-phase conversion’ method²²⁴ gave more stable signals and repeatable results than either direct analysis or analysis of tabletted samples by reducing the influence of particle size. The procedure involved spiking a soil sample with up to 500 mg kg⁻¹ of either CrCl₃ or PbCl₃ (a somewhat unexpected oxidation state for Pb). Samples then underwent a “soil–liquid–solid” conversion by shaking in distilled water (2 g in 20 mL), followed by filtration through a nitrocellulose membrane and heating of the filter paper to 50 °C to create the LIBS target. The authors claimed that improvements were due to the sample “being dissolved in water and then adsorbed onto the surface of nitrocellulose filter membrane absorbent paper”. However, since soil dissolution requires much more vigorous treatment, e.g., exposure to hot mineral acid, a perhaps more likely explanation is that the Cr or Pb spike dissolved and then was re-sorbed onto particles comminuted by the shaking procedure, followed by deposition of the resulting fine soil slurry onto the filter.

Efforts have continued to enhance LIBS signal intensity and thereby improve sensitivity and LODs. One approach²²⁵ recommended (in Chinese with English abstract) for the determination of Cd in soil was addition of NaCl to improve coupling between the laser and the sample and thereby increase ablation yields. With 90% NaCl doping, LODs were improved from around 30 mg kg⁻¹ to <2.5 mg kg⁻¹. Magnetic field plasma confinement²²⁶ (optimal field strength 0.98 T) gave signal enhancement factors ranging from 2.4-fold for Ba to 2.8-fold for Cr. When combined with a grid search and cross validation quadratic optimisation network chemometric approach, the method was able successfully to classify soils from different mining areas in China. A dual-enhancement LIBS system²²⁷ that used both cylindrical cavity confinement and a N₂ atmosphere increased signal intensity more than 3-fold for the determination of Sr. The S/N increased to >700 and the LOD decreased to 34.6 mg kg⁻¹cf. <350 and 58.1 mg kg⁻¹ in air without confinement. Finally, a LIBS-LIF procedure²²⁸ for estimation of readily-soluble P increased signal intensity for the P I line at 213.6 nm up to 40-fold, relative to conventional LIBS, and improved selectivity by decreasing spectral overlap with the Fe I line at 213.85 nm. Different soil types required different calibration graphs. The LODs were 0.12 mg kg⁻¹ for clay soil and 0.27 mg kg⁻¹ for silt loam/loam.

A novel means to reduce the influence of variations in laser energy on spectral line intensity involved²²⁹ addition of a second optical path to the spectrometer for collection of polarised light from the plasma. Although the signals obtained with this “micro-linear spectrum model” were less intense than those of conventional LIBS, the correlations between found and target concentrations for a series of Cr-spiked soils were improved.

Noteworthy amongst other improvements in LIBS methods was a study²³⁰ that compared two laser wavelengths for the determination of C in air dried soil samples without pelletisation. Better performance (prediction error 2.7% and LOD 0.34–0.5 w/w %) was obtained at 532 nm than at 1064 nm. Also in the area of soil C measurement was a detailed evaluation²³¹ of the advantages and limitations of 13 univariate and 22 multivariate calibration strategies that provided useful guidelines for method selection. The critical influence of soil pellet compactness on the laser ablation process was demonstrated²³² for Cd and Pb using a portable LIBS instrument and a suite of 60 Chinese CRMs. More compact targets produced less (or no) dust and smaller ablation craters than less compact targets. To account for this, samples were divided into six compactness categories, a separate calibration curve was constructed for each, and then method was applied to >300 soil samples. Whilst many of the soils had Cd concentrations below the LIBS LOD of 3 mg kg⁻¹, comparisons between the values that could be obtained and results from ICP-MS were promising (r² values >0.95).

Further examples of LIBS methods for the analysis of soils, plants and related materials that involved comparison with results of other analytical methods, or their use in construction of LIBS calibration models, are provided in Tables 7 (soils and sediments) and 8 (plants).

Table 7 Methods for the LIBS analysis of soils and sediments

Analyte(s)	Matrix	Sample preparation	Comparison or validation	Ref.
Al, Ba, C, Ca, Co, Cr, Cu, Fe, Mg, Mn, Na, Sr, Si, Ti	Nile river sediments	Dried, ground and pressed into pellets; no binder	Qualitative analysis only	233
C	Agricultural soils	Dried, ground and pressed into pellets; no binder	Results compared with those obtained by dry combustion	234
C	Tropical and subtropical soils	Sieved (<2 mm) and pressed into pellets; no binder	Results compared with those obtained by dry combustion	235
Ca, Cu, Fe, K, Mg, Mn, N, P, Zn	Agricultural soils	Dried, ground and pressed into pellets; no binder	Results compared with those obtained by WDXRF	236
Cr	Yellow brown soil and lateritic red soil	Dried, ground and pressed into pellets; no binder	Results compared with those obtained by ICP-MS	237
Pb, Zn	Soils from contaminated areas	Fixed to adhesive tape	Results compared with those obtained by ICP-AES	238

---

Table 8 Methods for the LIBS analysis of plants

Analyte(s)	Matrix	Sample preparation	Comparison or validation	Ref.
Al, Ba, C, Ca, Fe, H, K, Li, Mg, Na, Si, Sr, and Ti	Saussurea simpsoniana	Dried, ground and pressed into pellets; no binder	CF-LIBS results compared to those obtained by EDX	239
Al, Cu, Mg, Pb, Zn	Lily bulbs	Dried, crushed, sieved and pressed into pellets; no binder	Samples analysed by ICP-MS	240
C, Ca, Cu, Fe, K, Li, Mg, Na, Pb, Si, Sr	Peganum harmala seeds	Dried, ground and pressed into pellets; no binder	CF-LIBS results compared to those obtained by EDX and XRFS techniques	241
Ca, Cu, Fe, K Mg, Mn and Na	Rice leaves	Dried, ground and pressed into pellets; no binder	Samples analysed by ICP-AES or ICP-MS	242
Ca, K, Mg	Soy leaf	Dried, ground and pressed into pellets; no binder	Samples analysed by ICP-AES	243
Extractable Ca, K, Mg, Na	Yerba mate	Dried, ground, sieved and pressed into pellets; poly(vinyl alcohol) binder	Samples analysed by LIBS before and after extraction with ultrapure water at 80 °C for 30 min. Results obtained by difference and compared with analysis of extracts by AAS	244

---

4.4.5 X-ray fluorescence spectrometry. In a review of advances in synchrotron-based applications, methods for the ex situ and in situ study of the biogeochemical movement of elements, were outlined by Smieska et al.²⁴⁵ (122 references). The need for a micro-focusing synchrotron beam-line covering 2D and 3D μXRF, as well as time- and spatially-resolved μXAS and μXRD, was emphasized, as were the benefits of facilities with additional imaging techniques such as IR and Raman spectrometries, and MS. Methods for the characterization of soils, minerals and organic material with XRF, XPS, XAS and XRD, and the benefits of mega-science facilities, were reviewed by Kirichkov et al.²⁴⁶ (116 references).

Advances in XRF instrumentation included a prototype spectrometer for underwater Hg determinations.²⁴⁷ The waterproof PVC casing housed a CdTe detector and ⁵⁷Co excitation source at 90°, since this arrangement was found to lower background noise compared to other geometries. Copper pieces inside the casing enabled submersion and lowering of the prototype to the ocean floor. Despite the relatively high LOD of 2880 ppm and the relatively short half-life for the ⁵⁷Co K shell excitation source of 272 days, the spectrometer has potential as an in situ screening tool for Hg in contaminated sites.

A novel XRF setup was designed²⁴⁸to overcome the limitations of small analysis chambers and plant sizes associated with in vivo plant analysis. The in-house ‘spectrometer for in vivo plant analysis’, or SIPA, consisted of a 4 W Ag X-ray tube and a silicon drift detector, mounted at 45° and 135°, respectively, from the sample surface. The confocal point was determined by a two-laser convergence system. The SIPA experimental chamber was appropriate for tomato and coffee plants with heights of about 15 and 30 cm, and could be operated remotely with its door open, allowing parts of the plant not being analysed to remain outside the equipment. Using tomato plants as a model species and Rb and Sr as physiological tracers for potassium and calcium respectively, transport of the foliar-applied minerals throughout the plant tissues was successfully monitored, making the SIPA a useful tool for the long-term in vivo study of mineral absorption and transport.

5 Analysis of geological materials

5.1 Reference materials and data quality

The ongoing development of new RMs for accurate microanalysis and dating, as well as further characterization of existing materials, in terms of their trace element and isotopic composition, was reviewed²⁴⁹ (22 references). The trends recognized were: obtaining more information on existing RMs, and the development of new materials for isotopic measurements and for in situ and microanalysis applications using techniques such as SIMS or LA-ICP-MS. Within the field of U–Pb dating, to make full use of the spatial capabilities of LA-ICP-MS, homogeneous RMs are required for external calibration so 10 columbite-tantalite minerals were evaluated²⁵⁰ as potential RMs but only four of them were determined to be suitable.

Instrumental advances can influence the suitability of a selected RM for an application. An in-house non-matrix-matched primary standard, Rutile TB-1, was initially proposed²⁵¹ as a non-matrix-matched standard for in situ U–Pb dating of ilmenite by LA-ICP-SF-MS but was found to be unsuitable as a primary standard, so use of RM Zircon 91500, which possesses a similar U–Pb fractionation and average normalized Pb–U ratio to ilmenite BC269, improved the accuracy of the determinations. There are currently a limited number of matrix-matched secondary RMs for U–Pb dating of geological samples and advances in the development of such materials are summarised in Table 9.

Table 9 New geological reference materials for isotope measurements

Determinands	Matrix	Technique	RM name	Comments	Reference
Major (Al, Ca, K, Na, and Si), trace elements, (Ba, Ce, Cr, Eu, Fe, Ga, La, Mg, Mn, Nd, Ni, P, Pr, Rb, Sr, Ti, and Zn) and Sr isotopes	Plagioclase	LA-ICP-MS	MGP-1	Natural plagioclase sample from Madagascar. MGP-1 homogeneous at the μm to mm scale for major (Al, Ca, K, Na, Si and RSD <4%), most trace (Ba, Ce, Cr, Eu, Fe, Ga, La, Mg, Mn, Nd, Ni, P, Pr, Rb, Sr, Ti, Zn, and RSD <15%) elements and ^87Sr/^86Sr ratio (2SD = 0.00013); verified by PMA, LA-ICP-MS and LA-MC-ICP-MS. Major and trace elements also analysed through XRF and solution ICP-MS with good agreement (10%) with the in situ performed measurements. ^87Sr/^86Sr mean ratio (TIMS): 0.703459 ± 0.000028 (2SD)	252
Al, Na, Si isotopes	Albite	LA-ICP-MS	Piz Beverin Albite (PB Ab)	The homogeneity of three natural K-feldspar samples was verified by EPMA (nine independent laboratories), variation in Al and Si content in the order of ±0.5% (within the EPMA error). Absolute Al, Na and Si contents verified by XRF and agreed with those for LA-ICP-MS. Three additional K-feldspar samples (yellow sanidine crystals from Itrongay, Madagascar) confirmed homogeneity at the cm and μm scale (EPMA)	253
B, Sr isotopes	Tourmaline megacrysts (borosilicate)	LA-MC-ICP-MS	GIGT, XJ-1 and XJ-3	RM homogeneity in B isotopic compositions at the tens of μm scale (demonstrated with LA-MC-ICP-MS). Average δ^11B: −12.63 ± 0.51‰ (2SD, n = 74) for GIGT, −11.90 ± 0.64‰ (2SD, n = 78) for XJ-1 and −11.91 ± 0.64‰ (2SD, n = 73) for XJ-3. Tourmaline IAEA RM B4 (Tourmaline) taken as external standard. Uncertainty 0.38‰. The values agreed with the results found in solution MC-ICP-MS. ^87Sr/^86Sr (for XJ-1) = 0.70827 ± 0.00021 (2SD, n = 176)	254
C isotopes	Graphite	LA-MS-ICP-MS	LSTM, CSY	Two in-house graphite RMs from the Lutang deposit in Hunan Province and the Changshouyuan deposit in Jiangxi Province, China. Carbon isotope composition relatively homogeneous (RSD uncorrected ^13C/^12C ratios: ∼0.030%). A slight matrix effect was observed between graphite and dolomite and a strong matrix effect existed between graphite and calcite. Agreement in δ^13C values from the mutual calibration of the two graphite samples in previous and current studies using IRMS	255
C and Mg isotopes	Dolomites	LA-MC-ICP-MS	GBW07217, DOL-8	δ ^26Mg and δ^25Mg: 0.11‰ and 0.09‰, respectively. The materials were homogeneous; δ^13C, δ^25Mg and δ^25Mg = 0.37‰, 0.11‰ and 0.06‰ (2SD, n = 15), respectively	256
Nine isotopes (Co, Cr, FeO, Mg, Nb Sc, TiO2, V)	Ilmenite	LA-ICP-MS	LY	The ilmenite sample was homogeneous with respect to Cr, FeO, Mg, Sc, TiO2, V, whilst Co and Nb were unevenly distributed	257
Cu, Fe, and S, isotopes	Chalcopyrite, pyrrhotite, sphalerite	IRMS (S) solution analysis MC-ICP-MS (Fe)	Ll-Cpy, Ll-Po, Ll-Sp	Bulk S isotopic compositions determined by 7 independent laboratories (IRMS)	258
				Preferred δ^34SV-CDT for Ll-Cpy, Ll-Po, Ll-Sp: 6.13 ± 0.37‰ (2SD), 6.42 ± 0.37‰ (2SD) and 6.28 ± 0.38‰ (2SD), respectively. Recommended δ^56FeIRMM-014 for Ll-Cpy and Ll-Po (MC-ICP-MS): 0.57 ± 0.07‰ (2SD) and −0.62 ± 0.07‰ (2SD), respectively. Determined δ^65CuNIST SRM 976 for Ll-Cpy (MC-ICP-MS): 0.57 ± 0.06‰ (2SD). Data in agreement with LA-MC-ICP-MS and homogeneity of materials was verified at the 30–40 mm scale by LA-MC-ICP-MS
Fe isotopes	Olivine, garnet, amphibole, biotite	SIMS	YY11-12, YS-42, YS-37, YS-20	Proposed as matrix-matched in-house standards for SIMS Fe isotopic analysis. Homogeneous for Fe isotopic compositions. External reproducibility for YY11-12 olivine, YS-42 garnet, YS-37 amphibole and YS-20 biotite: 0.23‰, 0.28‰, 0.23‰ and 0.25‰ for δ^56Fe values (2SD), respectively	259
Fe and S isotopes	Pyrite	fs-LA-MC-ICP-MS	IGGPy-1	Pyrite grains were homogeneous in terms of major elements as confirmed by EPMA. fs-LA-MC-ICP-MS: variances in δ^56Fe and δ^34S of 0.07‰ (2SD, n = 105) and 0.13‰ (2SD, n = 105), respectively. δ^34SVCDT (EA-IRMS): 17.09 ± 0.30‰ (2SD, n = 6); δ^56FeIRMM-014 and δ^57FeIRMM-014 (solution-nebulization MC-ICP-MS): −1.31 ± 0.06‰ (2SD, n = 21) and −1.94 ± 0.12‰ (2SD, n = 21). Fe, S and Co contents (wt%): 46.25 ± 0.13 (1SD), 52.03 ± 0.26 (1SD) and 1.47 ± 0.1 (1SD), respectively	260
^4He dating	Detrital Pt-alloy grains	Magnetic sector mass spectrometry	RS-Pt	Native platinum grains from placer deposits in the Santiago river, Ecuador, analysed by two independent centres, yielded constant ^4He contents (n = 17). ^4He concentration: 215 ± 4 × 10^11 at g^−1 (2SD); mean ^190Pt–^4He age: 39.6 ± 0.7 Ma (2SD)	261
Hf/W dating	Zircon	NanoSIMS	Std-A Std-B	The RMs were applicable to the analysis of mesosiderite (a meteorite). Hafnium oxide, tungsten oxide and high-purity zircon powder were subjected to high temperature (1000 °C) and pressure (6 GPa). Hf/W ratios homogeneity was tested by SEM-EDS, EPMA and LA-ICP-MS. Hf/W ratios measured by NanoSIMS. Relative sensitivity factor (RSF) of Hf and W: 0.585 ± 0.180. Absolute zircon Hf–W age: 4536.5−7.2^+4.6 Ma	262
Nd–Sm isotopes	Vesuvianite	LA-MC-ICP-MS	M6635, M784, M1377, M1450, M659	In-house vesuvianite standards; external correction for Nd and Sr isotopes in situ determinations. Eleven samples were analysed from different locations. Sr and Sm–Nd isotopic compositions were consistent with those obtained by solution nebulization MC-ICP-MS. M659 recommended for in situ Sm–Nd isotope analysis (low Sr content, ∼20 μg g^−1; ^147Sm/^144Nd low variations, RSD ∼ 9.5%; and ^143Nd/^144Nd, 2SD: ∼0.00005). M6635, M784, M1377, and M1450 recommended for in situ Sr isotope analysis, with high Sr (>1000 μg g^−1) and low Rb (<0.5 μg g^−1) contents and relatively homogeneous ^87Sr/^86Sr ratios (2SD <0.0001)	263
O isotopes	Corundum	SIMS	HD-LR1	Synthetic laser ruby crystal as a new matrix-matched RM for SIMS. LF-IRMS averaging δ^18OV-SMOW = +18.40 ± 0.14‰ (95% confidence interval, n = 23). Material was homogeneous at the mg scale, as verified by LF-IRMS, and O-isotopic values at the ng scale. EPMA revealed that only Cr impurities were found and HD-LR1 could be used for Cr determination	264
O isotopes	Chromitites, harzburgite	SIMS		SIMS δ^18O measurements confirmed the homogeneity of eight chromite samples with a range of Al, Cr, Mg and Fe contents. Magnetite and spinel affected the SHRIMP instrumental mass fractionation, IMF. The most important variable being the proportion of magnetite in the chromite sample (IMF > 4%)	265
O isotopes	Barite	SIMS	NJU-Ba-1 FJ barite	Recommended δ^18OVSMOW of NJU-Ba-1 and FJ determined by GS-IRMS: 7.94 ± 0.30‰ (2SD, n = 11) and 7.52 ± 0.34‰ (2SD, n = 8), respectively. SIMS analyses proved homogeneity of both NJU-Ba-1 and FJ barite at the ∼20 μm level, (2SD): 0.36‰ (n = 383) and 0.58‰ (n = 325), respectively	266
O isotopes	Cassiterite	SIMS	Piaotang-Cst	SIMS measurements confirmed the homogeneity of this RM at the micrometer scale. Average 2SD: 0.49‰ (n = 626). δ^18OVSMOW: 5.33 ± 0.07‰ (2SD, n = 5), determined by IRMS with no matrix effects for cassiterite samples having variable concentrations of trace elements	267
O and S isotopes	Barite anhidrite gypsum	SIMS	Barite: ROM-13898 ROM-48628 ROM-48628 anhydrite: ROM-13491 ROM-40347 gypsum: ROM-16655 ROM-37603	Three barite, two anhydrite, and two gypsum samples were selected from the Royal Ontario Museum. SIMS repeatabilities better than ±0.39‰ and ±0.37‰ (1SD) for oxygen and sulfur isotope ratios, respectively. GS-IRMS analyses in independent laboratories gave narrow variation in δ^18OSMOW, although higher in δ^34SVCDT. GS-IRMS analysis provided good reproducibility within laboratories: 10^3δ^18O values between ±0.24‰ and ±0.44‰ (1SD) and 10^3δ^34S values ranging from ±0.07‰ to ±0.99‰ (1SD)	268
Os–Re dating	Molybdenite	LA-ICP-MS/MS	MDQ0252 and MDQ0221	Two RMs from the Merlin deposit in Queensland and Q-MolyHill from the Preissac pluton in Quebec. Spectral interference of ^187Re on ^187Os resolved by using CH4 as reaction gas. Homogenous Re–Os isotope ratios. ^187Os/^187Re ratio (ID-TIMS): 0.025649 ± 0.000105. Re mass fraction 65–5100 μg g^−1 (MDC0252) and 60–1300 μg g^−1 (MDC0221). Ages verified by ID-TIMS Re–Os measurements: 1520 ± 4 Ma for MDQ0252. LA-ICP-MS/MS ages: 1518 ± 4 Ma for MDQ0252 and 1516 ± 6 Ma for MDQ0221	269
REEs	Scheelite	LA-ICP-MS	CaW-0, CaW-1, CaW-3	CaWO4 homogeneous crystals were doped with REEs at 50, 250 and 5000 μg g^−1. REEs within and between unit major elements variation (sr) for LA-ICP-MS: 0.53 and 5.56%, respectively. Preliminary mass fractions determined by ICP-MS: from 15.5 ± 0.8 (Lu) to 36.2 ± 1.7 (Gd) μg g^−1 in CaW-0; from 49 ± 10 (Lu) to 209 ± 20 (Eu) μg g^−1 in CaW-1; and from 906 ± 29 (Lu) to 2851 ± 136 (Gd) μg g^−1 in CaW-3. Errors taken as 1SD	270
S isotopes	Chalcopyrite	SIMS	IGSD	Large grains of natural chalcopyrite were taken, and the homogeneity of major elements was tested by EPMA and four laboratories with SIMS. An LA-MC-ICP-MS confirmed homogeneity for S isotopes. Recommended δ^34S based on IRMS: 4.21 ± 0.23‰ (2SD, n = 30)	271
S isotopes	Stibnite	LA-MC-ICP-MS	Bj-Snt	Recommended δ^34S (determined by IRMS) was −0.71 ± 0.32‰ (2SD, n = 15). The material contained Sb and S as major elements, and hundreds to thousands of mg kg^−1 of trace elements such as As, Cu, Pb and Si	272
S isotopes	Barite	LA-MC-ICP-MS	NWU-Brt	Barite crystals from the Xiefang fluorite ore, Ruijin, Jiangxi province, China. The material was sufficiently texturally and chemically homogeneous; analytical uncertainty of 0.40‰ for δ^34S (2SD). Recommended mean δ^34S value obtained by bulk isotope analyses by GS-IRMS and MC-ICP-MS: 14.21 ± 0.33‰ (2SD)	273
Sb isotopes	Stibnite	fs-LA-MC-ICP-MS	DC, BJS	There were no matrix effects between the two samples. Homogeneity of RMs tested through MC-ICP-MS and backscattered electron maps (BSE). Sn was used as an internal standard to correct for isotopic fractionation. The δ^123Sb precision was 0.02‰ and 0.04‰ (2SD) within 1 and 3 h, respectively. fs-LA-MC-ICP-MS ^123Sb/^121Sb ratios: DC; 0.04 ± 0.07‰ (2SD, n = 62) and BJS; 0.39 ± 0.06‰ (2SD, n = 62), consistent with solution nebulization MC-ICP-MS results	274
Sr isotopes	Calcite	LA-MC-ICP-MS	TLM LSJ07	m-XRF images indicate they are roughly homogeneous with respect to Ca, Mg, and Sr, (except for Sr in LSJ07). Rb/Sr ratios <0.0002 and low Sr contents (<200 mg g^−1). For independent grains, ^87Sr/^86Sr: 0.70969 ± 0.00023 (2SD, n = 219) and 0.71006 ± 0.00015 (2SD, n = 219) for TLM and LSJ07, respectively. In agreement with data for TIMS and MC-ICP-MS	275
Sr isotopes	Plagioclases	LA-MC-ICP-MS	AMNH-107160, G29958, Hrappsey 14-2	Most accurate and precise ^87Sr/^86Sr were obtained by a combination of ^84Kr-baseline subtraction, Rb-peak-stripping using βRb derived from a bracketing glass RM, CaCa or CaAr correction for plagioclase and ^84(CaCa or CaAr) + REE^2+ correction for rock glasses. ^87Sr/^86Sr uncertainties better than 100 ppm (2SD) with a 100 μm laser spot size and materials with Sr mass fractions from 500 to 1000 μg g^−1. ^87Sr/^86Sr ± 2SD (solution MC-ICP-MS): 0.704371 ± 24 (n = 9) AMNH-107160; 0.707551 ± 22 (n = 7) G29958; 0.703168 ± 18 (n = 8) Hrappsey 14-2. ^87Sr/^86Sr ± 2SD (TIMS): 0.704365 ± 15 (n = 2) AMNH-107160; 0.707547 ± 15 (n = 1) G29958	276
Ti isotopes	Ilmenite, titanite, perovskites	LA-ICP-MS	Ilmenites SIN1, CZE2, PAK122 and RUS10 Titanites PAK8, AUS10, NAM12 and MAD8 Perovskites ITA4, RUS5, RUS1 and RUS2	Twelve Ti-rich minerals from Singapore, the Czech Republic, Pakistan, Russia, Australia, Namibia, USA and Italy. Isotopic composition was determined through SSB and fs-LA-MC-ICP-MS with identical δ^49TiOL–Ti values for each mineral. The natural samples were mostly homogeneous in terms of Ti isotopic composition. Intermediate precision: ±0.13‰ to ±0.17‰ (2SD) on δ^49TiOL–Ti	277
Sr isotopic composition and trace elements (Mn, Sr, REE, and Y)	Calcite	LA-MC-ICP-MS	BZS, WS-1	Homogeneity of Sr isotopes and trace elements (Mn, Sr, Y, RSD <10%, and REEs for BZS, RSD <20%) verified through LA-MC-ICP-MS. Recommended ^87Sr/^8686 values: 0.71181 ± 0.00013 (2SD, n = 183) and 0.70872 ± 0.00009 (2SD, n = 184), for BZS and WS-1, obtained by solution MC-ICP-MS	278
U–Pb dating and trace elements	Apatite	SIMS	BR2, BR5, BR96, AFG2, AFB1, MADAP, SLAP, GR40, OL2, OL3, OL4, NUAN	Twelve apatite samples examined as secondary RMs. Homogeneity of SLAP, NUAN, GR40 demonstrated with LA-ICP-MS in terms of trace elements. Homogeneous SIMS U–Pb data. ID-TIMS ages: 2053.83 ± 0.21 Ma, 2040.34 ± 0.09 Ma, 868.87 ± 0.25 Ma, 478.71 ± 0.22 Ma and 473.25 ± 0.09 Ma, respectively. Eight of the RMs had homogeneous S isotopic composition (2SD for 10^3δ^34S and 10^3δ^33S <0.55‰). BR96 and SLAP useful as RMs for sulfur isotope microanalysis of apatite. AFG2 and BR5: best suited for U–Pb isotope measurement by SIMS. ABF1: high Th content, useful as RM for SIMS Th–Pb apatite dating. ID-TIMS ^87Sr/^86Sr between 0.704214 ± 0.000030 and 0.723134 ± 0.000035. MADAP and OL3: best approaches for Sr isotope analysis	279
U–Pb dating	Andracite-rich garnet	LA-ICP-MS	IUC-1	Four different laboratories for LA-ICP-MS analysis, one for ID-TIMS and EPMA measurements, demonstrated homogeneity of the material IUC-1 from the Miocene trachyte dome near Ankara city (Turkey). This RM contained relatively high U levels (57 mg kg^−1). ID-TIMS ^207Pb/^235U and ^206Pb/^238U ages: 20.9 ± 0.4 and 20.6 ± 0.8 Ma, respectively, consistent with U–Pb LA-ICP-MS analyses	280
U–Pb dating	Apatite	LA-ICP-MS	MAP-2	Young apatite RM collected from Myanmar (22 Ma). Chemically and isotopically homogeneous material, free from inclusions. U concentrations: 161–231 mg g^−1. ID-TIMS isotopic ratios: ^206Pb/^238U = 0.00345 ± 0.00003, ^207Pb/^235U = 0.0243 ± 0.0039, ^207Pb/^206Pb = 0.0512 ± 0.0082. ID-TIMS ^206Pb/^238U weighed ages: 22.18 ± 0.22 Ma (2SD, MSWD = 0.01, n = 6). Results obtained with LA-ICP-MS were consistent with those measured by ID-TIMS	281
U–Pb dating	Rutile	LA-HR-ICP-MS ID-TIMS SIMS	RKV01	RKV01 grains from Kaap Valley pluton, South Africa. Major and trace elemental concentration was obtained by means of EPMA and LA-HR-ICP-MS, respectively. The material contained high U (∼83 μg g^−1), extremely low Th (∼0.003 μg g^−1) mass fractions and U–Pb homogeneous isotopic composition with no compositional zoning. Recommended ID-TIMS mean ^207Pb/^206Pb age: 3225.61 ± 0.64 Ma (2sr, MSWD = 0.83), consistent with LA-ICP-MS and SIMS determined ages	282
U–Pb dating	Calcite	LA-ICP-MS	TARIM	Natural low Mg megacryst calcite RM. Homogeneity for trace elements and U was tested through EPMA and LA-ICP-MS. ID-TIMS U–Pb analysis provided an intercept age of 208.5 ± 0.6 Ma (2SD, MSWD = 1.04). LA-ICP-MS U–Pb showed ages of 208.0 ± 0.4/3.2 Ma (2SD, MSWD = 3.0), 515 isotope analyses	283
U–Pb dating and Lu–Hf isotopes	Zircon megacrystals	LA-ICP-MS	OZ	Zircons from the Kawisigamuwa carbonatite in Sri Lanka with moderate to low radiation damage. ID-TIMS weighted mean ^206Pb/^238U age: 532.39 ± 0.66 Ma (2SD). Hf content ranged from 6630 to 9960 mg g^−1, while the in situ mean ^176Hf/^177Hf ratios of ten crystals agreed within the error bands. Recommended ^176Hf/^177Hf ratio: 0.282003 ± 0.000020 (2SD). Homogeneity of the material, verified by LA-ICP-MS. Two oscillatory zoned megacrysts yielded δ^18OVSMOW of 12.1 ± 0.4‰ and 12.2 ± 0.4‰ (2SD)	284
U–Th and U–Pb double dating	Zircon	ID-TIMS	SA01	(U–Th)/He ages for three different fragments: 350.4 ± 18.8 Ma (n = 5, MSWD = 0.66), 369.6 ± 11.4 Ma (n = 12, MSWD = 0.60), and 368.6 ± 14.4 Ma (n = 7, MSWD = 0.59). Ages were consistent with traditional single-crystal laser fusion He determination and U–Th isotope dilution. No evidence of fragment zoning was reported, and the material proved to be homogeneous at the 100–500 μm scale as revealed by cathodoluminescence, Raman spectroscopy, LA-ICP-MS and conventional chemical methods. Raman spectrometry analysis suggested that the material experienced a low degree of radiation damage	285
U isotopes	Glass	LA-ICP-MS	CAS glasses SAC glasses	Six different RMs for spatially resolved nuclear fallout analyses prepared by melting and quenching of SiO2, Al2O3 and CaCO3 powders. Materials with three different ^235U isotope enrichment (natural or 0.72%, 53% and 94%) and three U mass fractions: 5 μg g^−1, 50 μg g^−1 and 500 μg g^−1. Heterogeneous bulk elemental data was detected with a 10% RSD	286
U–Th–Pb dating	Xenotime	LA-ICP-MS	XN01	Material with similar ^206Pb/^238U and ^208Pb/^232Th ratios and calibration factors to bastnaesite (K-9) proposed as primary standard for bastnaesite dating. Ages for both materials: 118 ± 1 Ma with <2% offsets. Single-spot LA-ICP-MS analysis data reduction was performed through calculation of the ratio-of-the-mean intensity. XN01 was more homogeneous than K-9 and useful as primary RM for in situ LA-ICP-MS U–Th and Th–Pb bastnaesite dating	287

---

Whilst the advent of new analytical instrument variants has facilitated the isotopic analysis of sulfur, there is a requirement for new RMs and a natural stibnite, BJ-Snt, was proposed²⁷² as a potential candidate. Homogeneity of the material was verified by BSE mapping, and determination of the mineral phases and elemental compositions undertaken. Further verifications were conducted by comparing isotope values obtained by LA-MC-ICP-MS with those obtained using IRMS, as well as undertaking an intercomparison with two external laboratories.

A novel synthetic laser ruby crystal RM (HD-LR1) was proposed for the matrix-matched SIMS analysis²⁶⁴ of oxygen isotopes in corundum. Homogeneity testing of the material at the mg scale was undertaken using laser fluorination isotope ratio mass spectrometry (LF-IRMS) whereas homogeneous O-isotopic values at the ng-scale were determined using SIMS. The authors reported that the topography of the sample surface had to be considered to avoid δ¹⁸O bias at grain boundary edges. The proposed material was a chemically pure crystal with trace Cr concentrations at 276 μg g⁻¹ with Be concentrations <0.002 μg g⁻¹ so for this reason its use as a blank material for SIMS or LA-ICP-MS analysis of corundum was also proposed.

Suitable sample preparatory steps in the development of matrix matched RMs need to be considered. A sintering process used²⁵⁶ in the production of dolomite RMs for C and Mg isotopic microanalysis by LA-MC-ICP-MS demonstrated negligible isotope fractionation with respect to natural dolomites. Development²⁷³ of a matrix-matched RM with S isotope ratios similar to those of natural barites was possible using a fast hot-pressing sintering method. The resultant δ³⁴S data for the material obtained by IRMS, solution nebulization MC-ICP-MS and LA-ICP-MS assays performed in six independent laboratories were comparable. A new method for preparing²⁸⁸ a magnetite RM based on sintering at 500 °C at 1.2 GPa yielded a material with an Fe content and isotopic distribution that were homogeneous.

A process for certifying geological RMs was clearly established in the ISO Guide 35:2017 that recognizes a role here for proficiency testing. Within the GeoPT scheme, use of this certification protocol was successfully applied²⁸⁹ to the certification of nine major elements and 39 trace elements in a new candidate CRM IAG GMN-1 (Meissen Granite). Procedures based on those described within ISO Guide 35:2006 and JJF 1343-2012 were used in the development and preparation²⁹⁰ of new synthetic phosphate RMs for LA-ICP-MS. The candidate materials were prepared by co-precipitation of a series of analytes within a hydroxyapatite matrix for use in the multielemental analysis of apatite, monazite or other phosphates.

Additional certification of existing RMs is reported in Table 10. Ebihara et al.³⁰⁵ determined the mass fraction of Br, Cl and I in 17 USGS geochemical RMs by radiochemical NAA, three RMs were also analysed by INAA and the results were within 4% of each other. Whilst the new data for Br and Cl concentrations did not differ from the previously published values by the same authors, new data for I were 1/4 to 1/8 lower and so the authors concluded that their former determinations were biased.

Table 10 New data for existing geological reference materials

Determinand	Matrix	Technique	Comments	Reference
Ag isotopes	Basalt, granite, andesite, rhyolite, and ferromanganese crusts	MC-ICP-MS	Seven USGS RMs: BCR-2 and BIR-1 (basalt) GSP-2 and G-2 (granite), AGV-2 (andesite), RGM-1 (rhyolite) and NOD-P-1 (ferromanganese crusts), were analysed. Ag consumption lowered down to 5 ng. Instrumental mass bias corrected using the Pd-doping and SSB. Samples analysed with a precision better than 0.05‰. δ^109AgSRM978a for silicate rocks: from −0.24 ± 0.05‰ to 0.20 ± 0.05‰, and uncertainty for granite G-2: (2SD, 0.08‰)	291
B isotopes	Basalt	MC-ICP-MS	New data for Mid Ocean Ridge Basalt (MORB) glass were obtained: δ^11BSRM 951 (−5.6 ± 0.3‰ to −8.8 ± 0.5‰, 2SD), implying some upper mantle δ^11BSRM 951 heterogeneity. The results were fully in agreement with SIMS assays, although for low mass fractions MC-ICP-MS provided better precisions	292
Ce isotopes	Manganese nodules, diabase, granite gneiss, stream sediment	MC-ICP-MS	Analysis according to a sample-standard combined with a Sm-doping method done by two different laboratories. δ^142Ce, USGS RM NOD-P-1 (manganese nodule): 0.177 ± 0.035‰ (2SD, n = 7) and 0.172 ± 0.034‰ (2SD, n = 8) for the two laboratories; USGS RM NOD-A-1 (manganese nodule): 0.116 ± 0.028‰ (2SD, n = 8) and 0.104 ± 0.028‰ (2SD, n = 9), respectively. The δ^142Ce of two USGS RMs W-2a (diabase) was – 0.036 ± 0.046‰ (2SD, n = 3), USGS RMs BHVO-2 (basalt) was – 0.019 ± 0.036‰ (2SD, n = 4). Ce isotopic compositions of three Chinese RMs GBW07121 (granite gneiss), GBW07123, and stream sediment GBW07301a were: 0.039 ± 0.033‰ (2SD, n = 5), 0.068 ± 0.027‰ (2SD, n = 5) and 0.046 ± 0.015‰ (2SD, n = 3), respectively	293
Hf and Lu isotopes	Mafic and ultra-mafic rocks	ID-MC-ICP-MS	Hf and Lu mass fractions and ^176Hf/^177Hf isotopic ratios were measured in ten mafic-ultramafic rock RMs, including IAG RM OKUM (komatiite), CCRMP RM WPR-1 (serpentinised peridotite), MINTEK RM NIM-N (norite), MINTEK RM NIM-P (pyroxenite), ANRT RM UB-N (serpentinised peridotite), GSJ RM JP-1 (peridotite), MINTEK RM NIM-D (dunite), IAG RM MUH-1 (serpentinised harzburgite), IAG RM HARZ01 (harzburgite), USGS RM DTS-2b (dunite), with low contents of Lu and Hf (Lu, 2–150 ng g-1; hf, 5–500 ng g^−1). Variability in Hf mass fraction and isotopic composition were observed for: NIM-N, NIM-P and MUH-1 at test portion masses of 0.1 to 0.3 g. for the remaining materials, precision was lower than 60 ppm (2SD) for test portions of 0.06 to 0.3 g	294
Mg-isotopes	Basalt, granodiroite , andesite, carbonate, sediments, soil, rhyolite, amphibolite, limestone, clay, sandstone, shale, syenite, gabbro	MC-ICP-MS	The RMs studied were: six USGS RMs: (BHVO-2 and BCR-2, basalts), RGM-2 (rhyolite), AGV-2 (andesite), GSP-2 (granodiorite), COQ-1 (carbonate), two rhyolite geological survey of Japan RMs (JR-1 and JR-3), sixteen RMs from CRSRI (GSS-2, soil, GRS-11, rhyolite, GSR-15, amphibolite). For these RMs, δ^26Mg values (2SD, n = 3) agreed with previously published ones. δ^26Mg values for GSR-6, limestone, GSD-1, GSD-3A, GSD-4, GSD-5, sediments, GSS-1, soil, GSS9, sediment, GSS-15, clay, GSS-16, clay, GSR-4, sandstone, GSR-5, shale, GSR-7, syenite, and GSR-10, gabbro, were reported to be (2SD, n = 3): −1.42 ± 0.02‰; −1.11 ± 0.12‰; −0.08 ± 0.06‰; −0.12 ± 0.04‰; −0.24 ± 0.06‰; −0.71 ± 0.12‰; −0.54 ± 0.07‰; −0.40 ± 0.11‰; −0.06 ± 0.01‰; −0.07 ± 0.09‰; 0.21 ± 0.08‰; −0.50 ± 0.03‰; −0.33 ± 0.08‰, respectively	295
Nd, Rb, Sm, and Sr elemental concentrations and Sr and Nd isotopic ratios	Silicates	TIMS, MC-ICP-MS	13 Chinese silicate rock certified RMs were analysed. Test portions were spiked with tracers enriched in ^87Rb–^84Sr and ^149Sm–^145,146,150Nd and digested with HF, HNO3, and HClO4. For GBW07111, GBW07112, GBW07121, GBW07122, GBW07123, GBW07124 and GBW07125, the Nd and Sr isotopic compositions were reported for the first time	296
Nd, Sm, Sr isotope ratios	Basalt	ID-TIMS	Instrumental bias and fractionation corrections were performed based on repeated measurements of the certified RMs JNdi-1 (Neodymium oxide) and NIST SRM 987 (Strontium carbonate). RM Basalt Ribeirao Preto BRP-1: ^143Nd/^144Nd = 0.512408 ± 0.000010 (2SD); ^87Sr/^86Sr = 0.706011 ± 0.000017 (2SD). Respective Sm and Nd mass fractions: 11.0 ± 0.2 and 51.9 ± 0.6 μg g^−1. Accuracy of the methods tested with the following USGS RMs: AGV-1, BCR-1, BHVO-1, G-2 and GSP-1	297
Rb–Sr dating	Mica	LA-ICP-MS/MS	ZBH-25 biotite, a Chinese national primary RM (GBW04439) for K–Ar dating, was characterized for aging according to the Rb–Sr dating method. A younger age was determined with respect to the K–Ar age when using synthetic glass RM (NIST SRM 610) as the RM for external calibration (i.e., there was a significant matrix effect). ^87Sr/^86Sr ratios were 0.7100 ± 0.0020 (2SD, n = 15), in agreement with the recommended value	298
U isotopes	Coal	MC-ICP-MS	Samples were dry ashed and high-pressure digested; instrument isotopic fractionation was calibrated through ^233U–^236U double-spike. δ^238UCRM-145 of seven USGS coal RMs (SARM18, SARM1, SARM20, GBW11156, GBW11157, GBW11159, GBW11160) and a NIST SRM 1633c (fly ash) values ranged from −0.69 ± 0.04‰ (2SD) to – 0.29 ± 0.03‰ (2SD)	299
U isotopes	28 igneous, metamorphic and sedimentary rocks and sediments RMs	MC-ICP-MS	The ^233U–^236U double spike technique was used. U mass fractions ranged from 0.11 to 18.7 μg g^−1. δ^234/^238U values were provided for all RMs for the first time	300
U–Pb dating	Ilmenite	LA-SF-ICP-MS	Zircon 91500 and garnet PL-57 had similar U–Pb fractionation and average normalized Pb/U ratio with ilmenite BC269. Ilmenite samples HG79 (^206Pb/^238U age of 259.3 ± 1.3 Ma), LX32333 (^206Pb/^238U age ∼196.0 ± 2.7 Ma), and BC269 (2055.0 ± 3.9 Ma) contained appropriate U and Pb contents and were useful as potential RMs for in situ U–Pb dating of ilmenite	251
U–Pb dating	Apatite	Fission-track dating	McClure Mountain Syenite (MMS) apatite recommended as a RM for LA-ICP-MS fission track and U–Pb double dating. Overall central age: 254.1 ± 5.1 Ma (n = 238) in analyses over two years	301
			The MMS apatite is widely used as a secondary RM for U–Pb dating. It was also proposed as a secondary material for trace element as well as fission track assays
U–Pb isotopes and trace elements	Three titanite geochronology RMs (Ecstall, McClure and FCT)	LA-ICP-MS	EPMA results revealed intragrain heterogeneity of titanite materials; RSD for trace elements from 5 to 40%. For U, RSD = 70% (n = 26), 265% (n = 22) and 202% (n = 26) for Ecstall McClure and FTC, respectively meaning these RMs were not suitable for trace element analysis	302
U, Th and Pb dating	Titanite and apatite	LA-ICP-MS ID-MC-ICP-MS	Ten titanite (MKED-1, BLR-1, OLT-1, YQ82, Khan titanite, Mud tank titanite; in-house: ONT-A, ONT-B, ONT-C and EPMA titanite RM Renfrew) and five apatite (MAD-UCSB, MRC-1, BRZ-1, Tory Hill apatite; in house: ONTAPA) RMs were analysed. A good concordance with the results from EPMA in the MKED-1 titanite RM ^238U/^206Pb, ^235U/^207Pb and ^232Th/^208Pb ages was found. Spectral overlapping made it necessary to perform a careful selection of EPMA primary RMs to minimise inaccuracies	303
Zn isotopes	Dolerite, gabbro, microgabbro, andesite, diorite, syenite, granite, rhyolite, shale, limestone, and hornblendite	MC-ICP-MS	The RMs analysed in this study included the following USGS RMs: GSP-2 (granodiorite), AGV-2 (andesite), W-2 (diabase), BCR-2 (basalt), BHVO-2 (basalt), BIR-1 (basalt), DNC-1 (dolerite) and NOD-P-1 (manganese nodule); geological society of Japan, GSJ RM JB-1b (basalt); CRPG RMs: PM-S (microgabbro) and DR-N (diorite); CNRCG RMs: GBW07103 (GSR-1, granite), GBW07104 (GSR-2, andesite), GBW07105 (GSR-3, basalt), GBW07107 (GSR-5, shale), GBW07108 (GSR-6, limestone), GBW07109 (GSR-7, syenite), GBW07111 (GSR-9, diorite), GBW07112 (GSR-10, gabbro), GBW07113 (GSR-11, rhyolite), GBW07122 (GSR-15, hornblendite). Long-term reproducibility better than 0.03‰ (2SD). New δ^66Zn values for eleven CRMs were provided whereas, for four RMs (basalt JB-1b and GSR-3, diabase W-2 and dolerite DNC-1) an improved characterisation of δ^66Zn was performed	304
Zn isotopes	Ultramafic, felsic igneous rocks, carbonatites, sediments soils	MC-ICP-MS	CRMs from USGS, the Geological Survey of Japan, and the Institute of Geophysical and Geochemical Exploration, People’s Republic of China (peridotite, JP-1, basalt, BCR-2, BHVO-2 and BIR-1a, andesite, AGV-2, rhyolite, GSR-11, granite, GSR-1, JG-1, granodiorite, GSP-2, carbonatite, COQ-1, soil, GSS-11, GSS-13, and sediment, GSS-33). Long-term external reproducibility for δ^66Zn values: ±0.04‰ (2SD)	158

---

Sometimes, newly reported values for RMs disagree with previously established ones. One example was the heterogeneity encountered in microscale δ⁵⁶Fe analyses of aliquots of Balmat pyrite RM, a material previously thought to be homogeneous. Pasquier et al.³⁰⁶ combined δ⁵⁶Fe data obtained by solution-based MC-ICP-MS and in situ SIMS assays of Balmat-UNIL and Balmat-Original and found that these two populations had different δ⁵⁶Fe values of −0.39 ± 0.18‰ and −1.46 ± 0.25‰ despite having similar petrological and chemical characteristics.

Gallium is a moderately volatile element, used in tracing geochemical evaporation–condensation processes but reported δ⁷¹Ga values in geological RMs can vary, highlighting the importance of appropriate sample preparation protocols for volatile species. A reported purification method based on the use of an anion AG1-X8 and a cation AG50W-X8 column avoided³⁰⁷ isotope fractionation and enabled δ⁷¹Ga values for three USGS RMs to be obtained.

In an instructive guide for geologists, recommendations³⁰⁸ for the standardised reporting of ID-TIMS U–Pb geochronological data and associated metadata were presented so as to maximize the long-term utility and comparability of published results. The inclusion of all contextual information was recommended so as to allow the correct reproduction of experiments and data manipulations to be performed by others. Additional suggestions were rigorous separation of the radioisotopic results from age interpretation (and the inclusion of clear explanations of the inherent hypothesis) and the provision of information for preferred interpretations, e.g., age. Measurement precision is continuously improving, so the publication of data in a standardised format was advocated to more readily identify potential biases and the adoption of AI techniques for data-mining recommended.

5.2 Sample preparation, dissolution and chemical separation

Developments in digestion and fusion procedures were reported. A rapid (30 min) standardised sample digestion³⁰⁹ performed at high temperature (250 °C) and pressure (80 bar) using a SCR microwave system enabled the complete dissolution of the geological RMs (ANRT CRM GP-13, spinel lherzolite, ANRT CRM UB-N, serpentinite, and USGS SRM BHVO-2, basalt) to be performed. The subsequent digests were purified by means of an ion-exchange column and the results of determinations carried out by ID-HR-ICP-MS agreed with those previously reported. In an improved alkaline fusion procedure, undertaken in silver crucibles at 710 °C, use³¹⁰ of hydrated Na₂O₂ as a fusion reagent resulted in the complete dissolution of eight international silicate standards and where the uncertainty associated with blank subtraction was reduced by the lower reagent consumption required for dissolution of the fusion cake. An in-house built³¹¹ induction furnace enabled rapid lithium metaborate fusions in graphite crucibles to be performed (5–10 min) on five powdered USGS silicate rock RMs.

Implementation of suitable sample surface treatments was critical for U–Pb dating purposes. Annealing at 1400 °C was proposed³¹² to minimise morphological damage caused by alpha particles with resultant homogeneity in U–Pb dating confirmed by LA-ICP-MS and SIMS assays. An alternative chemical abrasion surface treatment method combined³¹³ annealing at a lower temperature alongside partial dissolution using HF performed over 4–12 h at 180 to 210 °C. In a companion paper by the same authors,³¹⁴ data obtained using microscale X-ray computed tomography, SEM, and Raman spectroscopy, revealed a mechanism for dissolution of zircons. It was found that HF permeated into the crystal core either through fractures caused by radiation damage, via inclusions within soluble high U-zones or via acid percolation through faults. This study demonstrated that the duration of chemical abrasion treatment processes and the temperature at which such processes are performed, can affect resultant U–Pb ratio determinations because elements like lead can be lost. Continuing this theme of elemental losses, it was highlighted³¹⁵ that the perturbed U–Pb isotopic ratio distribution obtained was the superposition of two signals, namely a Gaussian distribution, reflecting the MU of the true isotopic ratio and a skewed distribution that characterised Pb loss. A correction was successfully applied to model the distribution of apparent Pb loss in 10 different igneous samples previously analysed by LA-ICP-MS or SIMS. It was demonstrated³¹⁶ that a chemical abrasion treatment nevertheless can be successfully applied because no systematic bias in the U–Pb dates was observed when 13 zircon RMs were analysed by LA-ICP-MS.

Following sample dissolution, analyte purification through coprecipitation to minimise matrix effects with iron(III) hydroxide ensured³¹⁷ more accurate determinations by HG-MC-ICP-MS of ⁸²Se/⁷⁸Se isotopes in geological samples. Use of coprecipitation with Fe(OH)₃ followed by purification with a AG1-X8 resin was also proposed³¹⁸ for U–Th dating of gypsum so as to concentrate analytes and to remove the calcium matrix.

The use of resins for sample purification is well established and several reported studies are summarised in Table 11. Of note were the purification³⁴⁸ of digested samples for subsequent Mo and Zn isotopic assays using a double-stack column which shortened the separation time by a factor of two but maintained recoveries of 92% for Mo and 96% for Zn. Caution nevertheless should be exercised so as to avoid isotopic fractionation. In the isotopic analysis of Ce²⁹³ and Nd³²⁹ in rock samples, it was noted that use of a TODGA type resin led to the preferential retention of lighter isotopes. In the determination³⁴⁹ of the S mass fraction in carbonate-associated sulfate minerals, samples were mixed with an aqueous suspension of a weakly acidic cation-exchanger whose exchange group (i.e. –COOH) facilitated the decomposition of the carbonate minerals thus releasing soluble sulfates. This approach was successfully applied to the analyses of NMIJ CRMs JDo-1 (dolostone), JLs-1 (limestone) and ML-2 (limestone) with resultant S recoveries in the 74 to 94% range.

Table 11 Methods developed for the determination of isotope ratios in geological material

Element	Matrix	Sample preparation	Technique	Comment	Reference
Ag	Silicate rocks: basalt, andesite, granite, rhyolite and ferromanganese crusts	Four-step chromatographic procedure involving the use of an anion resin Bio-Rad AG® 1-X8, to separate first the major elements and, afterwards, Zn and Cd, and a cation AG® 50 W-X8 resin to further separate Ag from Nb and minor elements	MC-ICP-MS	Recoveries >95%. δ^109AgSRM978a value of doped solutions: (0.00 ± 0.05‰; 2SD, n = 4) in agreement with the recommended values. External precision: 0.05‰ (2SD). Seven USGS RMs were analysed: Basalt (BCR-2 and BIR-1), andesite (AGV-2), granite (GSP-2 and G-2), rhyolite (RGM-2), and ferromanganese crusts (NOD-P-1). δ^109AgSRM978a varied from −0.24 ± 0.05‰ to 0.20 ± 0.05‰, i.e., Ag isotopes fractionated in silicate rocks	291
B	Silicates	HF sample digestion with low-temperature evaporation and sequential chemical purification with Dowex AG® 50W-X8 cation-exchange resin; and an amberlite anion exchange B-specific (IRA 743) resin	MC-ICP-MS	δ ^11BSRM 951 carbonate values (NIST RM 8301, (Coral): 24.24 ± 0.11‰. Intermediate precision: <±0.2 and 0.6‰ for carbonate and silicate RMs, respectively. Good agreement between δ^11B, for carbonate and silicate RMs, with the mentioned sample preparation method and a fusion and purification procedure	292
C and O isotopes	Silicate	Quartz (Polaris, δ^18O = 13.0‰) grinding with a layer of alcohol and three different carbonate sources (KH-2 limestone and Ko and MSA-8 calcites) in a polished agate mortar. Then drying at 105 °C	CF-IRMS	Results affected by instrumental non-linearity for small amounts of gas, the amount of CO2 (blank effect) and the content of neutral silicate particles (matrix effect). The second factor precludes the determined carbonate fraction by shifting the isotope ratios towards an underestimated content of heavy isotopes (^13C and ^18O). Low carbonate fractions (1–2%) induce ppm deviations from the true δ^13C and δ^18O with an 20–40% underestimation in carbonate content	319
Ca	Basalt, andesite, ferromanganese nodules, granodiorite, carbonatite, dunnite	Digestion with HF and HNO3 at 120 °C for 12 h, evaporation and acid dissolution cycles. Purification with N,N,N′,N′-tetra-n-octyldiglycolamide (TODGA) chromatography resin	MC-ICP-MS	^44Ca/^40Ca ratios were determined in eight different geological RMs. To avoid isobaric interference caused by ^40Ar, cold plasma conditions were selected. Matrix effects removed by keeping the concentration of interferents (i.e., Al, Mg, K, Na, and Sr) below 1% that of Ca and below ‰ for Fe. Long term precision for δ^44/40Ca and δ^44/42Ca values <0.10‰	320
Cd isotopes	Soil, rock and manganese nodules	Digestion and Cd purification with an AG® MP-1M macroporous anion-exchange resin followed by a UTEVA resin	Double spike MC-ICP-MS	The combination separates Cd from Sn, Mo, organic matter and avoids the P-related anomalous shifts in the Cd isotope measurement. Recoveries >99% and procedural blank <20 pg. δ^114/110Cd values of rock, soil and manganese nodule RMs determined with respect to NIST SRM 3108 (cadmium standard solution) agree within analytical uncertainty with previous studies. Long-term external precision of δ^114/110Cd BCR-2: 0.04‰ (n = 10, 2SD). Minimum sample size: 10 ng	321
Cd isotopes	Mn nodule, igneous rock, shale, soil, and sediment RMs	Digestion and single-step Cd purification with an AG® MP-1M macroporous anion-exchange resin	Double spike MC-ICP-MS	>99% of Sn removed. δ^114/110Cd with respect to NIST SRM 3108 (cadmium standard solution): USGS RM BHVO-2 (basalt) = 0.055 ± 0.026‰ (2SD, n = 2); USGS RM GSP-2 (granodiorite) = −0.191 ± 0.035‰ (2SD, n = 4); USGS RM COQ-1 (carbonatite) = 0.143 ± 0.053‰ (2SD, n = 6); CNRCG RM GSR-2 (dolomite) = 0.162 ± 0.044‰ (2SD, n = 9); USGS RMs NOD-P-1 and NOD-A-1 (manganese nodules) = 0.185 ± 0.048‰ (2SD, n = 9), 0.184 ± 0.057‰ (2SD, n = 11), respectively; USGS RM SGR-1b (shale) = 0.076 ± 0.046‰ (2SD, n = 6); Chinese IGGP RMs GSD-4a, GSD-5a, GSD-3a (stream sediments) = 0.004 ± 0.047‰ (2SD, n = 9), 0.062 ± 0.046‰ (2SD, n = 6), −0.095 ± 0.055‰ (2SD, n = 6), respectively; NIST RMs 2711a, GSS-1a (soils) = 0.568 ± 0.057‰ (2SD, n = 7), −0.078 ± 0.050‰ (2SD, n = 8), respectively	322
Cr isotopes	Dunite, harzburgite, serpentinite, basalt, rhyolite, syenite, stream sediment, dolomite	Sample dissolution with HNO3 and HF, evaporation, redissolution in appropriate reagents depending on the sample. Three-step ion exchange chromatography with an AG® 50W-X8 cation-exchange resin	TIMS	Using DS-TE-TIMS the method lowered sample consumption to ∼20 ng per ^53Cr/^52Cr measurement. Cr procedural blank <0.5 ng. Cr recovery: 93 ± 8% (2SD, n = 7). External precision: 0.01–0.07‰ (2SD)	323
Fe isotopes	Rich Cr oxide minerals	Fe2O3 and Fe3O4 NPs were combined with pure chromium solutions	fs LA-MC-ICP-MS	A fs laser and ICP-MS under wet conditions are used to overcome matrix effects. The Cr fractionation factor was obtained for isobaric interference correction (i.e., ^54Cr on ^54Fe). Corrected δ^54Fe for hematite and magnetite samples with Cr/Fe ratio = 1.27 agreed with reference values. Long term reproducibility uncertainty = 0.1‰	324
I	Soil and sediments	Drying and sodium carbonate and zinc oxide semi-melting by heating at 750 °C in a muffle furnace. Then hot water washing, addition of anhydrous ethanol and boiling of the mixture, addition of Rh as internal standard, and addition of ascorbic acid to the supernatant. Addition of cation-exchange resin and liquid-phase analysis	ICP-MS	The method avoided problems such as incomplete melting, strong memory effects, and poor stability during the analysis and testing process of iodine. Accurate results were obtained with an RSD (n = 12) of 1.83–2.80% and LOQ = 0.13 μg g^−1	325
Li isotopes	Silicate glass	In situ analysis. Single 120–150 μm spot diameter and focusing the laser on the sample surface	ns-LA-ICP-MS	^6Li and ^7Li observed signal spikes were removed according to a 2SD rejection criterion. Best results were achieved for samples with >40 ppm Li based on matrix-matched standard. An internal precision of 3‰ was achieved for these samples. SiO2 contents deviating from the bracketing standard caused matrix effects, thus giving rise to δ^7Li fractionation	326
Lu–Hf dating	Apatite	Dissolution of apatite in HCl, addition of ^176Lu–^180Hf tracer. Use of two-stage chromatographic separation. Elution through an Eichrom Ln resin and further purification of the Hf fraction by means of a Eichrom DGA-normal resin, coated with chelate forming extractant and combining with trivalent metal ions after drying and re-dissolution	ICP-MS	DGA-normal chromatography column eliminated the interfering elements (Yb, Lu, and W), less than 1 pg of Lu in the analysed apatite Hf solution. Hf recovery: 100%. Apatite Lu–Hf ages of Otter lake, Bancroft, and Durango: 1047.6 ± 3.4 Ma, 1092 ± 17 Ma, and 31.1 ± 1.1 Ma, respectively. Apatite Lu–Hf isochrons independent of the initial isotopic composition. The Hf content in apatite was low and a big sample was required. Both points challenged the applicability of the method to rocks where apatite was a minor phase (e.g., igneous and metamorphic)	327
Nd	Basalts, granites, sediments and sedimentary rocks	Digestion with a HNO3–HF mixture and dissolution with HCl. A single cation-exchange column (AG® 50W-X8 resin) was used to extract Nd. The eluate was dried and re-digested	TIMS	The method was rapid (2–3 h) the ^145Nd/^146Nd ratio was used to correct for fractionation on ^143Nd/^146Nd ratio. The isobaric ^144Sm interference was eliminated	328
Nd	Carbonatite, dolerite, andesite, basalt, granite, rhyolite, granodiorite, manganese nodule, mica schist	Samples were digested for 5 days at 120 °C with HF and HNO3, drying and re-dissolving several times. Purification with a Eichrom DGA resin (TODGA type). Double spike used to correct for mass-dependent fractionation	DS-TIMS	Yields of the geological samples >99%. δ^146/148Nd for a total of twelve geological RMs (USGS RMs COQ-1 (carbonatite), DNC-1a (dolerite), AGV-1 (andesite), AGV-2 (andesite), BHVO-2 (basalt), BCR-2 (basalt), G-2 (granite), RGM-1 (rhyolite), GSP-2 (granodiorite), NOD-A-1 (manganese nodule), NOD-P-1 (manganese nodule), and SDC-1 (mica schist) were reported. Radiogenic ^143Nd/^144Nd of RMs were calculated by a double spike deconvolution with a precision of ≤5 ppm (2SE) that agreed with published values	329
Nd, Sr and Pb	Andesite, basalts, granite, granodiorite, rhyolite, nepheline syenite, andesite	Decomposition and purification on two tandem chromatographic columns filled with (CMPO) dissolved in tributyl phosphate and Sr specific resins (based on the crown ether di-tert-butylcyclohexano-18 crown-6 in 1-octanol). Sr and Pb fractions suitable for isotopic analyses were directly isolated from the Sr resin. An additional column of DGA resin (based on tetra(n-octyl)diglycolamide) was used, to obtain a Nd fraction isolated from the other LREEs	ICP-MS	Eleven geological RMs spanning a wide range of major element concentrations were analysed: USGS RMs AGV-2 (andesite), BCR-2, BHVO-2 and BIR-1a (basalts), G-2 (granite), GSP-2 (granodiorite), RGM-1 (rhyolite), and STM-1 (nepheline syenite), GJS RMs JA-1 (andesite) and JB-3 (basalt), and CRPG RM BE-N (basalt). Satisfactory recoveries and blanks were obtained for the three studied elements. Measured isotope ratios agreed well with published values. All the steps were completed in just one day	330
Ni	Mafic and ultramafic rocks, basalt, granodiorite, manganese nodule, cobalt rich-crust, shale and sediments RMs	Samples acid digestion. Purification with three columns in series: Chelex-100, for the removal of K, Na, Ca, Mg, Al, Fe, Mn, Ba, Cr, and certain amounts of Zn and Ti, AGMP-1M, to eliminate Fe, Mn, Co, Cu, Zn, V, and Ti, and AGMP-50, to remove any remaining Ca, Mg, and Al	MC-ICP-MS	Ni recovery >96%; procedural blank below 1.2 ng and an overall precision better than 0.09‰ (2SD). ^61Ni–^62Ni double spike corrected for fractionation in the chemical procedure and instrumental discrimination bias. δ^60NiSRM986 for RMs (BCR-2, GSR-3 (basalts), GSP-2 (granite), NOD-A-1 (iron-manganese nodule), GSMC-1 (cobalt rich crust), SDO-1, SGR-1 (shales), and GSD-5a and GSD-23 (river sediments) were consistent with previously published work	331
Np, Pu	Soils and sediments	Ashing, spiking with ^242Pu, leaching, co-precipitation and use of a single extraction with a chromatographic column (TK200), with (NH2OH·HCl)/HCl as the eluent	ICP-MS/MS	Oxygen was used as reaction gas to remove the UH^+ interference on Pu isotopes. Recoveries were higher than 70%. ^237Np, ^239Pu, and ^240Pu were determined at femtogram levels, with ^242Pu as a reliable tracer in samples with U/Np and U/Pu atom ratios of up to 10^17 and 10^12. Uranium interference was reduced by a factor of 3.2 × 10^7	217
Pu	Soils, sediments	Ashing, leaching, co-precipitation and use of two sequential chromatographic columns (TK200), with HCl and NH2OH·HCl as eluting solutions	ICP-MS/MS	^238Pu, ^239Pu, ^240Pu, and ^241Pu were simultaneously determined at femtogram levels. Uranium interference was reduced by a factor of 2.12 × 10^9. He and NH3 were used as reaction gases to remove the UH^+ polyatomic	332
Pu	Soils	Ashing (550 °C), leaching with nitric acid, spiking with ^242Pu and addition of NaNO2 to maintain the analyte as Pu^IV. Plutonium was separated with a TEVA column	ICP-MS/MS	The method removed the spectral interference of U on Pu and was validated by analysing the sediment IAEA CRM 384 (Fangataufa sediment). Recoveries improved from 61 to 87% ICP-MS/MS blanks: 0.012 ± 0.008 μg kg^−1 (^238U) and 0.066 ± 0.121 pg kg^−1 (^239+240Pu). Respective LODs were: 0.012 μg kg^−1 and 0.18 pg kg^−1	333
REEs	Sediments, soils and basalts	HNO3 and HF digestion with a high-pressure bomb method	ICP-MS	Micro-USN sample introduction for a 10 fold increase in sensitivity. Low registered ICP-MS oxide ratios (^139La^16O^+/^139La^+ = 0.79%, ^140Ce^16O^+/^140Ce^+ = 0.73%, ^150Sm^16O^+/^150Sm^+ = 0.06%, ^159Tb^16O^+/^159Tb^+ = 0.10%) LODs for 16 REEs from 0.03 (Tm and Lu) to 1.07 (Sc) ng L^−1. Repeatability (RSD, n = 8) between 0.3% (Eu, 10 ng mL^−1) and 3.4% (Lu, 1 ng mL^−1)	204
REEs	USGS BCR-2 and BIR-1A RMs	BCR-2: flux fusion, iron coprecipitation and bulk REE separation with a column. BIR-1A: HF and HNO3 dissolution, iron coprecipitation and bulk REE separation with an ion-exchange column	MC-ICP-MS	A commercial desolvation system was used. Spike/sample isotope ratios were measured in unseparated REEs with oxide-minimising tuning. BCR-2: concentrations for Ce, Nd, Sm, Eu, Dy, Er, and Yb fell within 2% of accepted values. BIR-1-: REE concentrations within 3% of consensus values with 0.3% RSD (n = 3)	334
Re, Os	Organic rich sedimentary and mafic rocks	HNO3 digestion at 230 °C for 24 h	TIMS	The method increased the accuracy of Re–Os isotope analysis, because of the low blank levels. Os isotopes in mafic rocks did not produce fractionation at temperatures above 200 °C	335
Rh	Geological RMs	Rh preconcentration by Sb2O3 fire assay and further microwave digestion of the granules employing 40% (v/v) aqua regia	ICP-MS	Cold plasma conditions were selected. The cell in KED mode removed interferences of ^206Pb^2+ and ^40Ar^63Cu^+ on ^103Rh^+. LOD: 0.012 ng g^−1	336
S	Carbonates and archaean rocks	Sample purification with an anion-exchange resin (AG1X8)	MC-ICP-MS	δ ^34S intermediate precision for 30 nmol S: 0.15‰ (2SD) at 95% confidence. δ^33S, intermediate precision: 0.05‰ (2SD)	337
Si	Silicon oxide, basalt and diatomite	Alkaline fusion, dissolution with ultrapure water, loading on an AG® 50W-X12 resin and subsequent silicon elution with water. pH adjustment with nitric acid	MC-ICP-MS	Under dry plasma conditions, no significant offsets in δ^30Si values outside the range of analytical precision (±0.09‰, 2SD) were caused by chloride, sulfate or dissolved organic carbon. It was recommended to remove organic matter by UV photolysis	338
Sm	Basalt, andesite, granodiorite, granite, nepheline, syenite, manganese nodule, and marine sediment	HNO3–HF dissolution on a hot plate under reflux for 7 days, followed by drying and redissolution with concentrated HCl. Chromatographic isolation with AG® 50W-X12 and TODGA columns	MC-ICP-MS	Sm purification (90.4 ± 0.3% yield) in complex matrices containing Nd, Eu and Gd. Procedural blank: <30 pg. δ^152/149Sm long term intermediate precision 0.04‰, 2SD, based on standard-sample bracketing with NIST SRM 3147a (Samarium standard solution), and Eu internal normalisation to correct for instrumental mass bias (4–8 times better than for the existing methods). For 11 RMs, δ^152/149Sm values from −0.07‰ (andesite) to 0.15‰ (marine sediment)	339
Sm, Nd	Rock (basalt, mafic, andesite and granodioritic) and powder CRMs	Sample digestion with concentrated HF–HNO3–HClO4 followed by chemical purification with a TODGA chromatography resin	MC-ICP-MS	Recoveries were 100% and isobaric interferences were corrected for by applying a mathematical method. The ^147Sm/^144Nd and ^143Nd/^144Nd ratios were simultaneously determined through a single analytical session for five rock powder CRMs. Accuracy and precision were similar as for ID	340
Sr	Rhyolite, basalt, diabase, granodiorite, biotite, phlogopite and SrCO3	Sample digestion with HF, HNO3 and HClO4 at 120 °C for 1 week, with several evaporation and HCl re-dissolution cycles. Purification on AG® 50 W-X12 cation resin, combined with Chelex-100 chelating and AG50-X8 resins	MC-ICP-MS	The method was validated by ^87Sr/^86Sr determination in ten RMs: GSJ JR-1, JR-2 (rhyolite) and JB-2 (basalt); USGS BCR-2, BHVO-2 (basalt), W-2 (diabase) and GSP-2 (granodiorite); CRPG Mica-Fe (biotite) and Mica-mg (phlogopite); NIST SRM 987 (SrCO3). Sr was purified in geological samples with high Rb/Sr mass ratios without the need for elution or evaporation steps, saving analysis time and costs even though large volume of eluent was required	341
Sr, Sm, Nd	Five igneous rock materials (basalts, andesite and granodiorite) and a meteorite (jilin)	Multiple dissolution to dryness steps with concentrated HNO3 and HF, then HCl, followed by HNO3 and centrifugation. Purification with a single-column separation procedure using TODGA-normal resin (DN-B100-S)	TIMS	Close to 3 mg sample mass were analysed with a total chemical procedural blank for Sr, Nd, and Sm of <80, 7, and 3 pg, respectively. Recoveries >91%. Static measurement of Sr, Nd, and Sm isotopes were performed with TIMS equipped with Faraday cups and 10^12 and 10^13 Ω amplifiers. Three single calibration solutions were used NIST SRM 987f (Strontium carbonate) Sr, La Jolla Nd, and Alfa Aesar Sm yielding ^87Sr/^86Sr, ^142Nd/^144Nd, ^143Nd/^144Nd, ^149Sm/^152Sm and ^150Sm/^152Sm ratios of 0.710248 ± 11, 141 846 ± 25, 0.511852 ± 13, 0.516845 ± 17, and 0.275998 ± 9 (2SD), respectively. All results agreed with the published data with a precision of 11–25 ppm (2 RSD)	342
Sr	Apatite	Dissolution and purification	LA-MC-ICP-MS	Apatite ablated volumes from 3000 to 75 000 μm^3. Two data reduction methods tested: (1) measured intensities corrected for gas blank and instrumental mass bias; and (2) additional correction for isobaric interferences of ^87Rb^+, ^166,168,170Er^++, ^170,172,174,176Yb^++, ^40Ca^44Ca^+, ^40Ca^46Ca^+, ^44Ca^43Ca^+ and ^40Ca^48Ca^+. ^87Sr/^86Sr 100 ppm (2SD) precision with a 50 μm laser ablation beam (method 2, more accurate only when ^173Yb^++ is above the LODglobal, 3 s all the blank measurements) and better than 3000 ppm at 10 μm with method 1	343
Thirty-one elements including U, Th, and Pb, and REEs	Zirconolite present in lunar basaltic and granitic rocks	Carbon coating at a 25 μm thickness	EMPA	Age of the zirconolites: 4332 ± 14 Ma (2SD, n = 20), consistent with the U–Pb age (4340.9 ± 7.5 Ma; 2SD) of zircon grains from the same clast measured by an ion microprobe	344
U	Uranyl nitrate	High-temperature dissolution with HNO3, HF and further addition of HClO4. Finally, samples were purified with a UTEVA resin	ICP-MS/MS	An efficient desolvation system and the addition of oxygen to the collision/reaction cell allowed the precise determination of the ^236U/^238U isotopic ratio. The method was validated with an in-house AMS standard of uranyl nitrate	345
V	Igneous rock, manganese nodules, carbonaceous siliceous shale, sediment and soil	Two-stage chromatography method using cation- and anion-exchange resins for V purification and separation from interfering matrix elements (Ti and Cr)	MC-ICP-MS	V recovery was 99%, the total procedural blank was <2 ng. The V isotope compositions of 13 RMs were measured. Long-term reproducibility was better than ±0.10%0 (2SD) for d^51V. d^51V values of two in-house standard solutions were 0.04 ± 0.08‰ (2SD, n = 121) and −1.23 ± 0.08‰ (2SD, n = 91)	161
W	Basalt	Samples were digested with HF–HNO3 at 150 °C for 48 h, followed by multi-step treatment with different acid mixes. The final liquid phase was purified by using a TEVA resin	MC-ICP-MS	W recoveries lay between 93.6 ± 4.7 (2SD, n = 3) and 98.5 ± 1.3% (2SD, n = 3). Total procedure blank was 0.46 ± 0.06 ng. Bracketed mean ^182W/^184W ratio was used to calculate μ^182WN6/4. This parameter was determined in basalt USGS RMs: BHVO-2, BCR-2 and JB-3 and the values were consistent with previously published works with an intermediate measurement precision as low as 5 ppm (2SD)	346
Zr, Lu–Hf, U–Pb isotope ratios	Zircon	Dissolution in a HNO3–HF mixture followed by a four-step chromatographic purification to allow separation of: U and Pb from the matrix, then a late purified ^91Zr–^96Zr double spike was then added. before separation of Zr from Hf, Hf from Ti, and Zr from Mo	MC-ICP-MS	Zr isotope results for four zircon RMs (91500, Mud tank, Plesovice and Penglai) agreed with published values. δ^94ZrIPGP–Zr: −0.041 ± 0.015‰ (2SD, n = 11), 0.018 ± 0.013‰ (2SD, n = 6), 0.089 ± 0.020‰ (n = 3) and −0.117 ± 0.021‰ (2SD, n = 3), respectively. ^176Hf/^177Hf ratios for RMs agreed with published ones. Thus, addition of a purified Zr double spike (with a Zr/Hf ratio of ∼18 000) did not affect the ^176Hf/^177Hf results	347

---

Boron determinations in NIST SRM 8301f (marine carbonate) were carried out³⁵⁰ following matrix removal by mixing the dissolved sample with Amberlite™ resin (IRA-743) and analysing the supernatant by MC-ICP-MS following centrifugation. The advantages of this approach over column clean-up methods included: no column blocking; lower procedural blanks of 10 ± 16 pg; and higher sample throughput, with the ability to process 24 samples within ca. 5 h. Excellent reproducibilities with results of 14.58 ± 0.11‰ (2SD) that agreed with the certified value were achieved when 15 sample aliquots (10 ng sample masses) were processed.

In Pb isotope ratio determinations on small sample sizes (ca. 3 mg) by MC-ICP-MS, use of matrix removal steps was deliberately skipped,³⁵¹ which improved the sample throughput four-fold. However to overcome the resultant potential interferences, an IS procedure or an optimised regression method (ORM) was successfully applied as verified by the accurate lead isotopic data achieved when USGS CRM G-2 (granite) was analysed.

Photochemical vapor generation enabled²⁰⁶ volatile species Se^IV and Te^IV to be liberated from digested samples within 30 s following UV irradiation in the presence of formic and acetic acids with cobalt(II) as a catalyst. The approach was confirmed by the successful analysis of several CRMs including soils, sediments and rocks.

The leaching and assaying of ore samples for total U determinations using an on-line ICP-MS approach³⁵² required samples to be ground and sieved to a particle diameter of <74 μm. The extractant was a mixture of HNO₃, HF and H₂O₂, only a mg of sample was consumed, and measurements were performed within 15 min resulting in recoveries >95%.

A commercially available high-temperature pyrolyser (Rock-Eval® 7S) coupled³⁵³ to a MC-ICP-MS instrument enabled pyrolysable organic and pyritic sulfur fractions to be thermally differentiated. To minimise matrix interference effects, the samples were initially acidified to remove carbonates as CO₂ thus enabling δ³⁴S values (δ³⁴S_organic and δ³⁴S_pyrite) to be sequentially determined on 5 to 90 mg of now decarbonated samples. Assays were performed within 30 min with an average measurement precision of 0.5‰ for those samples that contained >5 nanomole of S.

A triple silicon isotope ratio (²⁹Si/²⁸Si, ³⁰Si/²⁸Si, expressed as δ²⁹Si and δ³⁰Si) measurement protocol involved heating silica-containing samples using an infrared laser in an atmosphere of BrF₅ gas; the resultant SiF₄ gas was analysed³⁵⁴ by gas-source MS, allowing changes in triple silicon isotope ratios at the ppm level to be determined. Gas-source MS was also used³⁵⁵ in the high-precision triple (Δ₄₇, Δ₄₈, Δ₄₉) clumped isotopic analysis of CO₂ liberated from carbonates following acidification with phosphoric acid. Here a fully automated gas extraction system, connected to the dual inlet system of the gas-source MS instrument, enabled excellent (external) repeatabilities to be obtained, i.e., close to those predicted by the shot noise limit. A newly described³⁵⁶ analytical method to liberate oxygen from geological samples combined high-temperature conversion (HTC), in which oxygen-containing organics, phosphates, sulfates, nitrates, carbonates, and silicates were converted to CO within a glassy carbon reactor at temperatures >1400 °C, with a subsequent methanation-fluorination (HTC-M-F) process step wherein the newly generated CO was reacted with hydrogen at 560 °C over an iron catalyst to facilitate the transfer of oxygen from CO to oxygen in H₂O (methanation reaction), which ultimately yielded O₂ when this H₂O was subsequently fluorinated using CoF₃. The oxygen yield was essentially quantitative and in the determination of δ¹⁷O and δ¹⁸O by MS that followed, excellent reproducibilities in Δ¹⁷O of <10 ppm (1SD) were achieved.

5.3 Instrumental analysis

5.3.1 Laser-induced breakdown spectroscopy. In a recent³⁵⁷review (167 references) on LIBS, applications within the field of geosciences were summarised. The review described calibration-free methods that, together with their applications, are increasingly more widely used. Also discussed were use of ANN, SVM, and other chemometrics together with applications within the field of geotechnical engineering. Another review (140 references) discussed³⁵⁸ new strategies and applications of calibration-free LIBS. under non-local (and partially local) thermodynamic equilibrium conditions. The application of ANN and columnar density calibration-free LIBS approaches were included. The paper concluded that some of the challenges with using LIBS that are reported, e.g., self-absorption, could be addressed by calibration-free LIBS.

A recurring problem in the quantitative analysis of geological samples using LIBS is the mismatch in analytical performance between raw samples and RMs especially when the latter are used as pressed pellets. A learning transfer component analysis was applied³⁵⁹ to overcome this problem. By using machine vision technology to automatically detect raw rock samples and a machine-learning optimum algorithm (XGBoost), the contribution of the sample irregularities to inaccuracy was eliminated. Multivariate regressions based on machine learning were efficient in minimising matrix effects in the determination³⁶⁰ of Cl in a simulated Mars environment. The impact of confounding variables, e.g., strong signals caused by cations associated with Cl, was mitigated and a sample weighting procedure implemented that decorrelated the features in the model training phase. Two sets of samples (training and matrix matched) were prepared to test the trained models, and good LODs (0.16% w/w) and rmse of prediction of 0.57% w/w and 0.59% w/w were obtained for both sets of samples. The quantitative analysis capabilities of LIBS were further extended³⁶¹ by improving the models that account for matrix effects. The determination of EC and fixed C in coals was improved once it was verified that fixed C was less impacted by matrix effects in different coal samples than EC. Correct determination of EC content was of great importance when estimating CO₂ emissions from burning of coal. A method based on a dual-cycle variable selection mechanism with competitive adaptive reweighted sampling to optimise PLS regression improved the accuracy of such determinations. In fact, the CO₂ emissions estimated in this way were within 1.7% of the predicted values.

In support of future analysis of Martian rocks and soils, sufficient LIBS data were required³⁶² to build a component inversion model for predicting the collected spectra, which could then be transferred to other instruments. A domain-adaptive fully connected neural network that can realise knowledge transfer between different LIBS spectrometers was discussed.

Improvement of the analytical figures of merit of LIBS by means of LIBS-LIF was possible as exemplified in the determination³⁶³ of Pt in ore samples where excitation at 235.71 nm with secondary fluorescence emission measured at 269.84 nm resulted in the S/N being boosted and interferences reduced. Synthetic calibrants were used, and following 200 laser shots, the LOD improved from 21 mg kg⁻¹ using BS to 0.15 mg kg⁻¹ using LIBS-LIF.

Detailed multielemental compositional maps of geological samples in the order of 10⁷ pixels were achieved³⁶⁴ by employing a hf (kHz) μ-LIBS analyser that could survey areas of tens of cm² at high lateral resolutions <20 μm. The quantity of data generated however required automated fast data treatment methods. This approach enabled small discrete mineral phases to be identified in samples that would otherwise go undetected in conventional LIBS assays.

LIBS measurements supported EPMA measurements in determining³⁶⁵U oxidation states in siliceous materials From the LIBS spectra of oxygen and hydrogen it was proposed that U existed as Ca[(UO₂)₂(SiO₃OH)₂]·5H₂O) because the U oxidation state was inferred from the intensity of the O signal and the hydroxyl environment inferred from the intensity of the H signal. From the superimposition of images obtained using LIBS and EPMA, detection of primary and secondary U mineralisations were possible.

5.3.2 Dating techniques. Applications of ID-TIMS to U–Pb radioisotopic dating were reviewed³⁶⁶ (173 references) wherein the utility of measuring zircons was initially discussed. The paper then proceeded to cover various sample preparation procedures, e.g., abrasion, dissolution, and purification steps and the use of ID protocols and isotopic tracers in TIMS assays. Finally, data treatment methods, age interpretation and the advantages and disadvantages of this technique were discussed.

The application of laser ablation coupled to ICP-MS for dating of geological materials, relies on the appropriate selection of suitable standards. It was reported³⁶⁷ that poor accuracies were obtained when using the MicaMG pressed nano-powder pellet as a RM because of the different ablation yield for this material compared to real mica samples. Other RMs, e.g., natural biotite, muscovite, and phlogopite also yielded different fractionation trends compared to this standard. So to alleviate such issues, the authors proposed the use of the NIST SRM 610 (trace elements in glass) both as a primary RM for normalisation and for drift correction together with a natural mineral such as phlogopite with similar fractionation behaviour to account for ablation yields. Use of this protocol resulted in accurate Rb–Sr age determinations for minerals such as biotites and K-feldspars. In another study,³⁶⁸ NIST SRM 610 was also used, with a subsequent correction for the Rb/Sr offset based on the age of isochronous calibration micas. This approach afforded a <3% deviation in age determination with respect to that obtained using a solution-based method. A similar two-step methodology was reported²⁹⁸ using NIST SRM 610 for instrumental calibration and Chinese CRM GBW04439 (ZBH-25 biotite), originally developed for K–Ar dating, but used here as a second standard to correct matrix effects in Rb–Sr dating. The difficulty in finding a mica mineral with homogeneous ⁸⁷Rb/⁸⁶Sr and ⁸⁷Sr/⁸⁶Sr ratios was overcome³⁶⁹ by using LA-ICP-MS/MS for in situ Rb–Sr dating. Under optimized measurement conditions, it was found that there was now no need for a matrix matched RM. For micas with an age >15 Ma, this methodology was accurate and precise but for younger micas <5 Ma, dating was less accurate because of the shorter radiogenic accumulation time. The U–Pb dating³⁷⁰ of sulfates in a variety of carbonate-based matrices was improved by matrix matching using carbonate RMs, so ensuring comparable ablation yields.

Using LA-ICP-MS in fission-track analysis was advantageous over the use the conventional external detector method (EDM), because of a higher achievable sample throughput and for safety considerations. Both methodologies provided³⁷¹ similar results for samples with a wide range of fission track ages (from <1 to 2 Ga) when a common calibration approach was used, but measuring more isotopes was possible with LA-ICP-MS.

The in situ mapping by LA of geological samples for dating purposes using ⁸⁷Rb/⁸⁶Sr and ⁸⁷Sr/⁸⁶Sr measurements was improved³⁷² using a high repetition rate (>100 Hz) inline scan mode. Use of a 3–4 μm laser spot size enabled 2D Rb–Sr maps to be generated with good lateral resolution and minimised the ablated sample mass thirty-fold compared to a static spot ablation mode. Performance of this approach was verified by analysis of muscovite and biotite with matrix matched crystalline mica used for interference correction. Reverse depth profiling with LA was proposed³⁷³ as an alternative approach to U–Pb dating and multielemental analysis of zircons with complex structures. In this method, samples were embedded in resin as usual but then over-polished so revealing the internal zircon structures, when visualised by cathodoluminescence imaging, they were then ablated from the core to rim wherein the identified zircon age rim was determined to be as narrow as 0.6 μm. Geochronological information gleaned from analysis of leucogranites and granitic gneiss samples was consistent with that obtained using conventional LA-ICP-MS dating.

The age of young uranium ore concentrates was determined³⁷⁴ by LA-MC-ICP-MS using the ²³⁰Th–²³⁴U chronometer with reported ages that ranged between 3.5 to 4435 years. To overcome problems associated with sample impurities that can compromise the accuracy of results for ages >100 years, multiple measurements of ²³⁰Th/²³²Th and ²³²Th/²³⁴U ratios were taken at an interval of about 2 to 5 years. Interestingly, significant variations in the former ratio were detected as a function of age.

The timing of the magmatic crystallization of mafic rocks using the Lu–Hf dating system was³⁷⁵ determined using LA-ICP-MS/MS where addition of NH₃ to the reaction cell enabled ¹⁷⁶Hf and ¹⁷⁸Hf to be measured in mass shift mode at m/z 258 and 260, thus resolving the isobaric interferences on Lu. The ¹⁷⁷Hf values were calculated from the ¹⁷⁸Hf signal using their natural abundance ratios. Measurement of ¹⁷⁵Lu enabled ¹⁷⁶Lu isotope abundance to be calculated using the ¹⁷⁶Lu/¹⁷⁵Lu natural ratio. NIST SRM 610 RM (trace elements in glass) was used as the primary standard, RM OD306 (apatite) was used to correct for LA matrix-induced fractionation effects, and in-house prepared apatite RMs Bamble (Lu–Hf age: 1097 ± 5 Ma) and Harts Range (Lu–Hf age: 343 ± 2 Ma) were analysed to assess accuracy.

The in situ double-dating by laser-ablation – ICP-MS through the measurement of (U–Th–Sm)/He and U/Pb couplets used³⁷⁶ two different ablation assays: the first determined the radiogenic He content; the second the U–Th–Sm–Pb isotopic ratios and mass fractions. The age error associated with in situ double ablation, with concentric and successive ablations, was evaluated for 249 zircons from the Fish Canyon Tuff locality. The application of this procedure to minerals with U–Th–Sm zoning generated a significant (U–Th–Sm)/He age error. Nevertheless, the method allowed a comparison of the intra- and inter-sample maximum age and a deconvolution of a multimodal age spectrum. One of the main problems of this methodology was that U, Th, and He measurements must be expressed in units of molar concentration, rather than molar abundance. This means matrix-matched U–Th concentration standards were required together with accurate He ablation pit measurements. To overcome³⁷⁷ this challenge, proton-induced ³He measurements substituted for ablation pit volume, and use of standards of known U–Th–He age were proposed. The use of relative rather than absolute concentration measurements yielded more accurate results. Double dating U–Pb and U–Th methods were recommended³⁷⁸ in the application of LA-ICP-MS for dating of samples with low Pb contents. It was verified that the Toya tephra’s weighted mean U–Pb age determination of 0.103 ± 0.029 Ma (2SD) was accurate, but, due to the low ²⁰⁶Pb intensity, the age determination for SS14-28 zircon of (0.25 ± 0.10 Ma) was inaccurate. In contrast, both materials yielded accurate ages when U–Th dating approaches was applied.

The potential of γ-rays to extract geochronological information, introduced in the 1970s, was re-investigated³⁷⁹ by means of the production of ³⁸Ar_K from ³⁹K. The established ⁴⁰Ar/³⁹Ar dating procedure is based on irradiation with fast neutrons. In this study, various age RMs commonly used for the established ⁴⁰Ar/³⁹Ar-method were co-irradiated for 60 h at 17.6 MeV maximum energy. The γ-ray bremsstrahlung was low and, hence, the total production of ³⁸Ar_K was depressed. Ages of young RMs could be reproduced within error, whereas older age RMs showed discrepancies due to the low production rate. The advantages of this methodology over the irradiation with neutrons is a reduction in radioactivity produced during the irradiation process, instrumental availability and the possible absence of recoil effects of produced ³⁹Ar.

5.3.3 Inductively coupled plasma mass spectrometry. A wide ranging review³⁸⁰ (119 references) of advances in LA-ICP–MS/MS covered the determination of halogens, noble metals and REEs, as well as in situ dating of U–Pb minerals. The review paid particular attention to analysis methods and the reduction of polyatomic interferences. A companion section provided a valuable overview of different applications of LA-ICP-MS/MS for ¹⁷⁶Lu-¹⁷⁶Hf, ⁸⁷Rb–⁸⁷Sr, ¹⁸⁷Re–¹⁸⁷Os and U–Pb radiometric dating.

Several reviews on specific elemental determinations in geological materials by ICP-MS were published. The difficulties³⁸¹ (81 references) associated with Sn determinations by LA-ICP-MS included: memory effects and their impact on the transient signal shape; isobaric interferences due to doubly charged uranium ions; and other challenges arising from elemental fractionation and matrix effects. The authors also discussed homogeneity issues when interrogating standards at the μm scale. A review³⁸² (121 references) of V isotopic analysis by MC-ICP-MS, paid particular attention to the use of different sample pretreatment approaches dependant on the nature of the mineral encountered, e.g., silicates, metal oxides, carbonates. The potential challenges for in situ V isotopic analysis using fs LA were also addressed. Applications of ICP-MS and X-ray related techniques, i.e., XRF, XRD, XPS, XAS, XANES, μ-XAS for Tl determinations were reviewed³⁸³ (332 references) and suitable RMs for determining the Tl isotopic ratio and mass fractions tabulated.

In ICP-MS isotope ratio determinations, liquid sample introduction systems are still recognized as critical components. Rodríguez-Díaz et al.³⁸⁴ evaluated, for the first time, the performance of sample introduction systems in the isotopic analysis of B by MC-ICP-MS in marine biogenic carbonates, e.g., in clams and corals. A Scott type PFA spray chamber with a PFA MicroFlow nebulizer operating at 30 μL min⁻¹ provided the best results in terms of sensitivity, i.e., signal per mass of B aspirated by the nebulizer. The detector configuration was also varied, and it was found that by combining an ion counter for detecting ¹⁰B with a FC fitted to a 1012 rd amplifier for ¹¹B afforded more accurate results for total B signals with an intensity <0.35 V compared to the use of two FCs.

Advances in MC-ICP-MS have led to the use of iron isotopes as markers of geochemical history in a wide range of environments. King-Doonan et al.³⁸⁵ developed a new mathematical correction procedure for those situations in which the Fe concentration in the samples and isotope standards were mismatched by >10%, producing the so-called self-induced matrix effect that can cause mass bias.

The application of ICP-MS/MS for overcoming spectroscopic interferences in geochemical analysis is still expanding. Using N₂O as a reaction gas, it was possible³⁸⁶ to overcome the BaO⁺ interference on Eu⁺ even at Ba/Eu ratios as high as 125 000. Another study proposed³⁸⁷ NH₃ as a reaction gas to yield Ag(NH₃)₂⁺, for Ag determination. The isobaric interferences caused by ZrO⁺ and NbO⁺ were also removed by the generation of ZrO(NH₃)₄⁺, ZrO(NH₃)₅⁺, and NbO(NH(NH₃)₃)⁺, species thus improving the LOD to <0.53 ng g⁻¹. For in situ Rb–Sr geochronological applications, undertaking³⁸⁸ simultaneous ⁸⁷Sr/⁸⁶Sr and ⁸⁷Rb/⁸⁶Rb isotope ratio determinations was possible using SF₆ as a reaction gas, because Sr was now measured as SrF⁺. The now simultaneous measurement of Rb and Sr isotopes with improved precision by LA-MC-ICP-MS/MS, enabled integrations of signals from individual laser shots. Time-resolved signals demonstrated that individual laser spots contained multiple isochronous subpopulations.

Matrix effects, elemental fractionation or representative sub-sampling remain as issues in LA studies. For instance, when andradite-grossular garnets were ablated³⁸⁹ using a 193 nm excimer laser, there was evidence of melting and sample evaporation. In the case of rutile, examination of the craters revealed non-uniform ablation with melting, splashing and thermal stress cracking effects. One possible solution³⁹⁰ was to use a −30 °C cryogenic ablation cell. Here sulfide minerals such as pyrite, chalcopyrite, and galena were ablated and examination of the resulting crater verified that cooling the sample led to a reduction in melting and vapor redeposition at the area affected by heat. The LA-ICP-MS signal precision (RSD) improved from 28–39% to 11–24% when samples were chilled. The application of fs-LA-MC-ICP-MS also minimised³⁹¹ analyte fractionation effects encountered in the determination of the ³⁴S/³²S isotope ratios in apatite samples in so far that results obtained now were in good agreement with those obtained using SIMS.

In the determination³⁹² of Li isotopes in 11 spodumene samples by fs-LA, the impact of plasma operating conditions and data reduction protocols on measurement accuracy were investigated. Adding small amounts of water to the carrier gas before the ablation cell improved stability of the baseline signal and the reproducibility of the isotope ratios determined. The Li ratios obtained using NIST SRM 610 (trace elements in glass) as a calibrant agreed with those measured by solution MC-ICP-MS because matrix-matched calibrants were now not required. The use of a wet plasma also minimised²⁵⁹ matrix effects when determining Fe isotopes in silicate minerals such as olivine, garnet, amphibole and biotite by LA-MS-ICP-MS. With the aid of a ns ArF 193 nm excimer laser, scheelite samples were also successfully analysed³⁹³ with NIST SRM 610 (trace elements in glass) as calibrant coupled with He as a carrier gas and tungsten as an IS. Measured CaO concentrations agreed with those results obtained by EPMA and data obtained for both major (Ca, W) and trace elements (Fe, Mo, REEs, Si and Y) were in agreement with those obtained by ICP-MS following dissolution.

Additional sources of measurement inaccuracy can be attributed to the MS hardware itself, as demonstrated³⁹⁴ when B isotopic compositions were measured by LA-MC-ICP-MS. Here it was demonstrated that scattering of both Ca and Ar ions within the spectrometer flight tube degraded the accuracy of B isotope determinations, but this issue was eliminated when deflectors to focus and guide B ions were used. Furthermore, mass fractionation effects were influenced by the plasma operating conditions employed, hence use of hot plasma conditions was preferred but at the expense of accuracy when low boron concentrations were measured because of the inherent reduction in instrumental sensitivity when using such plasma conditions.

There is an increasing interest in performing spatially-resolved trace element mapping studies. The coupling³⁹⁵ of LA with ICP-ToF-MS enabled 2D ultra-fast mapping of solid materials at a scan rate of between 0.3 and 30 mm² h⁻¹ to be performed, thus enabling several elements in geological RMs and in melted and partially melted micrometeorites to be quantified. Fast data acquisition across the full periodic table within several tens of microseconds was now possible and the use of a low dispersion LA chamber enhanced the spatial resolution. Quantification was achieved using glass bead calibrants prepared by fusion of matrix-matched geological RMs and normalising data by summing element oxide content to 100%. Unfortunately, for those analytes that provided a low analytical signal or for those elements, such as As, Cd, Ge, In and Sn, for which suitable RMs were lacking, generation of indicative data were only possible. Analytical issues arising by an irregular distribution of the IS, added to correct for ablation rate differences, within- and between-samples and standards were addressed³⁹⁶ by use of an optical profilometer to measure the ablation volume per pixel so enabling elemental mapping data to be normalised with respect to volume.

Undertaking quantitative imaging by ID-ICP-MS was possible by combining³⁹⁷ the sample aerosol from the ablation chamber with that generated from standards using a conventional nebuliser – cyclonic spray chamber system. Use of isotopically spiked solutions enabled transport effects to be accounted for thus enabling the quantitative imaging of Fe and Sr in NIST SRM glasses 610, 612 and 614 to be performed without recourse to matrix-matched solid calibrants.

Accurate in situ multielemental analysis of single fluid inclusions by LA-ICP-MS in complex geofluids, e.g., multi-solute basinal brines, magmatic-hydrothermal fluids or pegmatitic fluids, was possible³⁹⁸ by combining microthermometric and LA-ICP-MS data in conjunction with thermodynamic modelling. This approach was applied to quantifying elements in chloride-dominated fluid inclusions where element to sodium ratios were calculated from the LA-ICP-MS data. The unknown Na concentration was calculated from modelling the chemical equilibrium between the aqueous phase and the last melting solid phase within the fluid inclusion. This was accomplished by applying the Pitzer ion-interaction model for aqueous electrolytes.

5.3.4 Secondary ion mass spectrometry. SIMS is often the technique of choice in analysis of special samples, such as lunar samples or meteorites, because of its inherent advantages such as low sample consumption, high spatial resolution, and high sample throughput. However because of the severe matrix effects, measuring complex samples required calibrations generated by assaying several RMs, which was accommodated here by modifying³⁹⁹ split mounts that could deploy several RMs within instrumental sample holders. Such mounts were independently prepared and could be reused many times thus reducing RM consumption. Their applicability was demonstrated in the successful determination of O and U–Pb isotopes in various CRMs (91500 zircon, Plešovice zircon, Qinghu zircon, ZN3 zircon, and NIST SRM 610 glass).

There have been issues with data reduction protocols and error propagation algorithms used in U–Pb geochronological studies involving SIMS that can degrade the precision and accuracy of the determinations, namely: different results were obtained dependent upon how data were treated, e.g., use of ²⁰⁶Pb/²³⁸U or ²³⁸U/²⁰⁶Pb; use of symmetrical confidence intervals could be unrealistic and random and systematic uncertainties propagate separately, but a new algorithm written⁴⁰⁰ in R addressed such concerns.

The trace H₂O content in quartz glasses was determined⁴⁰¹ by LG-SIMS. The problems associated with such measurements, i.e., low vacuum caused by the large volume including the transfer and coupling column, were alleviated and the vacuum within the analytical chamber could be increased. This could create other issues such as desorption of water molecules from instrumental surfaces and challenges in maintaining the required vacuum because of the large internal volumes of such instruments. However, installing a Peltier-cooling trap step instead of a liquid nitrogen tank trap, enabled stable trap operation at −80 °C and a vacuum pressure of 1.7 × 10⁻⁹ mbar to be maintained. To prepare test samples, small pieces of about 200 mm were selected, placed on a double adhesive tape and cast within a melted Sn–Bi alloy at 90 °C. These modifications improved the LOD from 50 mg kg⁻¹ to 0.15 mg kg⁻¹.

Data obtained by nanoSIMS can be strongly affected by matrix effects requiring empirically derived correction factors to be used. For example, when Li isotope ratios were determined⁴⁰² in silicate glasses, during the simultaneous measurement of ⁶Li, ⁷Li and ³⁰Si ions, a matrix effect on δ⁷Li was clearly observed with a 19‰ maximum instrumental mass fractionation. The extent of this mass fractionation correlated with the silica content in samples, so an offline correction method was developed whereby preliminary Si concentrations were determined to subsequently derive the required correction factors. When compared within an online method where the corresponding factors were calculated using measured ³⁰Si signals, similar performance was noted but the online method was faster and more accurate. To estimate accuracy, the absolute difference between the instrumental mass fractionation factors from the analytical data and those corrected using the calibration equation was calculated. It was observed that δ⁷Li was within 3‰ of the reference values for five glass standards.

5.3.5 X-ray fluorescence spectrometry and related techniques. In a review⁴⁰³ (90 references), the instrumental attributes (energy, energy range, resolution and sensitivity) of various commercially available WDXRF were tabulated as were a number of geological applications. Advice was provided on the selection of the most suitable instrument for a particular given application.

In the quantification⁴⁰⁴ of light elements, such as C, H, O and N, in coals by means of WDXRF, coherent and incoherent spectral scattering was used in conjunction with PLS regression techniques to generate required calibrations so enabling low-Z elements to be quantified without recourse to use of instruments equipped with specialised optics. Results agreed with those obtained using an alternative elemental combustion-based analyser approach.

An instrument that incorporated both pXRF and pXRD capabilities, enabled⁴⁰⁵field-based, in situ elemental mapping to be performed on a harzburgite material over a circular area with a 10 cm perimeter. Fast (<30 min) analysis with a LOD of ca. 1% (m m⁻¹) was possible and data obtained for Al, Ca, Cr, Fe, Mg, Mn, Ni and Si agreed with data obtained using laboratory-based EPMA, SEM-EDS, XRD and XRD assays.

The thickness of geological layers was analysed by XRF⁴⁰⁶ by interrogation of multi-element count data obtained from each layer. The shape of their resultant data frequency distributions and how well such distributions correlated were criteria for determining whether there was a degree of similarity between layers. Along similar lines, use⁴⁰⁷ of XRF-derived data in conjunction with PCA aided the identification and estimation of the thickness of layers in deposits arising from tsunami events.

Spectral interferences can be problematical in the analysis of geological samples using ED TXRF as exemplified⁴⁰⁸ in the determination of U and Th in ashes and ores where it was identified that the Th Lα signal overlapped those from Fe Kα and Rb Kα, and where the U Lα signal overlapped those from Rb Kα and Sr Kα. Use of PLSR improved the determination of Th and U in rubidium-rich samples, i.e., under conditions of strong line overlapping. However, in rubidium-depleted samples, spectral deconvolution was the suggested solution for U determinations. Another study addressed⁴⁰⁹ the influences on the analyte X-ray emission signals arising from nearby atoms. Here CaF₂, Na₃AlF₆, NaF and MgF₂ calibrants prepared as pressed pellets, together with fused bead samples of granite (CG-1a), basalt (SMB) and limestone (KN) were analysed to obtain three sets of F calibration lines. It was noted that the CaF₂ calibrants were best for the analysis of fluorites, apatites or sphenes, whereas MgF₂ calibrants were best suited in the analysis of micas and NaF calibrants were best suited in the analysis of villiomite or cryolite materials.

The potential of synchrotron X-ray fluorescence microscopy (XFM) and X-ray backscatter diffraction mapping (XBDM) as a combinational tool for the rapid acquisition of structural and elemental information in geological materials, enabled⁴¹⁰ data to be obtained at μg kg⁻¹ concentrations and at scales that spanned from that of the crystal lattice (10⁻¹⁰ m) to that of the specimen (10⁻² m) within a single measurement. The XFM/XBDM combination shortened assay times by two orders of magnitude compared to the time taken using the independent techniques of SEM and electron backscatter diffraction. Furthermore, complex sample preparation techniques were now not required, and it was possible also to examine features at grain and sub-grain boundaries as well as interrogate fractures.

In an attempt to obtain speciation information, WDXRF was used to determine Fe^II/Fe^total ratios in geological samples of complex mineralogy by recording⁴¹¹ the fluorescence emission from the Fe Lα_1,2 line which was inversely correlated with these ratios. To separate Fe Lα_1,2 emission peaks efficiently from the close Fe Lβ_1,2 peaks, spectral deconvolution was undertaken using the commercially available OriginPro 2022b software. Matrix effects were overcome through the calculation of a chemical index factor that enabled the accurate determination of Fe^II/Fe^total ratios in different matrices. In another reported study, it was highlighted⁴¹² that the chemical nature of the sample did not have any impact on the ratios of the close Fe Kβ₅/Fe Kβ_1,3 lines that correlated linearly with the FeO/Fe₂O_3total (w/w) ratios, so allowing Fe speciation to be elucidated. Although, a high-resolution WDXRF setup was required for such speciation measurements, i.e., use of a LiF analyser crystal with a 0.13° collimator, this approach offered important advantages such as high sample throughput, and minimum sample preparation without recourse to using radioactive sources.

A transition-edge sensor (TES) was applied for the first time as a new detector for analysis⁴¹³ by μ-XANES in the hard X-ray region. This TES included a thermometer with the ability to measure temperature changes after X-ray absorption and determination of both U distribution at the micro-scale in biotite, together with information on the U chemical species was now possible. Unlike when utilising a conventional SDD, effective separation of the fluorescent emissions of U Lα1 and Rb Kα lines was now possible when using the TES, which enabled the accurate mapping of trace U in the presence of high amounts of Rb. Furthermore, the S/B was close to 46 times higher than that achieved using a conventional SDD.

5.3.6 Other techniques. The quantification⁴¹⁴ of bromine in glasses and minerals was undertaken by EPMA by measuring the Br K_α X-ray emission (11.909 keV) in combination with use of a LiF diffracting crystal because, unlike at the Br L_β line, aluminium emissions did not interfere. Results obtained for scapolite-group minerals by EPMA compared favourably with those results obtained using alternatives techniques such as INAA, LA-ICP-MS, noble gas method or SIMS. No matrix-matched calibrants were required and other advantages of EPMA were exploited, namely: its high spatial resolution (≤10 μm) capability; its ability to perform non-destructive assays; and the fact that the EPMA instrument is typically less expensive than some of the alternatives.

Use of MC SHRIMP was reported⁴¹⁵ for determining isotope ratios in Fe–Mg containing oxidic and salicaceous materials. The Earth’s magnetic field caused isotope-dependent trajectory deviations for ²⁴Mg⁺, ²⁵Mg⁺ and ²⁶Mg⁺ within the instrument’s sample chamber that significantly affected the isotope ratios determined, but such deviations were corrected by applying a current to two Helmholtz coils so aligning the three Mg ion beams. Under these circumstances, instrumental mass fractionation was dependent on the mineral structure, whereas it was negligibly affected by the chemical composition. As such differences were constant however for a given mineral, i.e., structure, it was possible to calculate fractionation correction factors for any given mineral thus permitting the analysis of different specimens with a good accuracy (δ²⁶Mg = 0.3‰) using a single stated mineral as RM.

Multimodal assays offer new possibilities, such as the synergistic combination of LIBS with hyperspectral imaging (NIR-SWIR),⁴¹⁶ for mineral identification. Two distinct approaches were explored: a traditional sensor fusion, that allows increasing the information supplied by the two techniques; and, a knowledge distillation approach, in which knowledge is condensed and transferred from a complex to a simpler model, where LIBS was used as an autonomous supervisor for hyperspectral imaging. In this second approach, a teacher–student scheme was adopted and in assessing accuracy and robustness of a measurement mode, the teacher, was used to generate labels for a training dataset, subsequently giving support to the supervised training procedure using the dataset of the second spectral imaging modality, the student. This novel approach was used to train a model taking only NIR-SWIR data as input using the LIBS as a supervisor during training.

Planetary mineralogical samples typically contain osmium at low concentrations, an impediment for undertaking highly precise ¹⁸⁴Os/¹⁸⁸Os, ¹⁸⁶Os/¹⁸⁸Os, and ¹⁸⁷Os/¹⁸⁸Os isotopic ratio measurements. However such measurements were possible by operating⁴¹⁷ a NTIMS instrument in a mode that allowed the three ratios to be simultaneously measured. The mode required the system to be equipped with 9 FCs with 10¹² Ω and 10¹³ Ω amplifiers together with two compact discrete dynodes (CDDs). The precisions reported for a 12 ng Os sample were 2, 0.061, and 0.050‰ (2 RSD), for the ¹⁸⁴Os/¹⁸⁸Os, ¹⁸⁶Os/¹⁸⁸Os, and ¹⁸⁷Os/¹⁸⁸Os ratios, respectively. The method avoided non-linear signal change in the instrument during the analysis and removed oxygen isobaric interferences caused by PtO₂ and PtO₃.

Four modifications were made⁴¹⁸ to an EA-IRMS instrument to boost sensitivity in the determination of the N isotope ratio (δ¹⁵N) in geological samples that contained only trace nitrogen concentrations. These included: reducing the diameter of the reaction tube and hence the dead volume; replacing magnesium perchlorate in the water trap with soda lime; using a needle valve to better control split flow rates; and modifying GC operational parameters through selection of an appropriate GC column with optimal carrier gas flow rates and split flow ratios. Provided that a sample containing >12 μg N could be analysed, a SD of <0.2‰ was achievable.

5.4 Software and databases

The increasing volume of analytical data now being generated by novel, faster, more accurate and precise instrumental techniques necessitates new ways for extracting geochemical information. A review⁴¹⁹ (240 references) highlighted selected applications of machine-learning tools to help identify rocks and sediments, for digital mapping purposes, to assist in soil physical and chemical properties prediction, e.g., predicting the abundance of major elements in dust, rocks, and soils under Mars conditions, and deep space exploration, e.g., elemental distributions of lunar surface, lunar surface TiO₂ abundance maps, to elucidate the Martian surface mineral distribution and to determine its surface age. These tools help to interpret spectroscopy data, to develop numerical modelling and molecular ML. In contrast, applying ML techniques was problematic when minerals exist as a solid (e.g., chlorite) rather than a single, stoichiometric ideal. In these cases, sampling of the mineral phases surrounding that of interest can be a significant source of error. A dataset of more than 3000 major and minor multielemental analyses of chlorite by means of LA-ICP-MS and EPMA was interrogated⁴²⁰ when, interestingly, it was verified that 7.4% of these assays were from non-chlorite materials. Therefore, ML screening could be a useful tool for generating robust data obtained from in situ analyses.

Machine learning was also applied to U–Pb dating studies where obtaining good results in certain minerals is often hampered by their extremely low U concentrations. Therefore in order to ascertain⁴²¹ U content in garnets, predictive analysis was undertaken drawing upon data for other major and minor elements obtained by EPMA. A database was compiled, mostly from the publicly available GEOROC database, which contains elemental data for 4366 garnet samples, that PCA was able to classify into two independent groups, namely uranium-rich or -poor minerals. Additionally, a supervised machine-learning method (neural network) was developed to train a data analysis model with the ability to discriminate between U-rich and -poor (<2 mg kg⁻¹) samples with a predictive accuracy as high as 92%. The discrimination tool was especially useful in the assessment of garnets from kimberlites, high-grade metamorphic rocks and intermediate to felsic magmatic rocks.

A novel software algorithm, Isoclock, to correct RM data obtained by LA-ICP-MS for common Pb and to calculate isotopic fractionation and written in Python was released.⁴²² Two calculation modes were possible: firstly where the age of an RM is known or, secondly in which the measured U–Pb isotope ratio data of the RM could be inputted.

Undertaking quantitative 2D mapping exercises is a challenge when heterogeneous mineral phases are interrogated using LA-ICP-MS and here use of data reduction and processing tools were invaluable. One such open-source tool was XMapTools⁴²³ that enabled 2D density plots to be automatically constructed. The use of the open-source LinuxCNC software, originally designed to control CNC machinery but applied here to better control the movement of the laser ablation stage, facilitated better depth profiling exercises to be conducted in the analysis of olivine crystals.⁴²⁴ The subsequent elemental data obtained agreed with EPMA measurements and the Mg and Fe isotopic data information was precise with δ²⁴Mg ≤ 0.12‰ and δ⁵⁶Fe ≤ 0.15‰.

Use of a customised Iolite software package improved²⁷⁶ Sr isotopic measurements using LA-ICP-MS by performing baseline corrections in the removal of krypton (an impurity in the argon gas) interferences by calculating βRb and Rb–Sr fractionation factors (from repeated analyses of BCR-2G glass); by peak-stripping of the ⁸⁷Rb interferent (using the measured ⁸⁵Rb and canonical ⁸⁷Rb/⁸⁵Rb); and by correction for other isobaric interferences, e.g., Er²⁺, Lu²⁺ and Yb²⁺.

In a new fundamental parameter approach for the quantification of environmental matrices by EDXRF,⁴²⁵ the measured fluorescent intensities of samples were used together with either reference data of experimental absorption at a single energy or the known concentration of an internal standard in the sample. The claimed advantages of this procedure were: it provided information on the residual matrix and the analytes; it was based only on simple absorption calculations supported by tabulated fundamental parameters and absorption data; the determined concentration was independent of the number of elements quantified; quantification was possible if samples contained a known concentration of internal standard; and no special sample preparation methods were required. Validation was performed by analysing NIST SRM 2711 (Montana soil) and SRM 2704 (Buffalo River sediment) with relative measurement errors of 2–8%.

6 Abbreviations

AA	atomic absorption
AAS	atomic absorption spectrometry
AEC	anion exclusion chromatography
AES	atomic emission spectrometry
AF4	asymmetric flow field-flow fractionation
AI	artificial intelligence
AMS	accelerator mass spectrometry
ANN	artificial neural networks
AOF	adsorbable organic fluorine
APM	atmospheric particulate matter
BAM	Federal Institute for Materials Research and Testing (Germany)
BCR	Community Bureau of Reference (of the Commission of the European Communities)
BEC	background equivalent concentration
BSE	backscattered electron
CCP	capacitively coupled plasma
CENA-USP	Centro de Energia Nuclear e Agricultura-Universidade de São Paulo
CF	calibration free
CI	confidence interval
CIMS	chemical ionisation mass spectrometry
CMPO	octyl(phenyl)-N,Ndiisobutylcarbamoylmethylphosphine oxide
CNACIS	China National Analysis Centre for Iron and Steel
CNC	computer numerical control
CPE	cloud point extraction
CRC	collision/reaction cell
CRCCRM	Chinese Research Centre for Certified Reference Materials
CRM	certified reference material
CRT	creatine
CS	continuum source
CV	cold vapor
CVG	chemical vapor generation
DBD	dielectric barrier detector
DES	deep eutectic solvent
DGT	diffusive gradient in thin film
DLS	dynamic light scattering
DOC	dissolved organic carbon
DOM	dissolved organic matter
DS-TE-TIMS	double spike-total evaporation-thermal ionization mass spectrometry
EA	elemental analyser
EA-IRMS	elemental analyser-isotope ratio mass spectrometer
eBC	equivalent black carbon
EC	Environment Canada
ECCC	Environment and Climate Change Canada
ED	energy dispersive
EDM	external detector method
EDS	energy dispersive X-ray spectroscopy
EDTA	ethylenediaminetetraacetic acid
EDXRF	energy dispersive X-ray fluorescence
EF	enrichment factor
EMPA	electron probe microanalysis
EOF	extractable organic fluorine
EPMA	electron probe microanalysis
ERM	European Reference Material
ESI	electrospray ionisation
ETAAS	electrothermal atomic absorption spectrometry
ETV	electrothermal vaporisation
FAAS	flame atomic absorption spectrometry
FC	Faraday cup
fs	femtosecond
GC	gas chromatography
GC-CV-AFS	gas chromatography-cold vapor generation-atomic fluorescence spectroscopy
GC-IRMS	gas chromatography isotope ratio mass spectrometry
GEM	gaseous elemental mercury
GeoPT	Proficiency Testing Scheme organised by the International Association of Geoanalysts
GFAAS	graphite furnace atomic absorption spectrometer
GLS	gas liquid separator
GO	graphene oxide
GOM	gaseous oxidised mercury
GSR	gunshot residues
HgP	particulate-phase mercury
HG	hydride generation
HPLC	high performance liquid chromatography
HR	high resolution
HR-CS-GFAAS	high resolution continuum source graphite furnace atomic absorption spectrometer
HTC	high-temperature conversion
IAEA	International Atomic Energy Agency
IC	ion chromatography
ICP	inductively coupled plasma
ICP-MS	inductively coupled plasma mass spectrometry
ICP-OES	inductively coupled plasma optical emission spectrometry
ICP-TOF-MS	time of flight inductively coupled plasma mass spectrometry
ID	isotope dilution
ID-TIMS	isotopic dilution thermal ionization mass spectrometry
IGGE	Institute of Geophysical and Geochemical Exploration
IMF	instrumental mass fractionation
INAA	instrumental neutron activation analysis
IRMM	Institute for reference materials and measurements
IRMS	isotope ratio mass spectrometry
IS	internal standard
IUPAC	International Union of Pure and Applied Chemistry
JSAC	Japan Society for Analytical Chemistry
KED	kinetic energy discrimination
KRISS	Korean Research Institute of Standards and Science
LA	laser ablation
LA-ICP-MS	laser ablation-inductively coupled plasma mass spectrometry
LF-IRMS	laser fluorination isotope ratio mass spectrometry
LG-SIMS	large-geometry secondary ion mass spectrometry
LIBS	laser-induced breakdown spectroscopy
LIBS-LIF	LIBS assisted by laser fluorescence
LLE	liquid–liquid extraction
LLME	liquid–liquid microextraction
LOD	limit of detection
LOQ	limit of quantification
LPE	liquid phase extraction
LPME	iquid phase microextraction
M@GO-TS	magnetic particles and graphene oxide functionalized with methylthiosalicilate
MAD	microwave-assisted digestion
MAE	microwave-assisted extraction
MC	multicollector
MC-ICP-MS	multicollector inductively coupled plasma mass spectrometry
MDG	microdroplet generator
Me2Hg	dimethlymercury
MeCo	methylcobalamin
MeHg	methylmercury
MeOH	methanol
MICAP	microwave inductively coupled atmospheric-pressure plasma
MIL	magnetic ionic liquid
MIP	microwave-induced plasma
ML	machine learning
MNP	magnetic nanoparticles
MOF	metal–organic framework
MP	microplastic
MS	mass spectrometry
MS/MS	tandem mass spectrometry
MSWD	mean squared weighted deviation
MU	measurement uncertainty
mu CT	microscale X-ray computed tomography
NDIR	non-dispersive infrared
NIM	National Institute of Metrology of China
NIST	National Institute of Standards and Technology
NO	nitric oxide
NP	nanoparticle
NRCC	National Research Council of Canada
NTIMS	negative thermal ionization mass spectrometry
OES	optical emission spectroscopy
ORM	optimised regression method
PCA	principal component analysis
PFA	perfluoroalkyl
PFAS	per- and polyfluoroalkyl substances
PLS	partial least squares
PLSR	partial least squares regression
PM1.0	particulate matter (with an aerodynamic diameter of up to 1.0 μm)
PM10	particulate matter (with an aerodynamic diameter of up to 10 μm)
PM2.5	particulate matter (with an aerodynamic diameter of up to 2.5 μm)
PM2.5–10	particulate matter (with an aerodynamic diameter of between 2.5–10 μm)
PS	polystyrene
PS-MP	polystyrene microplastic
PTE	potentially toxic element
PTFE	polytetrafluoroethylene
PTR	proton transfer reaction
PVDF	polyvinylidene difluoride
PVG	photochemical vapor generation
pXRD	portable X-ray diffraction
pXRF	portable X-ray fluorescence
QICP	quadrupole inductively coupled plasma
QT	quartz tube
REE	rare earth element
RH	relative humidity
RM	reference material
rmse	root mean square error
RNAA	radiochemical neutron activation
RSD	relative standard deviation
S/B	signal-to-background ratio
SAGD	solution anode glow discharge
SBET	simplified bioaccessibility extraction test
SCR	single-chamber reactor
SDD	silicon drift detector
SEM	scanning electron microscopy
SES	spark emission spectroscopy
SF	sector field
SF-ICP-MS	Sector-field inductively coupled plasma mass spectrometry
SHRIMP	multi-collector Sensitive high-resolution ion microprobe
SIMS	secondary ion mass spectrometry
SIMS-SSAMS	secondary ion mass spectrometry-single-stage accelerator mass spectrometry
sp	single particle
SPE	solid-phase extraction
spICP-MS	single-particle inductively coupled plasma mass spectrometry
spICP-TOF-MS	single-particle time of flight inductively coupled plasma mass spectrometry
SPME	solid-phase microextraction
SR	synchrotron radiation
SRM	standard reference material
SSB	sample standard bracketing
SVM	support vector machine
SWIR	shortwave infrared
TBAOH	tetrabutylammonium hydroxide
TBP	tributyl phosphate
TBT	tributyltin
TCA	transfer component analysis
TD	thermal desorption
TDLAS	tuneable diode laser absorption spectroscopy
TEL	triethyllead
TEM	transmission electron microscopy
TES	transition-edge sensor
TET	triethyltin
TIMS	thermal ionisation mass spectrometry
TML	trimethyllead
TMT	trimethyltin
TOA	thermal optical analysis
TOC	total organic carbon
TODGA	N,N,N′,N′-tetra-n-octyldiglycolamide
TOF	time of flight
TPhT	triphenyltin
TXRF	total-reflection X-ray fluorescence
UAE	ultrasound-assisted extraction
UFP	ultra-fine particle
USGS	United States Geological Survey
USN	ultrasonic nebuliser
UTEVA	diamyl, amylphosphonate
VOC	volatile organic carbon
WDX	wavelength-dispersive X-ray fluorescence
WDXRF	wavelength-dispersive X-ray fluorescence
WEPAL	Wageningen Evaluating Programs for Analytical Laboratories
XAFS	X-ray absorption fine structure
XAS	X-ray absorption spectroscopy
XBDM	X-ray backscatter diffraction mapping
XFM	X-ray fluorescence microscopy
XPS	X-ray photoelectron spectroscopy
XRD	X-ray diffraction
XRF	X-ray fluorescence
XRFS	X-ray fluorescence spectroscopy
μ-XANES	micro-X-ray absorption near edge structure spectroscopy

Conflicts of interest

There are no conflicts of interest to declare. The contents of this paper, including any opinions and/or conclusions expressed, are those of the authors alone and do not necessarily reflect HSE policy.

References

1. J. R. Bacon, O. T. Butler, W. R. L. Cairns, O. Cavoura, J. M. Cook, C. M. Davidson and R. Mertz-Kraus, J. Anal. At. Spectrom., 2024, 39(1), 11–65.
2. M. Patriarca, N. Barlow, A. Cross, S. Hill, A. Robson and J. Tyson, J. Anal. At. Spectrom., 2024, 39(3), 624–698.
3. E. H. Evans, J. Pisonero, C. M. M. Smith and R. N. Taylor, J. Anal. At. Spectrom., 2024, 39(5), 1188–1211.
4. R. Clough, C. F. Harrington, S. J. Hill, Y. Madrid and J. F. Tyson, J. Anal. At. Spectrom., 2024, 39(7), 1629–1664.
5. C. Vanhoof, J. R. Bacon, U. E. A. Fittschen and L. Vincze, J. Anal. At. Spectrom., 2024, 39(9), 2152–2164.
6. R. Clough, A. Fisher, B. Gibson and B. Russell, J. Anal. At. Spectrom., 2024, 39(11), 2617–2693.
7. C. Yang, S. Niu, Y. Xia and J. Wu, TrAC, Trends Anal. Chem., 2023, 168, 117348.
8. H. Dong, X. Wang, X. Niu, J. Zeng, Y. Zhou, Z. Suona, Y. Yuan and X. Chen, TrAC, Trends Anal. Chem., 2023, 167, 117261.
9. X. Wu, Z. Wu, M. Amde, W. Zhao, G. Li, S. Gao, M. Song, Y. Ma, Z. Tan, Y. Yin and Q. Zhang, At. Spectrosc., 2023, 44(5), 282–297.
10. J. Labuda, J. Barek, Z. Gajdosechova, H. Goenaga Infante, L. Johnston, Z. Mester and S. Shtykov, Pure Appl. Chem., 2023, 95(2), 133–163.
11. J. Labuda, J. Barek, Z. Gajdosechova, S. Jacob, L. Johnston, P. Krystek, Z. Mester, J. Moreira, V. Svitkova and K. J. Wilkinson, Pure Appl. Chem., 2023, 95(11), 1159–1196.
12. E. S. Rødland, M. Gustafsson, D. Jaramillo-Vogel, I. Järlskog, K. Müller, C. Rauert, J. Rausch and S. Wagner, TrAC, Trends Anal. Chem., 2023, 165, 117121.
13. J. Wang, Y. Liu, T. Wang, M. A. Serageldin and W.-P. Pan, Fuel, 2024, 355, 129526.
14. S. Krishna and P. Ahuja, Egypt. J. Forensic Sci., 2023, 13(1), 51.
15. W. Wu, L. Zhang, D. Zhu, Z. Shi and B. Jin, J. Anal. At. Spectrom., 2024, 39(6), 1444–1453.
16. Z. R. Zhang, H. Xia, P. S. Sun, R. Q. Yu, X. Yang, Y. Q. Lin, B. Wu, T. Pang, Q. Guo, Z. Li, X. Liu, Y. J. Cai, M. W. Sigrist, B. Calshaw and C. M. Shu, Acta Photonica Sin., 2023, 52(3), 0352108.
17. T. Chen, Y. Ge, B. Chu, P. Zhang, Q. Ma and H. He, TrAC, Trends Anal. Chem., 2024, 172, 117614.
18. C. Crazzolara and A. Held, Atmos. Meas. Tech., 2024, 17(7), 2183–2194.
19. O. Zervaki, D. D. Dionysiou and P. Kulkarni, J. Aerosol Sci., 2023, 174, 106235.
20. N. Gaudel, S. Bau and V. Matera, J. Aerosol Sci., 2024, 181, 106408.
21. N. Allen, J. Gačnik, S. M. Dunham-Cheatham and M. S. Gustin, Atmos. Environ., 2024, 318, 120240.
22. L. Sørensen, M. H. Gerace and A. M. Booth, Camb. Prism., Plast., 2024, 2, e13.
23. B. E. Naes, S. Scott, A. Waldron, S. Lawson, M. G. Bronikowski, L. I. Gleaton, R. J. Smith, K. N. Wurth, T. J. Tenner and M. Wellons, Analyst, 2023, 148(14), 3226–3238.
24. J. Meija, Anal. Bioanal. Chem., 2024, 416, 2013–2021.
25. Y. Liu, J. Liu, J. Xiao, J. Ye, L. Guo and C. Yan, J. Environ. Sci., 2025, 149, 524–534.
26. M. S. Ermolin and P. S. Fedotov, J. Anal. Chem., 2023, 78(9), 1115–1124.
27. D. K. Oliveira, V. H. Cauduro, E. L. M. Flores and E. M. M. Flores, Anal. Chim. Acta, 2024, 1288, 342054.
28. S. Takano, H. Kanamura and Y. Sohrin, ACS Earth Space Chem., 2024, 8(3), 547–553.
29. A. Sanchez-Cachero, N. R. Farinas, M. Jimenez-Moreno and R. C. R. Martin-Doimeadios, Spectrochim. Acta, Part B, 2023, 203, 106665.
30. E. Zambrzycka-Szelewa and B. Godlewska-Zylkiewicz, Spectrochim. Acta, Part B, 2024, 213, 106859.
31. T. D. Andron, W. T. Corns, I. Zivkovic, S. W. Ali, S. Vijayakumaran Nair and M. Horvat, Atmos. Meas. Tech., 2024, 17(4), 1217–1228.
32. H. Moertenkoetter, C. Heilmeier, T. de Riese, S. Fendt and H. Spliethoff, Fuel, 2024, 357, 129939.
33. P. Heikkila, A. Rostedt, J. Toivonen and J. Keskinen, Aerosol Sci. Technol., 2024, 58(9), 1063–1078.
34. Z. Gao, F. Hu, H. He, J. Li, D. Cui, J. Gao and Z. Wang, Microw. Opt. Technol. Lett., 2023, 65(5), 1359–1365.
35. D. J. Palasti, L. Villy, B. Leits, A. Keri, A. Kohut, A. Belteki, G. Kajner, F. A. C. Plaza, E. Kovacs-Szeles, T. Ajtai, M. Veres, Z. Geretovszky and G. Galbacs, Spectrochim. Acta, Part B, 2023, 209, 106804.
36. C. Burgos-Palop, P. Purohit, F. J. Fortes and J. Laserna, Anal. Chem., 2023, 95(39), 14541–14550.
37. C. Vander Pyl, K. Menking-Hoggatt, L. Arroyo, J. Gonzalez, C. Liu, J. Yoo, R. E. Russo and T. Trejos, Spectrochim. Acta, Part B, 2023, 207, 106741.
38. L. Zheng, J. Huang, L. Liu, J. Li, Z. Feng and Y. Zhu, J. Anal. At. Spectrom., 2024, 39(4), 1118–1124.
39. S. Fernandez-Trujillo, M. Jimenez-Moreno, N. Rodriguez-Farinas and R. C. R. Martin-Doimeadios, Anal. Bioanal. Chem., 2024, 416, 2657–2676.
40. T. Cen, L. Torrent, A. Testino and C. Ludwig, J. Aerosol Sci., 2024, 175, 106283.
41. T. Cen, Y.-B. Zhao, A. Testino, J. Wang, L. Torrent and C. Ludwig, J. Aerosol Sci., 2024, 177, 106317.
42. W. M. Geiger, E. McCurdy and M. Kelinske, Spectroscopy, 2023, 38(S9), 12–16.
43. T. Shintani and A. Takeuchi, Anal. Sci., 2024, 40(3), 531–540.
44. A. Sakanupongkul, K. Sirisinha, R. Saenmuangchin and A. Siripinyanond, Microchem. J., 2024, 199, 110016.
45. T. Van Acker, A. Rua-Ibarz, F. Vanhaecke and E. Bolea-Fernandez, Anal. Chem., 2023, 95(50), 18579–18586.
46. T. Vonderach, A. Gundlach-Graham and D. Guenther, Anal. Bioanal. Chem., 2024, 416, 2773–2781.
47. C. Carrier, A. Habibi, L. Ferreux, L. Solier, D. Hebert, C. Augeray, M. Morin, D. Maro and L. Benedetti, J. Radioanal. Nucl. Chem., 2023, 332(6), 2003–2015.
48. S. Strekopytov, J. Entwisle, S. Hill and H. Goenaga-Infante, J. Anal. At. Spectrom., 2024, 39(3), 868–878.
49. L. Hendriks, R. Bruenjes, S. Taskula, J. Kocic, B. Hattendorf, G. Bland, G. Lowry, E. Bolea-Fernandez, F. Vanhaecke, J. Wang, M. Baalousha, M. von der Au, B. Meermann, T. R. Holbrook, S. Wagner, S. Harycki, A. Gundlach-Graham and F. von der Kammer, Nanoscale, 2023, 15(26), 11268–11279.
50. V. C. Bradley, B. W. Ticknor, D. R. Dunlap, N. A. Zirakparvar, S. C. Metzger, C. R. Hexel and B. T. Manard, Anal. Chem., 2023, 95(43), 15867–15874.
51. M. Grotti, M. A. Vecchio, D. Gobbato, M. Mataloni and F. Ardini, At. Spectrosc., 2023, 44(5), 311–317.
52. S. E. Szakas, K. Menking-Hoggatt, T. Trejos and A. Gundlach-Graham, Appl. Spectrosc., 2023, 77(8), 873–884.
53. J. A. H. Alpofead, C. M. Davidson and D. Littlejohn, Anal. Bioanal. Chem., 2023, 415(14), 2831–2848.
54. D. P. O’Loughlin, M. J. Haugen, J. Day, A. S. Brown, E. C. Braysher, N. Molden, A. E. Willis, M. MacFarlane and A. M. Boies, Environ. Int., 2023, 178, 108047.
55. J. S. Lim and Y.-H. Yim, Talanta, 2024, 271, 125627.
56. D. J. Price, A. M. Piasecki, R. U. Shah, K. L. Hayden, J. B. Burkholder, J. M. Roberts and A. M. Middlebrook, Aerosol Sci. Technol., 2023, 57(8), 727–741.
57. F. Bilo, P. Cirelli and L. Borgese, TrAC, Trends Anal. Chem., 2024, 170, 117427.
58. Y. Zhang, Y. Xu, B. Peng, W. Chen, X. Cui, T. Zhang, X. Chen, Y. Yao, M. Wang, J. Liu, M. Zheng and T. Zhu, Atmos. Environ., 2024, 318, 120205.
59. T. Okamoto, A. Iwata, H. Yamanaka, K. Ogane, T. Mori, A. Honda, H. Takano and T. Okuda, Aerosol Air Qual. Res., 2024, 24(3), 230156.
60. Z. Shu, C. Huang, K. Min, C. Long, L. Liu, J. Tan, Q. Liu and G. Jiang, TrAC, Trends Anal. Chem., 2023, 169, 117347.
61. T. A. Sipkens, A. Boies, J. C. Corbin, R. K. Chakrabarty, J. Olfert and S. N. Rogak, J. Aerosol Sci., 2023, 173, 106211.
62. L. Salo, K. Saarnio, S. Saarikoski, K. Teinila, L. M. F. Barreira, P. Marjanen, S. Martikainen, H. Keskinen, K. Mustonen, T. Lepisto, P. Aakko-Saksa, H. Hakkarainen, T. Pfeiffer, P. Jalava, P. Karjalainen, J. Keskinen, N. Kuittinen, H. Timonen and T. Ronkko, Atmos. Pollut. Res., 2024, 15(5), 102088.
63. M. Daoudi, P. Schiffmann, A. Faccinetto, A. Frobert and P. Desgroux, Aerosol Sci. Technol., 2023, 57(9), 872–889.
64. T. Storsjö, H. Tinnerberg, J. Sun, C. Ruiqi and A. Farbrot, NanoImpact, 2024, 33, 100499.
65. H. Koponen, K. Lukkarinen, M. Leppänen, L. Kilpeläinen, S. Väätäinen, P. Jussheikki, A. Karjalainen, J. Ruokolainen, P. Yli-Pirilä, M. Ihalainen, M. Hyttinen, P. Pasanen and O. Sippula, J. Aerosol Sci., 2024, 177, 106330.
66. D. A. Parks, Y. Zhao, P. R. Griffiths and A. L. Miller, Aerosol Sci. Technol., 2024, 58(6), 694–705.
67. J. C. Corbin, D. Clavel and G. J. Smallwood, Aerosol Sci. Technol., 2024, 58(7), 812–829.
68. Y. Aghaei, M. Aldekheel, R. Tohidi, M. M. Badami, V. Jalali Farahani and C. Sioutas, Atmos. Environ., 2024, 319, 120316.
69. D. C. P. King, M. J. J. Watts, E. M. M. Hamilton, R. Mortimer, D. P. A. Kilgour and M. Di Bonito, Environ. Sci.: Process. Impacts, 2023, 25(3), 351–363.
70. D. Wu, Y. Hu, H. Cheng and X. Ye, Molecules, 2023, 28(8), 3601.
71. M. Zhang, H. Song, J. Shang, X. Liu, S. Qi and H. Li, Spectrochim. Acta, Part B, 2023, 207, 106740.
72. N. Qin, J. Zheng, G. Yang and K. Tagami, J. Environ. Radioact., 2024, 271, 107328.
73. M. A. Habila, Z. A. Alothman, M. Sheikh and S. O. Alaswad, Appl. Sci., 2023, 13(18), 10036.
74. S. Samejo, J. A. Baig, T. G. Kazi, H. I. Afridi, A. Hol, A. Dahshan, K. Akhtar, S. A. Solangi, S. Perveen and S. Hussain, Anal. Methods, 2023, 15(31), 3863–3873.
75. M. A. J. Lopes da Costa, M. F. Costa, R. Sorrentino, N. M. F. Carvalho and J. S. de Gois, Talanta, 2023, 268(Pt 1), 125320.
76. J. R. Chen, W. H. Tsai and C. K. Su, Anal. Chim. Acta, 2023, 1271, 341489.
77. C. Tziasiou, A. D. Pournara, M. J. Manos and D. L. Giokas, Microchem. J., 2023, 195, 109387.
78. S. J. Abellan-Martin, D. Villalgordo-Hernandez, M. A. Aguirre, E. V. Ramos-Fernandez, J. Narciso and A. Canals, Separations, 2023, 10(11), 563.
79. M. Soylak, A. H. Kori and H. E. H. Ahmed, At. Spectrosc., 2023, 44(5), 326–335.
80. D. N. Karaman, H. Serbest, Y. Kilinc, R. Demirel and S. Bakirdere, Clean: Soil, Air, Water, 2023, 51(9), 2200186.
81. K. Greda, M. Welna, A. Szymczycha-Madeja and P. Pohl, Microchem. J., 2024, 196, 109715.
82. Y. Surme, G. K. Yildirim, M. Ucan and B. Narin, Microchem. J., 2024, 196, 109648.
83. S. Yilmaz, N. Ullah, D. Citak, B. Hazer and M. Tuzen, J. Food Compos. Anal., 2023, 123, 105487.
84. H. Alsaeedi, H. Ahmad, M. F. Altowairqi, A. A. Alhamed and A. Alsalme, Polymers, 2023, 15(3), 532.
85. J. M. de Sousa, G. F. B. Cruz, L. G. dos Santos and R. J. Cassella, J. Anal. Methods Chem., 2023, 2023, 9624637.
86. M. Zhang, J. Hu, Q. Tang, J. Zhang, X. Jiang and X. Hou, Anal. Chem., 2023, 95(38), 14169–14174.
87. L. Trzonkowska, B. Lesniewska and B. Godlewska-Zylkiewicz, Water, 2022, 14(4), 529.
88. H. E. H. Ahmed and M. Soylak, Water, Air, Soil Pollut., 2024, 235(4), 217.
89. M. Musielak, M. Serda, A. Gagor, E. Talik and R. Sitko, Spectrochim. Acta, Part B, 2024, 213, 106863.
90. Z. Aladaghlo, S. Malekzadeh, A. Sahragard and A. R. Fakhari, J. Food Compos. Anal., 2024, 129, 106097.
91. A. H. Kori, F. Uzcan and M. Soylak, J. Food Compos. Anal., 2023, 122, 105474.
92. P. Cao, V. Pichon, C. Dreanno, K. Boukerma and N. Delaunay, Talanta, 2023, 256, 124295.
93. P. A. M. Baghaei, M. R. A. Mogaddam, M. A. Farajzadeh, A. Mohebbi and S. M. Sorouraddin, J. Food Compos. Anal., 2023, 117, 105125.
94. Y. Vicente-Martinez, M. J. Munoz-Sandoval, M. Hernandez-Cordoba and I. Lopez-Garcia, Molecules, 2023, 28(6), 2549.
95. K. Pytlakowska, K. Kocot, B. Hachu and E. Talik, Talanta, 2024, 266, 124997.
96. J. Acuna-Nicolas, T. Montesinos-Vazquez, I. Perez-Silva, C. A. Galan-Vidal, I. S. Ibarra and M. E. Paez-Hernandez, Polymers, 2023, 15(14), 3086.
97. A. Sharifi, R. Hallaj and S. Bahar, J. Anal. Methods Chem., 2023, 2023, 5424221.
98. O. Ozalp and M. Soylak, Talanta, 2023, 253, 124082.
99. F. K. Algethami, Silicon, 2023, 15(14), 5891–5909.
100. Q. Wang, Y. L. Nie, Z. B. Wu and Y. Yang, Anal. Methods, 2023, 15(36), 4758–4766.
101. Y. Li, J. Hu, C. Li and X. Hou, Anal. Chem., 2024, 96(15), 5757–5762.
102. E. Kurahashi, S. Poehle and A. Koschinsky, Deep Sea Res., Part I, 2024, 206, 104261.
103. A. Justo-Vega, R. Dominguez-Gonzalez, P. Bermejo-Barrera and A. Moreda-Pineiro, J. Anal. At. Spectrom., 2024, 39(2), 573–581.
104. D. Lledo, G. Grindlay, R. Serrano, L. Gras and J. M. Sansano, Spectrochim. Acta, Part B, 2023, 204, 106672.
105. N. Mafakheri, M. Shamsipur and N. Babajani, Microchem. J., 2024, 199, 110010.
106. P. R. Martin, D. Buchner, M. A. Jochmann and S. B. Haderlein, Anal. Methods, 2024, 16(6), 919–929.
107. M. R. Jamali, J. M. Nakhaei, S. Sohrabnezhad and R. Rahnama, J. Anal. Chem., 2023, 78(8), 980–987.
108. S. V. Smirnova, I. V. Mikheev and V. V. Apyari, Talanta, 2024, 269, 125504–125504.
109. O. Sagsoez, B. Arvas, B. T. Zaman, C. Yolacan and S. Bakirdere, Environ. Monit. Assess., 2023, 195(2), 313.
110. F. A. S. Cunha, J. P. A. Fernandes, W. S. Lyra, A. G. G. Pessoa, J. C. C. Santos, M. C. U. Araujo and L. F. Almeida, J. Anal. At. Spectrom., 2024, 39(5), 1398–1404.
111. S. E. O’Sullivan, S. S. Jensen, G. N. Nikolajsen, H. Z. Bruun, R. Bhuller and J. Hoeng, J. Water Chem. Technol., 2023, 45(3), 256–269.
112. E. Margui and L. Torrent, Spectrochim. Acta, Part B, 2023, 204, 106682.
113. P. Conchado-Amado, J. Sanchez-Pinero, J. Moreda-Pinero, I. Turnes-Carou, P. Lopez-Mahia and S. Muniategui-Lorenzo, Spectrochim. Acta, Part B, 2023, 204, 106683.
114. N. Altunay, M. F. Lanjwani, M. Tuzen and G. Boczkaj, J. Mol. Liq., 2024, 396, 124000.
115. P. Montoro-Leal, J. C. Garcia-Mesa, I. Morales-Benitez, L. Vazquez-Palomo, M. D. L. Guerrero and E. V. Alonso, Microchim. Acta, 2023, 190(8), 296.
116. F. Guo, P. Zeng, J. Liu, H. Hu, W. Zhu, Y. Wang and H. Cheng, Anal. Chim. Acta, 2024, 1294, 342294.
117. D. Spanu, S. Fantinuoli, G. Binda, L. Rampazzi, D. Monticelli and S. Recchia, Spectrochim. Acta, Part B, 2023, 207, 106762.
118. Y. Chen, Q. Zhang, L. Zhang, Y. Wang, Y. Li, Y. Yin and Y. Cai, J. Chromatogr. A, 2023, 1712, 464472.
119. F. Pirade, K. Lompe, J. Jimenez-Lamana, S. Bandyopadhyay, K. Zuerbes, N. Bali, D. Materic and J. W. Foppen, Aqua, 2023, 72(12), 2347–2357.
120. L. Hendriks and D. M. Mitrano, Environ. Sci. Technol., 2023, 57(18), 7263–7272.
121. S. Harycki and A. Gundlach-Graham, Anal. Chem., 2023, 95(41), 15318–15324.
122. T. Vonderach, A. Gundlach-Graham and D. Guenther, Anal. Bioanal. Chem., 2024, 416(11), 2773–2781.
123. C. Trujillo, J. Perez-Arantegui, R. Lobinski and F. Laborda, Nanomaterials, 2023, 13(10), 1582.
124. C. Neuper, M. Šimić, T. E. Lockwood, R. Gonzalez de Vega, U. Hohenester, H. Fitzek, L. Schlatt, C. Hill and D. Clases, Anal. Chem., 2024, 96(21), 8291–8299.
125. K. Patidar, B. Ambade, F. Mohammad and A. A. Soleimanc, Phys. Chem. Earth, 2023, 131, 103448.
126. J. Raymond, M. Felipe-Sotelo, R. Bance-Soualhi, C. Crean and M. Al-Sid-Cheikh, Environ. Sci.: Nano, 2024, 11(1), 241–252.
127. M. Baalousha, J. Wang, M. M. Nabi, M. Alam, M. Erfani, J. Gigault, F. Blancho, M. Davranche, P. M. Potter and S. R. Al-Abed, Environ. Sci.: Nano, 2024, 11(1), 373–388.
128. M. A. Gomez-Gonzalez, T. Da Silva-Ferreira, N. Clark, R. Clough, P. D. Quinn and J. E. Parker, Global Challenges, 2023, 7(8), 2300036.
129. M. Mozafarjalali, A. H. Hamidian and M. H. Sayadi, Chemosphere, 2023, 324, 138332.
130. J. Song, R. Zheng, R. Yang, S. Yu, J. Xiao and J. Liu, Anal. Bioanal. Chem., 2024, 416, 3271–3280.
131. T. Nxumalo, A. Akhdhar, V. Mueller, F. Simon, M. von der Au, A. Cossmer, J. Pfeifer, E. M. M. Krupp, B. Meermann, A. Kindness and J. Feldmann, Anal. Bioanal. Chem., 2023, 415, 1195–1204.
132. L. A. Oliveira, J. L. O. Santos and L. S. G. Teixeira, Talanta, 2024, 266, 124945.
133. L. D. Chen, Q. L. Lin, D. X. Yuan and Z. B. Gong, Anal. Chem., 2023, 95(42), 15621–15627.
134. Q. He, C. Li and J. Zhang, Spectrochim. Acta, Part B, 2023, 206, 106728.
135. L. Suarez-Criado, E. Bolea-Fernandez, L. Abou-Zeid, M. Vandermeiren, P. Rodriguez-Gonzalez, J. I. Garcia Alonso and F. Vanhaecke, J. Anal. At. Spectrom., 2024, 39(2), 592–600.
136. P. S. M. Rocha, A. S. Araujo and R. J. Cassella, Anal. Methods, 2023, 15(36), 4674–4683.
137. X. Deng, L. Dong, H. Chen, W. Wang, Y. Yu and Y. Gao, Anal. Chem., 2024, 96(2), 652–660.
138. K. P. Hobbs, A. D. French, K. M. Melby, E. J. Bylaska, K. Harouaka, R. M. Cox, I. J. Arnquist and C. L. Beck, Anal. Chem., 2024, 96(15), 5807–5814.
139. A. D. French, K. M. Melby, K. P. Hobbs, R. M. Cox, G. Eiden, E. W. Hoppe, I. J. Arnquist and K. Harouaka, Talanta, 2024, 272, 125799.
140. N. Belkouteb, H. Schroeder, J. G. Wiederhold, T. A. Ternes and L. Duester, Anal. Bioanal. Chem., 2024, 416, 3205–3222.
141. Y. Y. Ni, W. T. Bu, K. Xiong, S. Hu, C. T. Yang and L. G. Cao, Talanta, 2023, 262, 124710.
142. V. Kinnunen, J. Frimodig, S. Peramaki and R. Matilainen, Spectrochim. Acta, Part B, 2023, 203, 106662.
143. C. W. Zhu, Y. Z. Wu, H. J. Wen, G. H. Wu, W. R. Song, Y. X. Zhang and B. Zhao, Acta Geochim., 2023, 42, 934–942.
144. Y. Xia, Q. Deng, G. Sun, Y. Wu, S. Qiao, J. Ali, X. Fu and X. Feng, J. Anal. At. Spectrom., 2023, 38(2), 359–368.
145. P. D. Barman, A. K. Maurya, M. Madaan, B. Kumar, A. Roy and S. Ghosh, Anal. Sci., 2024, 40, 309–317.
146. K. Sanyal, S. Dhara, N. Gumber and R. V. Pai, Talanta, 2023, 254, 124129.
147. K. Sanyal, A. Saha, A. Sarkar, S. B. Deb, R. V. Pai and M. K. Saxena, ACS Omega, 2023, 8(44), 41402–41410.
148. X. D. Liu, Y. Shao, M. Luo, L. L. Ma, G. Xu and M. H. Wu, Processes, 2023, 11(5), 1430.
149. S. M. Dowell, O. S. Humphrey, W. H. Blake, O. Osano, S. Chenery and M. J. Watts, Chem. Afr., 2023, 6, 2429–2444.
150. K. Shi, X. Tu, Y. Hu and X. Zhang, Appl. Spectrosc. Rev., 2024, 59(4), 483–518.
151. M. Sager and O. Wiche, Environments, 2024, 11(2), 24.
152. B. S. de Almeida, L. Fedele, M. D’Antonio, V. Morra, M. Mercurio, R. Stevenson and D. Widory, Isot. Environ. Health Stud., 2023, 59(4–6), 327–348.
153. Y.-s. Guo, T.-t. Zuo, A.-z. Chen, Z. Wang, H.-y. Jin, F. Wei, P. Li and S.-c. Ma, Chin. Med., 2023, 18(1), 73.
154. C. Peng, J. Zhou, C. Li, Y. Chen, Q. Huo and F. Xie, Open Chem., 2022, 20(1), 23–39.
155. C. N. Peng, L. P. Kang, X. Yuan, J. Q. Qiao, Y. L. Fan, L. Yao, K. L. Qi, Y. X. Sun, X. L. Dai, Y. Zhang and Q. Huo, Processes, 2023, 11(10), 2821.
156. Q. Li, J. J. Chang, L. F. Li, X. Y. Lin and Y. C. Li, Sci. Total Environ., 2023, 905, 167257.
157. Q. H. Zhong, L. Yin, J. Li, Y. X. Feng, N. P. Shen, B. Y. Peng and Z. Y. Wang, J. Anal. At. Spectrom., 2023, 38(11), 2291–2301.
158. S.-A. Liu, D. Li, W. Liu, T. Wu and C. Yang, Rapid Commun. Mass Spectrom., 2023, 37(17), e9606.
159. M.-Y. He, T. X. Ren, Z. D. Jin, L. Deng, H. J. Liu, Y. Y. Cheng, Z. Y. Li, X. X. Liu, Y. Yang and H. Chang, Spectrochim. Acta, Part B, 2023, 209, 106781.
160. R. Guo, H. M. Yu, S. B. Fang, Z. C. Xiao and F. Huang, J. Anal. At. Spectrom., 2023, 38(11), 2365–2377.
161. Z. Zeng and F. Wu, J. Anal. At. Spectrom., 2024, 39(1), 121–130.
162. H. Y. Zhai, X. C. Wang, C. F. Li, S. A. Wilde, X. Z. Li, B. Xu, X. L. Zhang and P. Zhang, RSC Adv., 2023, 13(28), 19030–19038.
163. S. Jahangir, Y. Liu, M. Noureen, J. Cui and Z. Wei, Isot. Environ. Health Stud., 2023, 35(1), 2263641.
164. F. Hallwirth, H. Motter and H. Wiltsche, J. Anal. At. Spectrom., 2023, 38(11), 2378–2386.
165. W. Z. Zhao, L. M. Xu, H. L. Wang, T. He and X. Xie, Anal. Sci., 2024, 40, 263–270.
166. K. Parvizzad, M. A. Farajzadeh and S. M. Sorouraddin, Anal. Sci., 2023, 39, 1493–1499.
167. R. Mishra, S. P. Datta, D. Golui, M. C. Meena, B. S. Dwivedi, M. M. Rahman, K. Bandyopadhyay, A. Bhatia and P. S. S. Pandey, Commun. Soil Sci. Plant Anal., 2023, 54(17), 2459–2474.
168. E. V. Shabanova, A. A. Zak and I. E. Vasil’eva, J. Anal. Chem., 2024, 79(3), 287–302.
169. A. L. Vieira, G. G. A. de Carvalho, J. A. Gomes Neto, P. V. Oliveira, M. Y. Kamogawa and A. Virgilio, J. Anal. At. Spectrom., 2024, 39(2), 356–363.
170. J. B. Pereira Jr, V. S. Carvalho, W. Q. Ferreira, R. G. O. Araujo and S. L. C. Ferreira, J. Pharm. Biomed. Anal., 2024, 238, 115810.
171. Z. Arslan and H. Lowers, Minerals, 2024, 14(3), 299.
172. L. Han, Y. Li, D. Xu, L. Gao and B. Gao, Sci. Total Environ., 2023, 865, 161304.
173. C. Moens, E. Lombi, D. L. Howard, S. Wagner, J. L. Payne, P. M. Kopittke and C. L. Doolette, Environ. Sci. Technol., 2024, 58(1), 440–448.
174. D. Uhlig, T. Goldberg, D. A. Frick and F. von Blanckenburg, Anal. Sci. Adv., 2020, 1(1), 8–21.
175. Y. Yang, M. Luo, Y. Wu, N. Yuan, J. He, H. Guo and Q. An, J. Environ. Radioact., 2023, 270, 107290.
176. V. H. Thien and N. Van Dong, Vietnam J. Chem., 2023, 61(S2), 66–72.
177. D. Moema, T. A. Makwakwa, H. N. Nyambaka, S. Dube and M. M. Nindi, Food Anal. Methods, 2024, 17, 464–474.
178. B. Kartoglu, S. Bodur, D. Zeydanli, T. Gover, E. Ozaydin, E. Gulhan Bakirdere and S. Bakirdere, Food Chem., 2024, 439, 138140–138140.
179. M. S. Dundar, S. Arpaozu, C. Caner, H. Altundag, O. Gulec and M. Arslan, J. Chem. Metrol., 2024, 18(1), 60–70.
180. P. Chaikhan, Y. Udnan, R. J. Ampiah-Bonney and W. C. Chaiyasith, J. Food Compost. Anal., 2023, 118, 105214.
181. N. M. Muslima, B. K. Hussainb, N. M. Abdulhusseinc and E. A. Azoozd, Anal. Bioanal. Chem. Res., 2024, 11(1), 11–22.
182. F. L. F. da Silva, F. E. H. Lima, D. M. A. Neto, F. L. de Menezes, L. M. U. D. Fechine, A. Akhdhar, L. P. D. Ribeiro, A. R. A. Nogueira, P. B. A. Fechine, G. S. Lopes and W. O. Matos, Food Chem., 2023, 412, 135461.
183. M. Soylak, F. Uzcan, O. Goktas and Z. P. Gumus, Food Chem., 2023, 429, 136855.
184. H. E. H. Ahmed, Z. S. Seddigi and M. Soylak, At. Spectrosc., 2024, 45(1), 44–55.
185. S. Tokalioglu, S. Shahir, Y. Yilmaz and S. Patat, Talanta, 2024, 266, 125002.
186. Y. Wang, Y. Lin, T. Ren, Y. Yang, Z. He, Y. R. Deng and C. B. Zheng, Anal. Chem., 2023, 95(29), 10873–10878.
187. S. Tokalioglu, S. T. H. Moghaddam, Y. Yilmaz and S. Patat, Microchem. J., 2023, 195, 109515.
188. D. G. Da Silva, L. N. Pires, A. L. Costa Pereira and F. S. Dias, Int. J. Environ. Anal. Chem., 2023, 103(19), 8454–8464.
189. H. E. H. Ahmed, A. M. A. Mohammed and M. Soylak, Food Chem., 2023, 428, 136794.
190. M. Khan, R. Alosmanov, K. Wolski, S. Zapotoczny and M. Soylak, Anal. Lett., 2024, 57(3), 327–341.
191. F. T. Abdelwahed, W. I. Mortada, M. M. El-Defrawy and R. M. Eltabey, J. Food Compost. Anal., 2024, 129, 106119.
192. N. N. Meeravali, K. Madhavi and A. C. Sahayam, Spectrochim. Acta, Part B, 2023, 200, 106613.
193. L. Chirita, E. Covaci, M. Ponta and T. Frentiu, Anal. Methods, 2023, 15(45), 6294–6301.
194. L. Chirita, T. Frentiu, M. Ponta and E. Covaci, Anal. Lett., 2024, 57(10), 1526–1543.
195. M. Aramendia, A. L. M. de Souza, F. V. Nakadi and M. Resano, J. Anal. At. Spectrom., 2024, 39(3), 767–779.
196. F. J. Xu, Y. Z. Liu, X. L. Liu, J. Tan, H. Xia and M. T. Li, Microchem. J., 2023, 193, 109109.
197. A. Orlowska, J. Proch and P. Niedzielski, Molecules, 2023, 28(11), 4539.
198. S. B. Angyus, M. Senila, E. Covaci, M. Ponta, M. Frentiu and T. Frentiu, J. Anal. At. Spectrom., 2024, 39(1), 141–152.
199. D. Stoitsov, V. Kmetov and A. Canals, J. Anal. At. Spectrom., 2023, 38(10), 1920–1926.
200. R. Serrano, G. Grindlay, L. Gras and J. Mora, Talanta, 2024, 271, 125666.
201. J. Naozuka, A. P. Oliveira and C. S. Nomura, Anal. Bioanal. Chem., 2024, 416(11), 2605–2623.
202. Q. T. Nguyen, T. T. Nguyen, V. N. Le, N. T. Nguyen, N. M. Truong, M. T. Hoang, T. P. T. Pham and Q. M. Bui, Foods, 2023, 12(9), 1848.
203. A. Kierulf and D. Beauchemin, J. Anal. At. Spectrom., 2023, 38(9), 1772–1784.
204. J. Dong, M. Chen, L. Li, P. Xing, S. Li, Z. Zhang, J. Zhang, J. Liu, X. Liu, W. Zhang, H. Tian, H. Zheng and Z. Zhu, J. Anal. At. Spectrom., 2023, 38(11), 2414–2423.
205. G. Y. Lan, X. Li, J. J. Yao, X. F. Yu, Q. H. Liu, C. Qiu and X. F. Mao, Front. Nutr., 2023, 10, 1201801.
206. Y. Yuan, Z. Han, F. Yang, H. Yu, Y. Zhang and M. Wen, Geostand. Geoanal. Res., 2024, 48(1), 133–143.
207. R. Sim, M. Weyer and A. H. Petursdottir, Anal. Bioanal. Chem., 2024, 416, 3033–3044.
208. M. Jablonska-Czapla, K. Grygoyc, M. Rachwal, A. Fornalczyk and J. Willner, J. Soils Sediments, 2023, 23, 3362–3375.
209. C. Dewey, D. I. Kaplan, S. Fendorf and R. M. Boiteau, Anal. Chem., 2023, 95(20), 7960–7967.
210. R. Yang, R. Zheng, J. Song, H. Liu, S. Yu and J. Liu, Anal. Chem., 2024, 96(1), 471–479.
211. P. Becker, T. Nauser, M. Wiggenhauser, B. Aeschlimann, E. Frossard and D. Gunther, Anal. Chem., 2024, 96(12), 4952–4959.
212. Z. W. Cui, M. He, B. B. Chen and B. Hu, Anal. Chim. Acta, 2023, 1275, 341588.
213. S. Braeuer, T. Van Helden, T. Van Acker, O. Leroux, D. Van Der Straeten, A. Verbeken, J. Borovicka and F. Vanhaecke, Anal. Bioanal. Chem., 2022, 414(25), 7517–7530.
214. D. d. S. Francischini and M. A. Z. Arruda, Anal. Bioanal. Chem., 2024, 416(11), 2737–2748.
215. G. Marolt, S. Novak, A. J. Kokalj, I. Talaber, V. Kononenko, S. Loureiro, Z. Khodaparast, P. V. Silva, M. B. Fité, R. D. Handy and D. Drobne, J. Anal. At. Spectrom., 2023, 38(11), 2396–2404.
216. Y. Wang, J.-j. Bai, Y.-j. Wei, C.-X. Zhao, Z. Shao, M.-L. Chen and J.-H. Wang, J. Hazard. Mater., 2024, 465, 133029.
217. Z. Huang, X. L. Hou, J. X. Qiao and X. Zhao, Talanta, 2023, 265, 124798.
218. N. Takata, A. Botha and P. N. Nomngongo, Anal. Lett., 2024, 57(14), 2200–2214.
219. C. Gomez-Pertusa, M. C. Garcia-Poyo, G. Grindlay, R. Pedraza, M. A. Yanez and L. Gras, Talanta, 2024, 272, 125742.
220. Z. Li, M. Hadioui and K. J. J. Wilkinson, Nanomaterials, 2023, 13(14), 2049.
221. K. Loeschner, M. E. Johnson and A. R. Montoro Bustos, Nanomaterials, 2023, 13(18), 2547.
222. H. Cui, Z. Zhang, T. Wang, J. Hong, L. Lei and S. Wei, At. Spectrosc., 2023, 44(5), 343–353.
223. J. Jimenez-Lamana, K. Bierla, A. Leskova, C. Curie, J. Szpunar and S. Mari, J. Anal. At. Spectrom., 2023, 38(8), 1560–1563.
224. C. Song, P. Lin, S. Ma, F. Xu, X. Wen, X. Fu and D. Dong, J. Anal. At. Spectrom., 2024, 39(4), 1087–1093.
225. C. Pan, N. Zhao, M. Ma, R. Yang, J. Yang and J. Liu, Laser Optoelectron. Prog., 2023, 60(17), 1730006.
226. H. Y. Jin, X. J. Hao and B. M. Mo, J. Anal. At. Spectrom., 2023, 38(11), 2280–2290.
227. H. L. Li, C. Wang, Y. T. Wang, S. J. Fu and L. D. Fang, Meas. Sci. Technol., 2023, 34(9), 095204.
228. S. Soni, J. Viljanen, R. Uusitalo and P. Veis, Heliyon, 2023, 9(6), e17523.
229. J. Xu, X. Wang, M. Y. Yao and M. H. Liu, J. Anal. At. Spectrom., 2023, 38(11), 2441–2447.
230. S. Mohajan, Y. Huang, N. F. Beier, M. Dyck, F. Hegmann, A. Bais and A. E. Hussein, Opt. Express, 2023, 31(20), 32335–32349.
231. W. N. Guedes, D. V. Babos, V. C. Costa, C. P. De Morais, V. D. Freitas, K. Stenio, A. A. P. Xavier, L. C. L. Borduchi, P. R. Villas-Boas and D. Milori, J. Opt. Soc. Am. B: Opt. Phys., 2023, 40(5), 1319–1330.
232. M. Ma, L. Fang, N. Zhao and X. Ma, Chemosensors, 2024, 12(3), 40.
233. O. A. Nassef and Y. E. E. D. Gamal, Soil Sediment Contam., 2023, 32(8), 970–993.
234. V. Dwivedi, J. Ahokas, J. Viljanen, P. Ryczkowski, N. J. Shurpali, H. R. Bhattarai, P. Virkajarvi and J. Toivonen, Geoderma, 2023, 436, 116550.
235. D. V. Babos, W. N. Guedes, V. S. Freitas, F. P. Silva, M. L. d. L. Tozo, P. R. Villas-Boas, L. Martin-Neto and D. M. B. P. Milori, Front. Soil Sci., 2024, 3, 1242647.
236. A. Erler, D. Riebe, T. Beitz, H.-G. Loehmannsroeben, M. Leenen, S. Paetzold, M. Ostermann and M. Wojcik, Sensors, 2023, 23(16), 7178.
237. X. Li, J. Huang, R. Chen, Z. You, J. Peng, Q. Shi, G. Li and F. Liu, J. Hazard. Mater., 2023, 448, 130885.
238. H. Yang, Y.-T. Jo, J. Chong, Y. Won and J.-H. Park, Soil Res., 2024, 62(3), Sr23248.
239. A. Fayyaz, N. Ali, Z. A. Umar, H. Asghar, M. Waqas, R. Ahmed, R. Ali and M. A. Baig, Anal. Sci., 2024, 40, 413–427.
240. Q. Zhao, Y. Yu, P. Cui, N. Hao, C. Liu, P. Miao and Z. Li, Spectrochim. Acta, Part A, 2023, 287, 122053.
241. T. A. Alrebdi, A. Fayyaz, H. Asghar, A. Kamal, J. Iqbal and N. K. Piracha, Appl. Sci., 2023, 13(5), 2780.
242. J. Peng, Y. Liu, L. Ye, J. Jiang, F. Zhou, F. Liu and J. Huang, Sci. Total Environ., 2023, 860, 160545.
243. J. V. Borges Assis, D. d. S. Ferreira, D. d. A. Bocoli, C. H. Hoff Brait and E. R. Pereira-Filho, Appl. Spectrosc., 2024, 78(2), 243–250.
244. D. D. Pace, J. Molina and C. I. Rodriguez, Chemosensors, 2024, 12(2), 18.
245. L. Smieska, M. L. Guerinot, K. O. Hoal, M. Reid and O. Vatamaniuk, Metallomics, 2023, 15(8), mfad041.
246. M. V. Kirichkov, V. A. Polyakov, S. S. Shende, T. M. Minkina, D. G. Nevidomskaya, M. H. Wong, T. V. Bauer, V. A. Shuvaeva, S. S. Mandzhieva and V. S. Tsitsuashvili, Chemosphere, 2024, 349, 140782–140782.
247. S. An, D. Krapohl, C. Gonzalez, S. Rydblom, B. Norlin and G. Thungstrom, X-Ray Spectrom., 2023, 52(4), 182–196.
248. E. Santos, J. D. Gozetto, E. de Almeida, M. A. S. Brasil, N. G. d. C. da Silva, V. P. Rezende, H. J. F. A. da Silva, J. R. Brandao, G. S. Montanha, J. Lavres and H. W. P. de Carvalho, ACS Agric. Sci. Technol., 2024, 4(4), 471–477.
249. U. Weis, B. Stoll, J. Brettschneider, D. Ernst, E. Winkes and K. P. Jochum, Geostand. Geoanal. Res., 2023, 47(4), 983–989.
250. M. Yang, Y. H. Yang, R. L. Romer, X. D. Che, R. C. Wang, F. Y. Wu, G. C. Fei, Y. Deng and T. Wu, J. Anal. At. Spectrom., 2023, 38(9), 1816–1829.
251. Y. Tang, T. Lan, J. Gao, Z. Bai, X. Huang, J. Han and N. Liu, J. Anal. At. Spectrom., 2024, 39(1), 109–120.
252. L. Jia, X. Ma, S. Wu, Y. Li, J. Yuan, Q. Mao, X. Li and Y. Chen, Geostand. Geoanal. Res., 2024, 48(1), 207–225.
253. J. M. M. Allaz, M. Guillong, L. Tavazzani, G. Spiekermann, L. Zehnder, E. Bullock, J. DesOrmeau, M. J. J. Jercinovic, J. Krause, F. Marxer, W. O. O. Nachlas and J. Spratt, Geostand. Geoanal. Res., 2023, 47(4), 907–929.
254. Q. Chen, R.-J. Wang, H.-X. Yu, G. Zhu, Y.-Q. Zhang, X.-P. Xia, Z.-Y. Ren and L. Zhang, J. Anal. At. Spectrom., 2024, 39(3), 917–924.
255. J. Zhang, C. Li, X. Li, W. Wang, P. Yu, L. Zhou and W. Qu, J. Anal. At. Spectrom., 2024, 39(3), 800–807.
256. J. Lu, W. Chen, H.-Y. Jin, J. Jiang, J. Lin, A. Yang, M. Li, K.-D. Zhao, S.-Y. Jiang and Y.-S. Liu, J. Anal. At. Spectrom., 2024, 39(3), 820–838.
257. Y. Luo, J. Wang, Y. Xiao, W.-J. Li, S.-Y. Wang, Z.-Y. Ding, Y.-H. Liu, H.-Y. Zhang and B.-X. Su, Spectrochim. Acta, Part B, 2023, 209, 106798.
258. Y. Feng, W. Zhang, Z. Hu, T. Luo, Q. Li and J. Liu, Geostand. Geoanal. Res., 2024, 48(1), 227–244.
259. L. Xu, J.-H. Yang, H. Wang, H. Ye, L.-W. Xie, Y.-H. Yang, C. Huang and S.-T. Wu, J. Anal. At. Spectrom., 2024, 39(3), 900–916.
260. L.-W. Xie, X.-J. Wang, H.-M. Yu, J.-F. Gao, L. Xu, C. Huang, G.-Q. Tang, Q. Mao, L.-J. Feng, Y.-H. Yang, S.-T. Wu and H. Wang, J. Anal. At. Spectrom., 2024, 39(3), 723–734.
261. O. V. Yakubovich, F. M. Stuart, E. S. Ivanova and F. Gervilla, Geostand. Geoanal. Res., 2023, 47(4), 957–968.
262. Y. Sano, Y. Koyama, N. Takahata, T. Matsuzaki, M. Koike, M. K. Haba, S. Sakata, H. Kuwahara and T. Irifune, J. Anal. Sci. Technol., 2024, 15(1), 24.
263. Q.-D. Wei, Y.-H. Yang, H. Wang, S.-T. Wu, M. Yang, C. Huang, L. Xu, L.-W. Xie, J.-H. Yang and F.-Y. Wu, J. Anal. At. Spectrom., 2024, 39(5), 1284–1301.
264. S. Schmidt, A. Hertwig, A. K. Schmitt, K. Cionoiu, K. D. McKeegan, I. Bindeman, T. Di Rocco and A. Pack, J. Anal. At. Spectrom., 2024, 39(2), 439–446.
265. Q.-Q. Pan, J. Wang, Y. Xiao, B. Fu, Y. Bai, I. S. Williams and B.-X. Su, Geostand. Geoanal. Res., 2024, 48(2), 363–379.
266. L.-L. Tian, Y. Guan, W.-L. Xie, K. Xu, F.-T. Tong, T. Yang, Y.-B. Peng and X.-L. Wang, J. Anal. At. Spectrom., 2024, 39(4), 1110–1117.
267. S. He, Y. Li, L.-G. Wu, R.-P. Liu, D.-F. Guo, Z.-Y. Li and X.-H. Li, Surf. Interface Anal., 2023, 56(3), 136–146.
268. B. Li, M. Wiedenbeck, F. Couffignal, A. M. Alvarez-Valero, H.-M. Bao, C.-F. Fan, J. Han, G.-S. Jin, Y.-B. Peng, M. D. Syczewski, K. T. Tait, F. D. H. Wilke and U. G. Wortmann, Geostand. Geoanal. Res., 2024, 48(1), 179–205.
269. R. Tamblyn, S. Gilbert, S. Glorie, C. Spandler, A. Simpson, M. Hand, D. Hasterok, B. Ware and S. Tessalina, Geostand. Geoanal. Res., 2024, 48(2), 393–410.
270. Y. Zhang, S. You, F. Tan, J. Shao, J. Zhou, T. Zheng, W. Yi, P. Xia, L. Jin, H. Li, R. Wang, H. Wen, Y. Ke and Y. Sun, Geostand. Geoanal. Res., 2023, 47(4), 773–792.
271. Y. Chen, L. Chen, G. Tang, Z. Bao, Z. Cui, X. Qiu, Z.-Q. Chen, J. Gu, S. Dong and J. Gao, At. Spectrosc., 2023, 44(3), 131–141.
272. Z.-h. Dai, S.-l. Fu, Y.-f. Liu, Y.-m. Meng, Z.-a. Bao, K.-j. Hou and T.-g. Lan, J. Anal. At. Spectrom., 2024, 39(1), 216–226.
273. N. Lv, Z. Bao, X. Nie, K. Chen, Y. Zhang and H. Yuan, Geostand. Geoanal. Res., 2024, 48(2), 411–421.
274. W. Wang, C. Li, B. Zu, J. Zhang, P. Yu, H. Ren, X. Li, L. Zhou and W. Qu, At. Spectrosc., 2024, 45(1), 33–43.
275. S. Wu, Y. Yang, T. Li, C. Huang, Z. Bao, Y. Li, C. Li, L. Xu, H. Wang, L. Xie, J. Yang and F. Wu, J. Anal. At. Spectrom., 2023, 38(12), 2528–2537.
276. J. Mulder, G. Hagen-Peter, T. Ubide, R. Andreasen, E. Kooijman, M. Kielman-Schmitt, Y.-X. Feng, B. Paul, A. Karlsson, C. Tegner, C. Lesher and F. Costa, Geostand. Geoanal. Res., 2023, 47(2), 311–336.
277. H. Liu, Z. Deng, Z. Hu, W. Zhang, M. Schiller, M. Bizzarro, Y. Liu, Z. Wang, L. Feng and M. Li, Geostand. Geoanal. Res., 2024, 48(1), 269–287.
278. X. Yin, M. He, L. Zhang, G. Zhu, W. Deng and G. Wei, J. Anal. At. Spectrom., 2024, 39(5), 1277–1283.
279. A. K. Kennedy, J.-F. Wotzlaw, J. L. Crowley, M. Schmitz, U. Schaltegger, B. Wade, L. Martin, C. Talavera, B. Ware and T. H. Bui, Geostand. Geoanal. Res., 2023, 47(2), 373–402.
280. N. Aysal, M. Guillong, T. Bayanova, M. Fukuyama, N. Leonard, I. Yilmaz, E. Varol, F. S. Tukel, Y. K. Kadioglu, N. Hanilci, F. Uzun and E. Kaygisiz, Geostand. Geoanal. Res., 2023, 47(2), 297–310.
281. L.-J. Duan, L.-L. Zhang, D.-C. Zhu, J.-C. Xie, Q. Wang, S.-T. Wu, W.-T. Xu, S. Kamo and G.-H. Shi, Int. J. Mass Spectrom., 2023, 493, 117121.
282. C. Huang, H. Wang, J.-H. Yang, S. L. Kamo, J.-R. Tu, Z.-H. Hou, L. Zhang, Y.-H. Yang, L.-W. Xie, S.-T. Wu and L. Xu, Geostand. Geoanal. Res., 2024, 48(1), 245–267.
283. L.-L. Zhang, D.-C. Zhu, J.-C. Xie, Q. Wang, S. Kamo, H. Rochin-Banaga and Y. Xiao, J. Anal. At. Spectrom., 2023, 38(11), 2302–2312.
284. D. Gallhofer, E. Skrzypek, C. Hauzenberger, W. Kurz, A. Moller, J. Andrew, L. A. Parra-Avila, L. Martin, A. Kemp, G. W. A. R. Fernando and D. He, Geostand. Geoanal. Res., 2023, 47(4), 749–771.
285. L. Wu, J. An, F. Wang, L. Yang, X. Shen, Z. Zhang, C. Huang, R. Ren, B. Liu, W. Shi and Y. Wang, J. Anal. At. Spectrom., 2023, 38(3), 642–655.
286. D. Willingham, J. Wimpenny, E. Groopman, T. Williamson, T. Tenner, B. Naes, M. Zimmer, H. Cunningham and K. Knight, Geostand. Geoanal. Res., 2023, 47(4), 893–906.
287. Y. Tang, N. Liu, J. Yang, G. O. Goncalves, L. Liu, T. Lan, J. Gao and J. Han, J. Anal. At. Spectrom., 2022, 37(12), 2599–2614.
288. X.-W. Huang, S. Lin, K. Chen, J.-T. Kang, Z.-H. Li, Y.-M. Meng and L. Qi, J. Anal. At. Spectrom., 2024, 39(4), 1043–1049.
289. P. J. Potts, P. C. Webb, C. J. B. Gowing and A. D. Renno, Geostand. Geoanal. Res., 2024, 48(3), 697–706.
290. D. Sun, C. Fan, W. Guo, L. Zhao, X. Zhan and M. Hu, Geostand. Geoanal. Res., 2024, 48(3), 677–695.
291. Y. Fang, Q.-Y. Wen, Z.-C. Xiao, H.-M. Yu and F. Huang, J. Anal. At. Spectrom., 2024, 39(7), 1750–1758.
292. A. N. Paul, J. A. Stewart, S. Agostini, L. A. Kirstein, J. C. M. De Hoog, I. P. Savov, K. Walowski, J. W. B. Rae and T. Elliott, Geostand. Geoanal. Res., 2024, 48(1), 91–108.
293. F. Liu, M. Ling, Z. Zhang, W. Lu, J. Xu, X. Li, D. Yang, J. Wu and H. Yang, Chem. Geol., 2023, 637, 121664.
294. Q. Ma, Y. H. Yang and Z. M. Yang, Geostand. Geoanal. Res., 2023, 47(4), 869–891.
295. A. Yang, J. Lin, T. Zhang, X. Zhu, R. Lin, K. Deng, Z. Liu, Z. Hu and Y. Liu, At. Spectrosc., 2023, 44(5), 365–374.
296. Y.-H. Yang, S.-H. Tang, W. G. Liu, Y. Yan, L. W. Xie, C. Huang, S.-T. Wu, H. Wang, J.-H. Yang and F. Y. Wu, Int. J. Mass Spectrom., 2024, 500, 117234.
297. C. Neto, C. d. M. Valeriano, J. Enzweiler, G. Paravidini, M. Carvalho, M. Heilbron, C. Lana and J. H. Larizzatti, Geostand. Geoanal. Res., 2023, 47(4), 841–854.
298. C. Huang, H. Wang, W. Shi, J. Sun, F. Hu, L. Xu, Y. Yang, S. Wu, L. Xie and J. Yang, Sci. China Earth Sci., 2023, 66(11), 2603–2621.
299. J. Sheng, S. Li, J. D. Owens, X. Wang, Y. Wei, G. Ming and F. Huang, Geostand. Geoanal. Res., 2024, 48(1), 289–299.
300. X. Wang, X. Wang, L. Wang, S. Wu, D. Xue, W. Duan, Z. Ma, J. Xiao and X. Li, Geostand. Geoanal. Res., 2023, 47(4), 945–955.
301. N. Cogne, A. Derycke and K. Gallagher, Geostand. Geoanal. Res., 2024, 48(2), 381–391.
302. A. Barla, A. Triantafyllou, M. N. Ducea, G. Jepson, W. McClelland, D. Giesler, G. E. Gehrels and C. Fellah, Chem. Geol., 2023, 636, 121635.
303. O. K. Droubi, A. M. M. Bauer, C. Bonamici, W. O. O. Nachlas, M. J. J. Tappa, J. M. M. Garber and J. R. R. Reimink, Geostand. Geoanal. Res., 2023, 47(2), 337–369.
304. J. Wang, D.-M. Tang, Q.-H. Yuan, B.-X. Su, W.-J. Li, B.-Y. Gao, Z.-A. Bao and Y. Zhao, Geostand. Geoanal. Res., 2023, 47(4), 969–982.
305. M. Ebihara, N. Shirai and S. Sekimoto, Geostand. Geoanal. Res., 2023, 47(4), 931–944.
306. V. Pasquier, E. S. Rego, J. Dupeyron, A.-S. Bouvier, T. Bovay, M. Robyr and J. Marin-Carbonne, Geostand. Geoanal. Res., 2024, 48(2), 423–431.
307. J. Roland, V. Debaille, J. de Jong and S. Goderis, Talanta, 2023, 263, 124714.
308. D. Condon, B. Schoene, M. Schmitz, U. Schaltegger, R. B. Ickert, Y. Amelin, L. E. Augland, K. R. Chamberlain, D. S. Coleman, J. N. Connelly, F. Corfu, J. L. Crowley, J. H. F. L. Davies, S. W. Denyszyn, M. P. Eddy, S. P. Gaynor, L. M. Heaman, M. H. Huyskens, S. Kamo, J. Kasbohm, C. B. Keller, S. A. MacLennan, N. M. McLean, S. Noble, M. Ovtcharova, A. Paul, J. Ramezani, M. Rioux, D. Sahy, J. S. Scoates, D. Szymanowski, S. Tapster, M. Tichomirowa, C. J. Wall, J.-F. Wotzlaw, C. Yang and Q.-Z. Yin, Geol. Soc. Am. Bull., 2024, 136(9–10), 4233–4251.
309. S. V. Palesskiy, I. V. Nikolaeva and O. A. Kozmenko, Geochem. Int., 2023, 61(7), 744–749.
310. Y. Cai, T. Ruan, Y. Li, B. C. Li, W. W. Zhang, Z. H. Li, H. Z. Wei and E. T. Rasbury, J. Anal. At. Spectrom., 2023, 38(10), 1934–1942.
311. X. Qiu, Z. Hu, T. He, T. Luo, W. Zhang, M. Li, K. Zong, Z. Wang and Y. Liu, J. Anal. At. Spectrom., 2024, 39(2), 545–557.
312. M. Fan, X. Liu, S. Sun, Y. Dong and Z. Bao, J. Anal. At. Spectrom., 2024, 39(3), 754–766.
313. A. J. McKanna, B. Schoene and D. Szymanowski, Geochronology, 2024, 6(1), 1–20.
314. A. J. McKanna, I. Koran, B. Schoene and R. A. Ketcham, Geochronology, 2023, 5(1), 127–151.
315. G. R. Sharman and M. A. Malkowski, Geochronology, 2024, 6(1), 37–51.
316. E. E. Donaghy, M. P. Eddy, F. Moreno and M. Ibanez-Mejia, Geochronology, 2024, 6(1), 89–106.
317. J. Karasinski, K. Tetfejer, P. Radzinski, A. Tupys, A. Gambin, E. Bulska and L. Halicz, Anal. Chem., 2024, 96(9), 3763–3771.
318. X. Wang, L. Wang, Z. Ma, Y. Liu, W. Duan, M. Tan and J. Xiao, J. Anal. At. Spectrom., 2024, 39(2), 447–454.
319. E. O. Dubinina, Y. N. Chizhova and S. A. Kossova, Geochem. Int., 2023, 61(11), 1164–1176.
320. H.-O. Gu, X. Liu, H. Sun, G. Sun, D. Li, F. Wang, C. Ge, Y. Fan and T. Zhou, Anal. Chem., 2024, 96(16), 6131–6138.
321. Z. Zhang, T. Li, B. Li, Y. Lin, G. Li and T. Chen, J. Anal. At. Spectrom., 2024, 39(4), 1142–1151.
322. Q.-H. Zhong, L. Yin, J. Li, Y.-X. Feng, N.-P. Shen, B.-Y. Peng and Z.-Y. Wang, J. Anal. At. Spectrom., 2023, 38(11), 2291–2301.
323. D. M. Ratnayake, R. Tanaka and E. Nakamura, Anal. Chim. Acta, 2023, 1278, 341723.
324. X. Lu, D. He, Y. Liu, M. Li, J. Lin, W. Chen, L. Zhu, A. Yang, Y. Feng, Z. Liu, X. Zeng and Z. Hu, Chem. Geol., 2024, 648, 121935.
325. T. Zhang and J. Li, Russ. J. Gen. Chem., 2023, 93(Suppl. 4), S899–S907.
326. X. Fan, C. K. I. Sio, X. Chu and H. Jiang, Rapid Commun. Mass Spectrom., 2024, 38(2), e9668.
327. C. Zhang, T. W. Chen and J. D. Vervoort, J. Anal. At. Spectrom., 2023, 38(12), 2636–2647.
328. B. Dey, T. Shibata and M. Yoshikawa, Geochem. J., 2023, 57(6), 176–183.
329. F. Liu, X. Li, H. Yang, Q. Peng, J. Wu and Z. Zhang, J. Anal. At. Spectrom., 2023, 38(12), 2581–2589.
330. C. Pin and A. Gannoun, J. Anal. At. Spectrom., 2024, 39(1), 204–215.
331. T. Yang, H. Yu, Z. Zhu, R. Ding, J. Wang and B. Zhu, J. Anal. At. Spectrom., 2024, 39(5), 1270–1276.
332. Z. Huang, X. L. Hou and X. Zhao, Anal. Chem., 2023, 95(34), 12931–12939.
333. S. M. Dowell, T. S. Barlow, S. R. Chenery, O. S. Humphrey, J. Isaboke, W. H. Blake, O. Osano and M. J. Watts, Anal. Methods, 2023, 15(34), 4226–4235.
334. S. Shen, E. Krogstad, E. Conte and C. Brown, Int. J. Mass Spectrom., 2022, 471, 116726.
335. B. Zhao, X. Long, J. Li, A. Shen and X. He, Geol. J., 2023, 58(8), 3172–3180.
336. W. S. Ni, X. J. Mao, F. Xiao, X. R. Guo, L. Wang, J. Z. Wu and T. T. Wang, Anal. Lett., 2024, 57(12), 1816–1828.
337. G. Paris, Geostand. Geoanal. Res., 2024, 48(1), 29–42.
338. S. Lopez-Urzua, T.-H. Luu and L. Derry, J. Anal. At. Spectrom., 2024, 39(4), 1033–1042.
339. J.-H. Bai, M. Lin, S.-X. Zhong, Y.-N. Deng, L. Zhang, K. Luo, H. Wu, J.-L. Ma and G.-J. Wei, J. Anal. At. Spectrom., 2023, 38(3), 629–637.
340. Y. Yang, Z. Chu, S. Wu, L. Xie, C. Huang, J. Yang and F. Wu, At. Spectrosc., 2023, 44(4), 247–252.
341. D. Peng, X. Cai, G. Li, X. He, A. Sun, L. Wang, D. Yang, T. Wang and J. Xu, Spectrochim. Acta, Part B, 2024, 214, 106888.
342. Y.-M. Xu, G.-Q. Wang, Z. Yang, Y.-L. Zeng and F. Guo, J. Anal. At. Spectrom., 2024, 39(1), 86–98.
343. A. Buzenchi, H. Moreira, O. Bruguier, D. Bosch and B. Dhuime, J. Anal. At. Spectrom., 2023, 38(10), 2113–2126.
344. C. Park and H. Kim, J. Anal. Sci. Technol., 2024, 15(1), 18.
345. H. Jaegler and A. Gourgiotis, J. Anal. At. Spectrom., 2023, 38(10), 1914–1919.
346. Z. Qin, C. Wang, W. Zhou, H. Liu, R. Gao, J. Li and J. Liu, Geostand. Geoanal. Res., 2023, 47(4), 811–819.
347. N. K. Jensen, Z. B. Deng, J. N. Connelly and M. Bizzarro, Geostand. Geoanal. Res., 2024, 48(1), 5–28.
348. Y. Han, L. Zhou, M. Shi, Y. Hu, G. Zhang, X. Hou and L. Feng, J. Anal. At. Spectrom., 2024, 39(1), 269–280.
349. T. Kochi, T. Yoshimura, N. O. Ogawa, N. Ohkouchi, Y. Kobayashi and J. Kuroda, Geostand. Geoanal. Res., 2024, 48(1), 77–89.
350. M. Trudgill, S. Nuber, H. E. Block, J. Crumpton-Banks, H. Jurikova, E. Littley, M. Shankle, C. Xu, R. C. J. Steele and J. W. B. Rae, Geochem., Geophys., Geosyst., 2024, 25(3), e2023GC011350.
351. J. Karasinski, E. Bulska, L. Halicz, A. Tupys and B. Wagner, J. Anal. At. Spectrom., 2023, 38(11), 2468–2476.
352. F. Li, D. Guo, L. Song, B. Li, J. Xu and X. Zhang, At. Spectrosc., 2023, 44(5), 336–342.
353. H. Cohen-Sadon, Y. O. Rosenberg and A. Amrani, Chem. Geol., 2023, 640, 121738.
354. A. Pack, E.-M. Bultmann, M. Tatzel and J. Reitner, Geochem., Geophys., Geosyst., 2023, 24(12), e2023GC011243.
355. M. Bernecker, S. Hofmann, P. T. Staudigel, A. J. Davies, M. Tagliavento, N. Meijer, A. Ballian and J. Fiebig, Chem. Geol., 2023, 642, 121803.
356. N. M. Ellis and B. H. Passey, Chem. Geol., 2023, 635, 121616.
357. O. A. Al-Najjar, Y. S. Wudil, U. F. Ahmad, O. S. B. Al-Amoudi, M. A. Al-Osta and M. A. Gondal, Appl., Spectrosc. Rev., 2023, 58(10), 687–723.
358. F. Poggialini, B. Campanella, B. Cocciaro, G. Lorenzetti, V. Palleschi and S. Legnaioli, J. Anal. At. Spectrom., 2023, 38(9), 1751–1771.
359. Y. Rao, W. Ren, W. Kong, L. Zeng, M. Wu, X. Wang, J. Wang, Q. Fan, Y. Pan, J. Yang and Y. Duan, J. Anal. At. Spectrom., 2024, 39(3), 925–934.
360. Y. Rao, C. Sun, X. Yu, F. Chen, L. Wang, B. Zhang, T. Sun, C. Liu, J. Qiu, Y.-Y. S. Zhao and J. Yu, Spectrochim. Acta, Part B, 2024, 213, 106881.
361. S. Zhu, G. Song, W. Zhang, Y. Zhang, Y. Wei, Q. Zhang, D. Chen, J. Li and T. Sun, J. Anal. At. Spectrom., 2024, 39(3), 841–853.
362. X. Wang, C. Liu, P. Liu and Z. Ling, J. Anal. At. Spectrom., 2024, 39(2), 558–572.
363. I. Elhamdaoui, S. Selmani, M. Sabsabi, M. Constantin, P. Bouchard and F. Vidal, Appl. Spectrosc., 2023, 77(12), 1382–1392.
364. C. Alvarez-Llamas, A. Tercier, C. Ballouard, C. Fabre, S. Hermelin, J. Margueritat, L. Duponchel, C. Dujardin and V. Motto-Ros, J. Anal. At. Spectrom., 2024, 39(4), 1077–1086.
365. I. Krempl, K. Novotny, V. Wertich, R. Skoda, V. Kanicky and J. Leichmann, Spectrochim. Acta, Part B, 2023, 206, 106734.
366. T. Wang, J. Ramezani, C. Yang, J. Yang, Q. Wu, Z. Zhang, D. Lv and C. Wang, Earth-Sci. Rev., 2023, 245, 104550.
367. S. Glorie, S. E. Gilbert, M. Hand and J. C. Lloyd, Geochronology, 2024, 6(1), 21–36.
368. A. Giuliani, S. Oesch, M. Guillong and G. H. Howarth, Chem. Geol., 2024, 649, 121982.
369. S. Huang, Z. Chang, K. Liu and D. Garbe-Schoenberg, Geostand. Geoanal. Res., 2023, 47(4), 725–747.
370. A. Beranoaguirre, I. Vasiliev and A. Gerdes, Geochronology, 2022, 4(2), 601–616.
371. C. Seiler, S. C. Boone, B. P. Kohn and A. J. W. Gleadow, Chem. Geol., 2023, 635, 121623.
372. M. Kutzschbach and J. Glodny, J. Anal. At. Spectrom., 2024, 39(2), 455–477.
373. Y. Lu, L.-L. Zhang, L. Liu, D.-C. Zhu, J.-C. Xie and Q. Wang, J. Anal. At. Spectrom., 2024, 39(3), 829–840.
374. W. Wang, J. Xu, R. Xi, S. Guo, Y. Su, S. Fang, H. Zhang, Y. Wang, J. Fan, L. Feng, Y. Wang and Z. Li, J. Anal. At. Spectrom., 2024, 39(1), 178–189.
375. M. B. K. Kharkongor, S. Glorie, J. Mulder, C. L. Kirkland, D. Chew, B. Kohnd and A. Simpson, Chem. Geol., 2023, 636, 121630.
376. J. Leger, R. Pik, B. Tibari, S. Ternois, M. Ford, C. Peiffert and J. Mercadier, Geostand. Geoanal. Res., 2023, 47(2), 219–242.
377. P. Vermeesch, Y. Tian, J. Schwanethal and Y. Buret, Geochronology, 2023, 5(2), 323–332.
378. H. Ito, Minerals, 2024, 14(4), 436.
379. J. Hopp, M. Vogt, W. H. Schwarz and M. Trieloff, Geostand. Geoanal. Res., 2023, 47(3), 579–594.
380. Q. Ma, Z. Yang, Y. Yang and Z. Chu, Ore Geol. Rev., 2023, 163, 105769.
381. M. Hlozková, M. V. Galiová, P. Coufalík, K. Breiter, R. Skoda, M. Brezina, M. Brtnicky and J. Kynicky, Chem. Geol., 2023, 641, 121775.
382. F. Wu and Z. Zeng, At. Spectrosc., 2023, 44(5), 298–310.
383. X. Huangfu, Y. Zhang, Y. Wang and C. Ma, Chemosphere, 2024, 358, 142201–142201.
384. C. N. Rodríguez-Díaz, E. Paredes, L. D. Pena, I. Cacho, C. Pelejero and E. Calvo, Talanta, 2024, 269, 125473.
385. E. K. King-Doonan, L. D. Bilenker, D. Weis, K. Gordon, A. Fourny, G. M. Patton and Y. Zhao, Geostand. Geoanal. Res., 2024, 48(2), 309–330.
386. V. K. Zepeda, B. S. Kamber and O. Y. A. Ghidan, Chem. Geol., 2024, 647, 121827.
387. J. W. Li, K. Q. Zong, D. Yan, Y. W. Du, Z. C. Wang, W. Gao, M. Li, J. Lin, W. Zhang, H. H. Chen, Z. C. Hu and Y. S. Liu, J. Anal. At. Spectrom., 2023, 38(10), 1984–1994.
388. A. M. Cruz-Uribe, G. Craig, J. M. Garber, B. Paul, C. Arkula and C. Bouman, Geostand. Geoanal. Res., 2023, 47(4), 795–809.
389. K. Jenkins, K. Goemann, I. Belousov, M. Morissette and L. Danyushevsky, Geostand. Geoanal. Res., 2023, 47(2), 267–295.
390. H. Li, F. Li, W. Guo, L. Jin and S. Hu, J. Anal. At. Spectrom., 2024, 39(6), 1493–1502.
391. Q. Li, W. Zhang, Z. Hu, Y. Feng, H. Hu, T. Luo, J. Huang and X. Zeng, Chem. Geol., 2023, 636, 121651.
392. X. Tan, J. Koch, D. Guenther and B. Hattendorf, Geostand. Geoanal. Res., 2024, 48(1), 43–55.
393. X. Tan, H. Tian, L. Lu, D. Xiong and T. Liang, Molecules, 2024, 29(1), 51.
394. J. Fietzke and E. Anagnostou, Geostand. Geoanal. Res., 2023, 47(3), 481–492.
395. S. M. Chernonozhkin, T. Van Acker, S. J. M. Van Malderen, J. Belza, S. Goderis and F. Vanhaecke, J. Anal. At. Spectrom., 2024, 39(4), 1050–1056.
396. K. Mervic, J. T. van Elteren, M. Bele and M. Sala, Talanta, 2024, 269, 125379.
397. K. Yanagisawa, M. Matsueda, M. Furukawa, H. Ishiniwa, T. Wada, T. Hirata and Y. Takagai, Analyst, 2023, 148(18), 4291–4299.
398. Y. Liu, T. Wagner and T. Fubwinkel, Chem. Geol., 2024, 644, 121863.
399. J. Li, X.-X. Ling, Y. Liu, G.-Q. Tang, H.-X. Ma, X.-H. Li and Q.-L. Li, At. Spectrosc., 2023, 44(5), 318–325.
400. P. Vermeesch, Geochronology, 2022, 4(2), 561–576.
401. Z. Cui, X.-P. Xia, Q. Yang, K. Zhang, X. Yang, C.-K. Lai, W.-F. Zhang, Y.-Q. Zhang and Y.-N. Yang, J. Anal. At. Spectrom., 2024, 39(4), 1070–1076.
402. R.-Y. Li, J. Hao, W. Yang, H.-C. Tian, S. Hu and Y. Lin, J. Anal. At. Spectrom., 2023, 38(10), 1962–1972.
403. E. De Pauw, P. Tack and L. Vincze, J. Anal. At. Spectrom., 2024, 39(2), 310–329.
404. I. P. Sverchkov, V. A. Matveeva and M. A. Chukaeva, Spectrochim. Acta, Part B, 2023, 207, 106738.
405. B. Maestracci, S. Delchini, D. Chateigner, H. Pilliere, L. Lutterotti and E. Borovin, J. Geochem. Explor., 2023, 252, 107250.
406. T. Kuwatani, T. Sakai, K. Nakamura and T. Komai, J. Geogr., 2023, 132(5), 367–384.
407. K. Nakamura, T. Kuwatani and T. Komai, J. Geogr., 2023, 132(5), 403–415.
408. T. F. Akhmetzhanov, T. Y. Cherkashina, A. N. Zhilicheva, V. M. Chubarov and G. V. Pashkova, J. Anal. At. Spectrom., 2023, 38(12), 2664–2673.
409. T. G. Kuz’mina, T. V. Romashova, M. A. Troneva and I. V. Khokhlova, J. Anal. Chem., 2023, 78(8), 975–979.
410. C. E. Schrank, M. W. M. Jones, D. L. Howard, A. Berger and M. Herwegh, Chem. Geol., 2024, 645, 121886.
411. A. K. Maurya and P. Deb Barman, X-Ray Spectrom., 2024,(1) DOI:10.1002/xrs.3422.
412. A. Plessow, M. Mehne and S. Kureti, Geostand. Geoanal. Res., 2024, 48(2), 433–444.
413. T. Yomogida, T. Hashimoto, T. Okumura, S. Yamada, H. Tatsuno, H. Noda, R. Hayakawa, S. Okada, S. Takatori, T. Isobe, T. Hiraki, T. Sato, Y. Toyama, Y. Ichinohe, O. Sekizawa, K. Nitta, Y. Kurihara, S. Fukushima, T. Uruga, Y. Kitatsuji and Y. Takahashi, Analyst, 2024, 149(10), 2932–2941.
414. J. Hammerli and S. Boroughs, J. Anal. At. Spectrom., 2023, 38(10), 1927–1933.
415. F. Bea, P. Montero, D. Ortega, J. F. Molina, A. Cambeses, L. Barcos, S.-J. Wang and S. Ke, Geostand. Geoanal. Res., 2024, 48(2), 331–343.
416. T. Lopes, D. Capela, D. Guimaraes, M. F. S. Ferreira, P. A. S. Jorge and N. A. Silva, Sci. Rep., 2024, 14(1), 9123–9123.
417. G. Wang, Y. Zeng, L. Qi, W. Liu and J. Xu, Anal. Chim. Acta, 2023, 1278, 341721.
418. L. Zhong, J. Peng, J. He, Y. Du, T. Xing, J. Li, W. Guo, Q. Ni, J. Hu and H. Song, Appl. Geochem., 2023, 159, 105832.
419. Y. He, Y. Zhou, T. Wen, S. Zhang, F. Huang, X. Zou, X. Ma and Y. Zhu, Appl. Geochem., 2022, 140, 105273.
420. N. Freij, D. D. Gregory and Y. Liu, Geosci. Data J., 2023, 10(4), 500–504.
421. Y. Deng, R. Zhong, D. Li, Y. Li and H. Cui, Acta Geol. Sin., 2022, 96(6), 2148–2157.
422. G. Q. Liu, K. D. Zhao, T. Ulrich, W. Chen, D. Zhang, Q. Li, H. D. Zhao, R. Q. Zhang and F. Xia, J. Anal. At. Spectrom., 2023, 38(10), 2007–2018.
423. T. A. Markmann, P. Lanari, F. Piccoli, T. Pettke, M. Tedeschi, M. Lueder, B. E. Kunz, N. Riel and J. Laughton, Chem. Geol., 2024, 646, 121895.
424. M. Oeser, I. Horn, R. Dohmen and S. Weyer, Eur. J. Mineral., 2023, 35(5), 813–830.
425. P. Kump, X-Ray Spectrom., 2024, 53(1), 16–26.

---

Footnote

† Review coordinator.

---