Time-gated multi-dimensional luminescence thermometry via carbon dots for precise temperature mobile sensing

Abstract

Luminescence thermometry presents precise remote temperature measurement capabilities but faces significant challenges in real-world applications, primarily stemming from the calibration's susceptibility to environmental factors. External factors can compromise accuracy, necessitating resilient measurement protocols to ensure dependable temperature (T) readings across various settings. We explore a novel three-dimensional (3D) approach based on time-gated (t) luminescence thermometric parameters, Δ(T,t), employing physical mixtures of surface-engineered carbon dots (CDs) based on dibenzoylmethane and rhodamine B. These CDs showcase enduring, temperature-responsive, and customizable phosphorescence, easily activated by low-power LEDs and distinguished by their prolonged emission time due to thermally activated delayed phosphorescence. Quantifying the thermal emission dependency is achievable through conventional spectrometer analyses or by capturing photographs with a smartphone's camera under flashlight illumination, yielding up to 30 time-gated ratiometric thermometric parameters per sample. Notably, within the temperature range of 23–45 °C, the maximum relative sensitivity of 7.9% °C⁻¹ surpasses current state-of-the-art CD-based thermometers and ensures temperature readout with low-resolution portable devices as non-modified smartphones.

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Introduction

Luminescence thermometry has emerged as a promising method for remote temperature measurement. It provides high relative thermal sensitivity (S_r > 1% °C⁻¹), fine spatial resolution (10⁻⁶ m), and rapid acquisition times (<10⁻³ s).^1–5 This technique attains applications across diverse scientific domains, including photonics,⁶ microelectronics,⁷ microfluidics,^8,9 catalysis,^10,11 and nanomedicine.^12,13 The operating principle is straightforward, utilizing the luminescence properties of materials—such as intensity, lifetime, or spectral characteristics—to monitor temperature variations.^14,15 A widely used method for determining absolute temperature involves measuring the intensity ratio of distinct spectral regions in the emission spectrum, known as a self-reference ratiometric thermometer.¹ These thermometers provide consistent temperature readings and are immune to local intensity fluctuations. However, external factors like humidity, pressure, and chemical contaminants can interfere, compromising accuracy and reliability. Research efforts aim to develop robust measurement protocols and explore strategies for enhancing resilience in challenging environments.^15–17

One solution involves primary luminescent thermometers characterized by a well-established state equation.^16,18–26 Most of the cases deal with the upconversion phenomena in which the Boltzmann equation describes the thermally coupled levels involved in the emission.^15,27–29 Upconversion requires NIR laser radiation, preventing the widespread use in mobile sensing and within the Internet of Things (IoT) due to safety conditions and the need to adjust the smartphone device. Very few examples deal with primary thermometers involving downshifting and non-coherence low-power excitation sources.^14,25,30,31 Other examples are based on an empirical equation³² directly relating a specific measured value to absolute temperature, eliminating the need for calibration. A recent approach utilizes energy transfer-driven luminescent thermometers, combining experimental and theoretical calculations.^33–35 However, circumventing the calibration curve is not universally applicable, as the physical mechanism underlying the thermal dependence of optical features is often complex or unknown from a theoretical point of view, limiting the application of this strategy.

Another method to enhance the robustness is through synchronous and independent temperature multiple readouts or multiparametric approaches to improve relative sensitivity (S_r).^33,36–38 These approaches optimize thermal response through two or more thermometric parameters. A few examples include luminescent QR codes^14,39 printed on commercial medical adhesives³³ or protective masks⁴⁰ with multiplexed color and spatial layers³⁹ that provide simultaneous tracking and multiple synchronized temperature readouts. This is achieved using calculations from density functional theory, intramolecular energy transfer, along with a rate equations model.^33,35 Examples involve improving the performance of luminescent thermometers by integrating multiparametric sensing and multiple regression.^24,25,36 Also, recent advancements in machine learning have demonstrated significant potential in enhancing the performance of luminescent materials in distinct fields.^41–45 By applying machine learning algorithms, complex and subtle patterns in photoluminescence data can be effectively identified, leading to more precise and reliable temperature readouts. This is particularly relevant for time-dependent signals, such as luminescence lifetimes.⁴⁴

Pushing luminescence thermometry to real-world applications, the inclusion of multi-parameter readouts is achieved through the construction of prototypes, including photovoltaic (PV) cells in a new generation of smart luminescent solar concentrators (LSC), that provide novel thermometric parameters involving PV cells and smartphones.^37,38,46 The thermal-dependent luminescence properties of emissive materials applied in the LSC will directly impact the number and energy of the photons reaching the PV cells and thus, will determine their electrical output in the temperature readout based on luminescent thermometers through the increase of the number of simultaneously acquired thermometric parameters. The PV cells provide the energy and the smartphone the ubiquitous and mobile-sensing⁴⁷ required for the widespread luminescence thermometry with real societal impact on day life activities.

To achieve our primary objective, the judicious choice of luminescent materials presents an additional challenge in luminescence thermometry. Carbon dots (CDs) appear as good candidates,⁴⁸ combining stable luminescence properties across a wide temperature range while being sustainable and compatible with various measurement environments. Surprisingly, since 2012, there have been few studies on CD-based thermometers (Fig. S1†). Despite the pioneering work by Yu et al.,⁴⁹ which gathered attention from the scientific community, progress in thermometry based on CDs has been limited thus far.

Leveraging its low toxicity, stability, and hydrophilicity, we selected phosphorescent CDs⁵⁰ (CD_A and CD_B) easily excited under low-power excitation sources like LEDs or smartphone flashlights to design smart e-tags for thermal sensing. These CDs are formed by nanoparticles with nearly spherical shapes and average diameters of 4±1 and 5±1 nm, respectively (Fig. 1a and b). They are crystalline structures with a lattice fringe spacing of 0.21 nm corresponding to graphene's (100) diffraction plane.^51–53 An interplanar spacing of 0.18 nm was also detected in the case of CD_B (Fig. 1c), associated with the (103) diffraction plane of graphitic carbon.^38,54 A single e-tag formed of a physical mixture of CD_A and CD_B (Fig. 1d) provides multiple layers of thermal sensing by exploiting different excitation energies, and temporal characteristics accessible under portable spectrometers and smartphones (Fig. 1e–g). A comprehensive case study employing photonic thermometric parameters involving a sample size of up to 660 emission spectra in combination with 8168 photographs taken with a smartphone, displays intriguing S_r values (Fig. 1f), which supports the potential of this combined approach toward a new generation of ubiquitous, reliable, and high-performance mobile temperature optical sensors (Fig. 1h). Additionally, this study investigates the influence of Thermally Activated Delayed Phosphorescence (TADP)⁵⁵ on thermometric performance, highlighting the challenges posed by this phenomenon. The TADP is a process similar to the well-known thermally activated delayed fluorescence (TADF), but it involves the emission of a triplet state instead of a singlet state. This research provides evidence of the TADP process in the luminescence of CDs and details how it affects the thermometric properties.

Fig. 1 Sustainable smart e-tags for mobile thermal sensing. TEM images of (a) CD_A and (b and c) CD_B; the insets reproduce HR-TEM images of individual CDs showing lattice fringes with the corresponding d-spacing determined by FFT analysis. (d) Schematics synthesis. (e) Emission spectra as a function of temperature and delay time. (f) Definition of the thermometric parameters. (g) Interpolation of experimental points with a Python code (ESI†), totaling 2.50 × 10⁴ points per thermometric parameter and (h) 3D scatterplot of S_r (T,t) (maximum at S_m).

Results and discussion

Photoluminescence features

The CDs have gathered significant attention across various research domains owing to their exceptional properties, including luminescence, resistance to photobleaching, chemical stability, affordability of precursors, low toxicity, and biocompatibility.^49,56 Recent studies have shown the thermal-sensing capabilities of certain CDs, positioning them as viable alternatives to other nanomaterial-based thermometers.^38,57–60 One of the most intriguing features of CDs is that under certain conditions their broad emission reveals an afterglow emission visible to the naked eye.^50,61 Additionally, energy transfer between different CDs or between aggregates of CDs may arise due to energy mismatch conditions of the spectral overlap between donor and acceptor bands.^38,50,61

CDs synthesized from dibenzoylmethane (CD_A) and rhodamine B (CD_B) were selected to process mixed e-tags due to their intriguing properties that include high absolute emission quantum yield (up to 0.52), phosphorescence emission characterized by long lifetimes activated under low-power excitation.⁵⁰ The emission spectra reveal broad bands across the visible spectrum (Fig. 2a and b) in the blue to the green spectral regions (Fig. S7, ESI†), whose long-lived emission is attributed to the B₂O₃ host.^50,62 This rationale behind the selection of these CD families renders an easier and more sustainable solution to explore different mixture ratios (CD_AB). The luminescence features of CD_AB display the contribution of each component (Fig. 2c, CD_A and CD_B) together with new features assigned to the interaction between CD_A and CD_B.⁵⁰

Fig. 2 Emission spectra and Perrin-Jablonski diagrams. Emission spectra of (a) CD_A, (b) CD_B, (c) CD₁₁ excited at 365 nm. (d) and (e) Perrin-Jablonski diagrams: absorption (dashed straight line starting from S₀ state) to the emission (waved lines from singlet and triplet excited). CD_A absorbs to a second singlet state (S₂), decay to the first singlet state (S₁) through an internal conversion (IC) process. Subsequently, S₁ may decay radiatively (S₁ → S₀ fluorescence, I¹_A) and populate the low-energy triplet state (T₁) via an intersystem crossing (ISC) process. T₁ decays radiatively (T₁ → S₀ phosphorescence, I²_A). CD_B displays absorption to the first singlet state (S₁), which can populate both the T₁ level and the aggregation-induced state (AIS). With increasing temperature, T₁ may also populate AIS through a Thermally Activated Delayed Phosphorescence (TADP) process. The phosphorescence emissions involving these two states are represented as I²_B and I¹_B.

By exploring for the first time the emission dependence on the different mixture ratios (Fig. 3), the photoluminescence mechanism was investigated. In particular, CD_B exhibits an Aggregation-Induced State (AIS),^63–67 which experiences a population increase due to TADP⁵⁵ process, as represented schematically in a Perrin-Jablonski diagram (Fig. 2e) and evident by the increase of the emission intensity with temperature (inset Fig. 3b). The TADP, in turn, is characterized by delayed emission after the cessation of an external excitation source, such as light (or electricity). This delayed emission occurs as the excited triplet state transitions back to the ground state via a thermally activated process. In TADP materials, the excited triplet states have longer lifetimes compared to traditional TADF,^68,69 enabling delayed emission. The presence of TADP becomes evident when the thermally activated emission band around 475 nm lacks evidence of an intensity increase with temperature, particularly noticeable for the lower amount of CD_B in the mixed CD_AB samples (CD₁₁ and CD₄₁), Fig. 3a and c.

Fig. 3 Thermal and temporal behaviors of mixed CDs emission. Emission spectra excited at 365 nm as a function of the temperature (23–45 °C) in the steady-state regime of (a) CD₁₁, (b) CD₁₄, and (c) CD₄₁. Panels (d), (e) and (f) display the time-resolved emission with different starting delay values (0.1–1.0 s) at 23 °C. The integrated intensity values within the shadowed areas I¹_AB, I²_AB, and I³_AB are assigned.

Conversely, CD_A lacks evidence of the presence of the AIS phenomenon (Fig. S8a and S8c†) in the spectral region of interest. Instead, it appears to attenuate the AIS and the TADP mechanisms, affecting thermometric performance. This combined effect (AIS and TADP) on CD_B leads to lower values of S_r, as detailed next.

The emission spectra of the mixed CD_AB samples (Fig. 3) reveal the emission characteristics of the precursors (Fig. S8†) affected by their relative intensity dependence on the AB ratio. For example, in Fig. 3a–c, the high-energy component (389–450 nm, I¹_AB) is an indication of the fluorescence emission arising from CD_A, as evidenced by its faster decay (Fig. S8c†). Moreover, due to the lower relative amount of CD_A in CD₁₄, it exhibits lower relative intensity I¹₁₄ (389–450 nm) compared to the I¹₄₁ observed in CD₄₁ (Fig. 3c and f), which lacks the TADP emission characteristic of CD_B. Despite the distinct emission maxima, while CD_A exhibits fluorescence and phosphorescence, CD_B is predominantly characterized by phosphorescence emission (I²_AB and I³_AB). Even in the physical mixtures, energy transfer between CDs and processes like triplet-triplet annihilation or other quenching channels, such as electron-phonon coupling may also occur, suggesting that the underlying interactions with CDs can be complex and multifaceted.^50,63,70,71

The CD_AB samples are sensitive to low-power non-coherent light, such as LEDs and smartphone flashlights. To visualize the emission of these phosphorescent materials, see the ESI video files for the pure samples CD_A and CD_B (SV01-CDA-CDB_UV.mp4†) and the mixed CD₁₁ sample under UV and flashlight excitations (SV02-CD11_UV.mp4 and SV03-CD11_Flash.mp4,† respectively). The CD_B has a longer-lasting emission when compared to CD_A, which is a consequence of the TADP process, as illustrated in Fig. 2e.

3D luminescence thermometry

Taking advantage of the long-lived luminescence and of the alterations in luminescence properties in response to changes in the temperature (T, °C) and in the time delay (t, seconds) for optical readings, our objective is to develop sensors at the nanoscale that establish connections with T and t as independent variables, introducing two-dimensional ratiometric parameters Δ(T,t) (eqn (1)) to access a 3D response surface of the relative sensitivity parameter, S_r(T,t) (eqn (3)). For pure CDs, Table S2† outlines six definitions of the ratiometric thermometric parameters (Δ_i, i = 1–6). Despite some appearing redundant (e.g., Δ₁ and Δ₂ are their reciprocals) from the perspective of S_r (eqn (1)), their selection is not arbitrary. The integrated intensity I¹_A represents the fluorescence emission for CD_A and the higher-energy phosphorescence emission of CD_B influenced by the TADP effect, while the integrated intensity I²_A represents the phosphorescence bands of CD_A and the lower-energy phosphorescence contribution of CD_B. This selection enhances S_r because the derivative of Δ is influenced by different emission mechanisms. For mixed CDs, the selection of I¹_AB, I²_AB, and I³_AB (Table S3, ESI†) was made to ensure spectral overlap with the color coordinates of the RGB (Red, Green, and Blue) model featuring mobile sensing (as detailed below).

The overall values of the maximum relative sensitivity (S_m) for all Δ_i based on the emission features measured with the spectrometer are summarized in Fig. 4. It is evident that pure CDs show lower S_m, with a maximum for CD_A for Δ₅ (S_m = 1.6% °C⁻¹), compared to the mixed one CD₄₁ (S_m = 7.9% °C⁻¹). Furthermore, a drawback for CD_A and CD_B is that S_m values occur at restricted delay times (0.0–0.1 s) and temperature intervals (23–25 °C). The physical mixed samples (CD₄₁, CD₁₁, and CD₁₄) exhibit superior performance concerning S_m compared to the precursors for a wider temperature range.

Fig. 4 Overall thermometric performance of pure and mixed CDs. (a) Distribution of S_m based on delay time and temperature. (b) Maximum relative sensitivity (S_m); the inset shows S_m for pure CDs.

Table 1 compares S_m from thermometers based on spectroscopic techniques with those obtained in this work. Higher values are found for the physical mixtures (CD_AB). For instance, a core-shell Gd(OH)CO₃-cys-CD@mSiO₂-RhB nanocomposite system presents an S_r value of 1.39% °C⁻¹.⁷² However, with the proposed multiparametric approach and considering the time, higher S_r values are observed for all mixed CDs and CD_A (Table 1) ranging from 1.6% °C⁻¹ for CD_A (Δ₅, Table S2†) to 7.9% °C⁻¹ for CD₄₁ (Δ₄ = I²₁₄/I¹₁₄, Table S3†).

Table 1 Comparison of S_m values found in the literature for CD-based luminescent thermometers. Maximum sensitivity (S_m) value and the temperature at which it occurs. Data were collected using spectroscopic techniques and compared with mobile sensing (smartphone camera)

Detection mode	Designation	S m (% °C^−1)	T (°C)
cys = L-cystine, RhB = rhodamine B, ZAO = ZnAl2O4, CLNO = Ca2LaNbO6, ZIF = zeolitic-imidazolate-framework, dU6 = di-ureasil, MMM = mixed-matrix membrane, BiOCl = bismuth oxychloride.
Spectrometer-based	CDA^this work	1.63	23
	CDB^this work	0.64	23
	CD11^this work	4.96	23
	CD14^this work	3.16	23
	CD41^this work	7.89	33
	Gd(OH)CO3-cys-CD@mSiO2-RhB^72	1.39	100
	CDs@ZAO:Eu^73	0.11	110
	CLNO:0.5%Pr^74	0.79	175
	CLNO:3%Pr^74	0.89	250
	CLNO:9%Pr^74	0.69	175
	CDs&RB@ZIF-8^2-MMM^75	0.74	20
	dU6/CD-RhB^38	1.03	45
	CDs/BiOCl^76	1.35	20
Mobile sensing	CD11 (under UV)^this work	7.8	29
	CD11 (under flashlight)^this work	3.5	45

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Even though CD₄₁ exhibits consistently higher S_m values on average, its maximum sensitivity is restricted to 30–33 °C (Fig. 4b), considering all thermometric parameters (Δ_i with i = 1–30, Table S3†). Among the mixed CDs, the CD₁₄ contains a higher proportion of CD_B. Surprisingly, the presence of TADP negatively affects the thermometric response, as indicated by the trend where higher amounts of CD_B correlate with lower thermometric performance. Moreover, there is a synergistic interaction between CD_A and CD_B, leading to improved performance in mixed CDs manifested with an enhancement of thermometric performance from pure CD_A to the mixed CD_AB samples (even for the case of CD₁₄). While this synergetic interaction between CD_A and CD_B is intriguing, it falls outside the scope of the present work and will be investigated in future research focused on energy transfer calculations involving CDs in diluted systems.⁶³ Since the CD₁₁ has the best performance, it was selected as a promising candidate for application in mobile sensing.

Mobile 3D luminescence thermometry: smartphone-based temperature readouts

Photographs in Fig. 5a illustrate the afterglow exhibited over time when the excitation sources are turned off, showcasing that emission is still detectable using a smartphone camera. This intrinsic property underscores the potential applications of these materials in environments with low-light conditions or for energy-efficient purposes. Additionally, Fig. 5b displays temperature-dependent luminescence behavior (t = 0.6 s fixed). Employing mobile detection, we quantified the emission color by analyzing the RGB color coordinates and computing thermometric performance, Fig. 5c–f. As expected, the dominant intensity was observed for the B and G coordinates, with the R coordinate values being significantly lower.

Fig. 5 Mobile sensing thermal readout. (a) Sequential photographs capturing the CD₁₁-based e-tag over time at 25 °C, under UV or the flashlight, at the starting point (t = 0 s). For t > 0, the illumination sources were promptly turned off and the photographs were recorded at t = 0.6 and 1.5 s. (b) Photographs of the e-tag showing variations at different temperatures at fixed time t = 0.6 s. Thermometric scatterplots of (c) Δ^IoT₅ under UV and (e) Δ^IoT₃ under flashlight. The S_r values are shown in panels (d) and (f), respectively. The S_m values are highlighted.

Measuring the RGB intensity as a function of the temperature and time, the mobile thermometric parameter Δ^IoT_i(T,t) was calculated (eqn (2)), yielding a high S_m value of 7.8% °C⁻¹ at T = 29 °C and t = 4.81 s for Δ^IoT₅, under UV radiation (Fig. 5c and d). Using the smartphone flashlight, an S_m value of 3.5% °C⁻¹ at T = 45 °C and t = 2.89 s was calculated (Fig. 5e and f). For the other Δ^IoT_i, the values of S_m remain higher than those found in the literature that employ traditional and expensive spectroscopic equipment (Table 1).

We note that thermal resolution depends on several factors, including the smartphone camera's ability to accurately measure different spectral bands without cross-correlation of the RGB channels, as well as its sensitivity and Signal-to-Noise Ratio characteristics. Based on the experimental data (Table 1), the maximum thermal resolution is approximately 3.5% °C⁻¹ (flashlight), and up to 7.8% °C⁻¹ (UV). This implies that 3.5% or 7.8% intensity variations correspond to a temperature change of 1 °C. For example, at higher signal intensities (∼200), the camera must distinguish intensity intervals of ∼7 (flashlight) or ∼16 (UV) to detect a 1 °C change. At lower intensities (∼20), the required interval is ∼1 (flashlight) or ∼2 (UV). These requirements are easily accomplished by current smartphone cameras and traditional CCD cameras, even under low-light conditions.⁷⁷ This finding underscores the practical applicability of our e-tags, offering a promising future in mobile sensing.

Conclusions

This work underscores the multifaceted utility of CDs in luminescence, showcasing their exceptional properties such as stable luminescence, resistance to photobleaching, and chemical stability. By synthesizing CDs from dibenzoylmethane (CD_A) and rhodamine B (CD_B) precursors, we have demonstrated their potential as thermometric materials, particularly in mixed configurations. The distinctive emission characteristics of CD_A and CD_B, including aggregation-induced state (AIS) and Thermally Activated Delayed Phosphorescence (TADP), have been modeled revealing intriguing thermometric behaviors. The integration of time-delayed luminescence and temperature-dependent emission enables the development of sensors at the nanoscale with enhanced sensitivity (7.9% °C⁻¹) using mobile (smartphone-based) luminescence thermometry. Notably, our investigation highlights the synergistic interaction between CD_A and CD_B, resulting in improved thermometric performance in mixed CDs. Furthermore, leveraging smartphone-based detection, we have demonstrated the practical applicability of CDs in mobile luminescence thermometry using a non-modified smartphone. The ability to quantify emission color and temperature-dependent luminescence behavior using readily accessible devices paves the way for advanced luminescent thermometric materials and methodologies.

Materials and methods

Materials, synthesis, and processing

Dibenzoylmethane (DBM), rhodamine B (RhB), boric acid (BA) (Scheme S1†), ethanol (EtOH), and sodium hydroxide (NaOH) were used as received. Two CDs with phosphorescence emission derived from DBM and RhB (designated as CD_A and CD_B, respectively) were synthesized according to our previous strategies.^38,50 The samples were processed as e-tags based on free-standing pellets with mixing ratios of A : B = 1 : 1, 1 : 4, and 4 : 1 (w/w). The mixed e-tags were designated as CD_AB.

Structural and optical characterization

Transmission electron microscopy (TEM). Images were obtained at the Iberian Nanotechnology Laboratory using a JEOL JEM 2100 (200 kV) microscope. Samples were dispersed in water and placed into the analyzing grids (UC-A on holey 400 mesh Cu grids, Ted Pella ref. 01824) by drop-casting, followed by drying at room temperature.

Photoluminescence. Spectra were recorded with a modular double-grating excitation spectrofluorimeter with a TRIAX 320 emission monochromator (Fluorolog-3, Horiba Scientific) coupled to an R928 Hamamatsu photomultiplier. The spectra were corrected for the detection and optical spectral response of the spectrofluorometer. The emission quantum yield values were measured at room temperature using a system (C9920-02, Hamamatsu) with a 150 W xenon lamp coupled to a monochromator for wavelength discrimination, an integrating sphere as the sample chamber, and a multichannel analyzer for signal detection. The method is accurate to within 10%.

Image acquisition and processing. The photographs of the e-tags were acquired with the smartphone camera (Zenfone 3, Asus®) under portable UV excitation (365 nm) or illumination with the smartphone's built-in flash. Under the same conditions, videos were recorded with a smartphone camera (Mi 10T, Xiaomi®) with a resolution of 64 megapixels and 6 different types of lenses with an aperture of f/1.89. The main camera sensor is a 1/1.7′′ Sony® IMX682 sensor with 4-in-1 technology (Super Pixel), combining four adjacent pixels (0.8 μm) into a single pixel measuring 1.6 μm. The phosphorescence videos (SV02-CD11_UV.mp4 and SV03-CD11_Flash.mp4†) were recorded at 30 frames per second after the excitation was turned off. Each frame (interval 0.03 s) was cropped in 55 × 45 pixels and analyzed according to the RGB (red, green, blue) color model (see section 5.2. Matlab® routine for RGB analysis in the ESI†). This model segments images into three channels, each representing a primary color, enabling the calculation of RGB coordinates.^32,47 Smartphone cameras, with their ability to effectively capture and analyze color variations,³² offer a convenient alternative to specialized equipment like spectrometers for temperature measurement. Given the widespread use of smartphones equipped with cameras capable of capturing images and videos, they serve as helpful tools for monitoring color changes in luminescent materials, providing information on temperature fluctuations. Integrating smartphone technology enhances user experience by simplifying temperature quantification processes, rendering them more accessible and adaptable to various applications, including the Internet of Things (IoT) networks.⁴⁷

Luminescence thermometry performance

Ratiometric thermometric parameters (Δ) were defined according to the emission spectra’ dependence on time and temperature. Thus, distinct ratiometric parameters (i = 1–36) can be found as a function of the temperature and delay time, Δ(T,t).where I_AB is the integrated intensity of e-tag CD_AB in the low and high-wavelength ranges (λ₁ and λ₂, respectively) of the emission spectra.

The fact that the e-tags from the CD_AB samples are excited under the flashlight of the smartphone provides an opportunity to set an additional and complementary number of thermometric parameters (Δ^IoT_i(T,t), where i = 1–30) based on the RGB color change using a smartphone towards mobile sensing, including IoT based on the intensity of the RGB color coordinates (I_RGB), avoiding the use of expensive and non-ubiquitous equipment.³³ The R intensity value was considered the average value from all the images due to its lower relative intensity.

From eqn (1) and (2) a single e-tag allows the definition of different thermometers providing reliability enhancement through temperature sensing using independent methods. In addition, introducing two-dimensional ratiometric parameters, Δ(T,t) to access a 3D response surface in which the relative sensitivity parameter, S_r(T,t) is defined as:

The S_r value represents the variation of the Δ(T,t) parameter per degree of temperature. Another important parameter is the maximum sensitivity, S_m, which corresponds to the highest value of the S_r. A Python code was developed to conduct a thorough analysis of the input data, involving tasks such as 2D interpolation of points, numerical derivative calculations, and generation of outputs. Fig. 1 illustrates the overall procedure used to extract the S_r surface and S_m values. For instance, Fig. 1d illustrates the schematics for synthesizing pure (CD_A and CD_B) and mixed (CD_AB) materials, while their morphological characterization is presented in Fig. 1a–c. As example, the emission spectra as function of temperature and delay time for CD₁₁ is shown in Fig. 1e, with shaded areas highlighting the definition of the thermometric parameter Δ(T,t). This parameter is computed for a large variety of Δ(T,t) values (Fig. 1f), resulting in the interpolation of experimental points using a Python code, yielding a total of 250 000 points per thermometric parameter (Fig. 1g) to proper calculate numerical partial derivatives. Finally, Fig. 1h displays the relative sensitivity S_r(T,t) in a 3D scatterplot, with the maximum sensitivity S_m indicated. The code and a detailed description of its functions can be found in the ESI.†

Author contributions

Sílvia F. V. Silva: data curation, software, investigation. Gonçalo V. Figueiredo: data curation, validation, investigation. Rui F. P. Pereira: data curation. Verónica de Zea Bermudez: data curation, writing – review & editing. Lianshe Fu: data curation, methodology, writing – original draft, writing – review & editing. Paulo S. André: formal analysis, supervision, validation, writing – original draft, writing – review & editing. Albano N. Carneiro Neto: conceptualization, data curation, formal analysis, investigation, methodology, software, supervision, validation, visualization, writing – original draft, writing – review & editing. Rute A. S. Ferreira: conceptualization, data curation, formal analysis, methodology, supervision, visualization, writing – original draft, writing – review & editing, resources, project administration.

Data availability

The authors declare that the main data supporting the findings of this study are available within the article and its ESI.† Extra data are available from the corresponding author upon request.

Conflicts of interest

There are no conflicts to declare.

Acknowledgements

This work was developed within the scope of the project CICECO-Aveiro Institute of Materials, UIDB/50011/2020, UIDP/50011/2020 & LA/P/0006/2020, financed by national funds through the FCT/MCTES (PIDDAC) and Instituto de Telecomunicações (UID/EEA/50008/2021). GVF thanks FCT for a PhD grant (2023.00526.BDANA). SFH Correia (IT) and LMSD (CICECO) are acknowledged for experimental support. ANCN acknowledges funding from the LogicALL project (PTDC/CTMCTM/0340/2021) financed by FCT/MEC (PIDDAC). RFP Pereira acknowledges FCT for CQ-UM base and programmatic projects (UIDB/00686/2020 and UIDP/00686/2020) and FCT-UMinho for the contract in the scope of Decreto-Lei 57/2016 (DOI: 10.54499/DL57/2016/CP1377/CT0050). V. de Zea Bermudez thanks FCT for funding CQ-VR (UIDB/0616/2020 and UIDP/0616/2020).

References

1. C. D. S. Brites, A. Millán and L. D. Carlos, in Handbook on the Physics and Chemistry of Rare Earths, ed. J.-C. G. Bünzli and V. K. Pecharsky, Elsevier, 2016, vol. 49, pp. 339–427.
2. D. Jaque and F. Vetrone, Nanoscale, 2012, 4, 4301–4326.
3. L. Marciniak, K. Prorok, L. Francés-Soriano, J. Pérez-Prieto and A. Bednarkiewicz, Nanoscale, 2016, 8, 5037–5042.
4. R. G. Geitenbeek, H. W. De Wijn and A. Meijerink, Phys. Rev. Appl., 2018, 10, 1.
5. M. Dramićanin, Luminescence Thermometry: Methods, Materials, and Applications, Woodhead Publishing, 2018.
6. R. A. S. Ferreira, C. D. S. Brites, C. M. S. Vicente, P. P. Lima, A. R. N. Bastos, P. G. Marques, M. Hiltunen, L. D. Carlos and P. S. André, Laser Photonics Rev., 2013, 7, 1027–1035.
7. T. P. van Swieten, T. van Omme, D. J. van den Heuvel, S. J. W. Vonk, R. G. Spruit, F. Meirer, H. H. P. Garza, B. M. Weckhuysen, A. Meijerink, F. T. Rabouw and R. G. Geitenbeek, ACS Appl. Nano Mater., 2021, 4, 4208–4215.
8. R. G. Geitenbeek, J. C. Vollenbroek, H. M. H. Weijgertze, C. B. M. Tregouet, A.-E. Nieuwelink, C. L. Kennedy, B. M. Weckhuysen, D. Lohse, A. van Blaaderen, A. van den Berg, M. Odijk and A. Meijerink, Lab Chip, 2019, 19, 1236–1246.
9. O. A. Savchuk, J. J. Carvajal, J. Massons, C. Cascales, M. Aguiló and F. Díaz, Sens. Actuators A: Phys., 2016, 250, 87–95.
10. T. S. Jacobs, T. P. van Swieten, S. J. W. Vonk, I. P. Bosman, A. E. M. Melcherts, B. C. Janssen, J. C. L. Janssens, M. Monai, A. Meijerink, F. T. Rabouw, W. van der Stam and B. M. Weckhuysen, ACS Nano, 2023, 17, 20053–20061.
11. M. Gao, M. Ye and Z. Liu, Curr. Opin. Chem. Eng., 2023, 42, 100969.
12. K. Okabe, N. Inada, C. Gota, Y. Harada, T. Funatsu and S. Uchiyama, Nat. Commun., 2012, 3, 705.
13. D. Jaque, B. del Rosal, E. M. Rodríguez, L. M. Maestro, P. Haro-González and J. G. Solé, Nanomedicine, 2014, 9, 1047–1062.
14. J. F. C. B. Ramalho, A. N. Carneiro Neto, L. D. Carlos, P. S. André and R. A. S. Ferreira, in Handbook on the Physics and Chemistry of Rare Earths, ed. J.-C. G. Bünzli and V. K. Pecharsky, Elsevier B.V, 2022, pp. 31–128.
15. C. D. S. Brites, R. Marin, M. Suta, A. N. Carneiro Neto, E. Ximendes, D. Jaque and L. D. Carlos, Adv. Mater., 2023, 36, 2302749.
16. M. Suta, Ž. Antić, V. Đorđević, S. Kuzman, M. D. Dramićanin and A. Meijerink, Nanomaterials, 2020, 10, 543.
17. M. Suta and A. Meijerink, Adv. Theory Simul., 2020, 3, 2000176.
18. A. M. P. Botas, C. D. S. Brites, J. Wu, U. Kortshagen, R. N. Pereira, L. D. Carlos and R. A. S. Ferreira, Part. Part. Syst. Charact., 2016, 33, 740–748.
19. L. Labrador-Páez, M. Pedroni, A. Speghini, J. García-Solé, P. Haro-González and D. Jaque, Nanoscale, 2018, 10, 22319–22328.
20. Y. Shen, J. Lifante, N. Fernández, D. Jaque and E. Ximendes, ACS Nano, 2020, 14, 4122–4133.
21. X. Qiu, Q. Zhou, X. Zhu, Z. Wu, W. Feng and F. Li, Nat. Commun., 2020, 11, 4.
22. J. C. Martins, A. R. N. Bastos, R. A. S. Ferreira, X. Wang, G. Chen and L. D. Carlos, Adv. Photonics Res., 2021, 2, 2000169.
23. J. M. Gonçalves, A. R. N. Bastos, S. J. L. Ribeiro, L. D. Carlos, R. L. Longo, J. M. A. Caiut and R. A. S. Ferreira, Nanoscale Adv., 2024, 6, 1486–1496.
24. T. J. S. Ramos, R. L. Longo, C. D. S. Brites, R. A. S. Ferreira, O. L. Malta and L. D. Carlos, Nanoscale, 2023, 15, 9993–10003.
25. K. M. N. de Souza, R. N. Silva, J. A. B. Silva, C. D. S. Brites, B. Francis, R. A. S. Ferreira, L. D. Carlos and R. L. Longo, Adv. Opt. Mater., 2022, 10, 2200770.
26. J. C. Martins, C. D. S. Brites, A. N. C. Neto, R. A. S. Ferreira and L. D. Carlos, in Luminescent Thermometry, Springer International Publishing, Cham, 2023, pp. 105–152.
27. F. H. Borges, R. Mauricot, D. Neumeyer, V. d. S. de Souza, M. Verelst and R. R. Gonçalves, J. Lumin., 2024, 273, 120642.
28. T. Zheng, M. Runowski, P. Woźny, B. Barszcz, S. Lis, M. Vega, J. Llanos, K. Soler-Carracedo and I. R. Martín, J. Alloys Compd., 2022, 906, 164329.
29. M. Suta, Opt. Mater.: X, 2022, 16, 100195.
30. A. S. Souza, L. A. O. Nunes, I. G. N. Silva, F. A. M. Oliveira, L. L. Da Luz, H. F. Brito, M. C. F. C. Felinto, R. A. S. Ferreira, S. A. Júnior, L. D. Carlos and O. L. Malta, Nanoscale, 2016, 8, 5327–5333.
31. A. De, M. A. Hernández-Rodríguez, A. N. Carneiro Neto, V. Dwij, V. Sathe, L. D. Carlos and R. Ranjan, J. Mater. Chem. C, 2023, 11, 6095–6106.
32. J. F. C. B. Ramalho, S. F. H. Correia, L. Fu, L. L. F. António, C. D. S. Brites, P. S. André, R. A. S. Ferreira and L. D. Carlos, Adv. Sci., 2019, 6, 1900950.
33. J. F. C. B. Ramalho, L. M. S. Dias, L. Fu, A. M. P. Botas, L. D. Carlos, A. N. Carneiro Neto, P. S. André and R. A. S. Ferreira, Adv. Photonics Res., 2021, 2100206.
34. R. A. S. Ferreira, E. Mamontova, A. M. P. Botas, M. Shestakov, J. Vanacken, V. Moshchalkov, Y. Guari, L. F. Chibotaru, D. Luneau, P. S. André, J. Larionova, J. Long and L. D. Carlos, Adv. Opt. Mater., 2021, 9, 2101495.
35. A. N. Carneiro Neto, E. Mamontova, A. M. P. Botas, C. D. S. Brites, R. A. S. Ferreira, J. Rouquette, Y. Guari, J. Larionova, J. Long and L. D. Carlos, Adv. Opt. Mater., 2022, 10, 2101870.
36. F. E. Maturi, C. D. S. Brites, E. C. Ximendes, C. Mills, B. Olsen, D. Jaque, S. J. L. Ribeiro and L. D. Carlos, Laser Photonics Rev., 2021, 2100301.
37. S. F. H. Correia, A. R. N. Bastos, M. Martins, I. P. E. Macário, T. Veloso, J. L. Pereira, J. A. P. Coutinho, S. P. M. Ventura, P. S. André and R. A. S. Ferreira, Adv. Sci., 2022, 9, 2104801.
38. S. F. H. Correia, L. Fu, L. M. S. Dias, R. F. P. Pereira, V. de Zea Bermudez, P. S. André and R. A. S. Ferreira, Nanoscale Adv., 2023, 5, 3428–3438.
39. J. F. C. B. Ramalho, S. F. H. Correia, L. Fu, L. M. S. Dias, P. Adão, P. Mateus, R. A. S. Ferreira and P. S. André, npj Flexible Electron., 2020, 4, 11.
40. L. M. S. Dias, J. F. C. B. Ramalho, T. Silverio, L. Fu, R. A. S. Ferreira and P. S. Andre, IEEE Access, 2022, 10, 24433–24443.
41. G. Figueiredo, S. F. H. Correia, L. Fu, V. de Zea Bermudez, A. N. Carneiro Neto, P. S. André and R. A. S. Ferreira, in Handbook on the Physics and Chemistry of Rare Earth, ed. J. G. Bünzli and S. M. Kauzlarich, Elsevier B.V., 2024.
42. R. A. S. Ferreira, S. F. H. Correia, L. Fu, P. Georgieva, M. Antunes and P. S. André, Sci. Rep., 2024, 14, 4160.
43. P. S. André, L. M. S. Dias, S. F. H. Correia, A. N. Carneiro Neto and R. A. S. Ferreira, Sol. Energy, 2024, 268, 112290.
44. L. Ming, I. Zabala-Gutierrez, P. Rodríguez-Sevilla, J. R. Retama, D. Jaque, R. Marin and E. Ximendes, Adv. Mater., 2023, 35, 2306606.
45. E. P. Santos, R. S. Pugina, E. G. Hilário, A. J. A. Carvalho, C. Jacinto, F. A. M. G. Rego-Filho, A. Canabarro, A. S. L. Gomes, J. M. A. Caiut and A. L. Moura, Sens. Actuators, A, 2023, 362, 114666.
46. G. Figueiredo, S. F. H. Correia, B. P. Falcão, V. Sencadas, L. Fu, P. S. André and R. A. S. Ferreira, Adv. Sci., 2024, 11, 2400540.
47. J. F. C. B. Ramalho, L. D. Carlos, P. S. André and R. A. S. Ferreira, Adv. Photonics Res., 2021, 2, 2000211.
48. L. Đorđević, F. Arcudi, M. Cacioppo and M. Prato, Nat. Nanotechnol., 2022, 17, 112–130.
49. P. Yu, X. Wen, Y.-R. Toh and J. Tang, J. Phys. Chem. C, 2012, 116, 25552–25557.
50. L. M. S. Dias, L. Fu, R. F. P. Pereira, A. N. Carneiro Neto, V. de Zea Bermudez, P. S. André and R. A. S. Ferreira, FlexMat, 2024, 1, 116–126.
51. L. Wang, Y. Wang, T. Xu, H. Liao, C. Yao, Y. Liu, Z. Li, Z. Chen, D. Pan, L. Sun and M. Wu, Nat. Commun., 2014, 5, 5357.
52. F. Yuan, T. Yuan, L. Sui, Z. Wang, Z. Xi, Y. Li, X. Li, L. Fan, Z. Tan, A. Chen, M. Jin and S. Yang, Nat. Commun., 2018, 9, 2249.
53. Y. Ding, X. Wang, M. Tang and H. Qiu, Adv. Sci., 2022, 9, 2103833.
54. J. Fayos, J. Solid State Chem., 1999, 148, 278–285.
55. C. H. Mak, R. Liu, X. Han, Y. Tang, X. Zou, H. Shen, Y. Meng, G. Zou and H. Hsu, Adv. Opt. Mater., 2020, 8, 2001023.
56. C. Li, Y. Wang, H. Jiang and X. Wang, J. Electrochem. Soc., 2020, 167, 037540.
57. W. U. Khan, L. Qin, A. Alam, P. Zhou, Y. Peng and Y. Wang, ACS Appl. Bio Mater., 2021, 4, 5786–5796.
58. L. J. Mohammed and K. M. Omer, Nanoscale Res. Lett., 2020, 15, 182.
59. Y. Han, Y. Liu, H. Zhao, A. Vomiero and R. Li, J. Mater. Chem. B, 2021, 9, 4111–4119.
60. Y. Li, X. Xiao, Z. Wei and Y. Chen, Z. Anorg. Allg. Chem., 2022, 648, e202100323.
61. Z. Zhou, Z. Song, J. Liu, B. Lei, J. Zhuang, X. Zhang, Y. Liu and C. Hu, Adv. Opt. Mater., 2022, 10, 2100704.
62. W. Li, W. Zhou, Z. Zhou, H. Zhang, X. Zhang, J. Zhuang, Y. Liu, B. Lei and C. Hu, Angew. Chem., 2019, 131, 7356–7361.
63. B. S. D. Onishi, A. N. Carneiro Neto, R. Bortolleto-Santos, V. R. Masterlaro, L. D. Carlos, R. A. S. Ferreira and S. J. L. Ribeiro, Nanoscale, 2024, 16, 6286–6295.
64. J. Wang, Y. Yang and X. Liu, Mater. Adv., 2020, 1, 3122–3142.
65. L. Ding, X. Jin, Y. Gao, J. Wu, T. Ai, H. Zhou, X. Chen, X. Zhang and W. Chen, Adv. Opt. Mater., 2023, 11, 2202349.
66. X. Xu, L. Mo, Y. Li, X. Pan, G. Hu, B. Lei, X. Zhang, M. Zheng, J. Zhuang, Y. Liu and C. Hu, Adv. Mater., 2021, 33, 2104872.
67. U. Rocha, L. E. G. Armas, W. F. Silva, M. R. Dousti, A. L. Moura, A. Novatski, N. G. C. Astrath and C. Jacinto, Spectrochim. Acta, Part A, 2024, 317, 124409.
68. Q. Lou, N. Chen, J. Zhu, K. Liu, C. Li, Y. Zhu, W. Xu, X. Chen, Z. Song, C. Liang, C. Shan and J. Hu, Adv. Mater., 2023, 35, 2211858.
69. P. K. Samanta, D. Kim, V. Coropceanu and J.-L. Brédas, J. Am. Chem. Soc., 2017, 139, 4042–4051.
70. L. Naimovičius, P. Bharmoria and K. Moth-Poulsen, Mater. Chem. Front., 2023, 7, 2297–2315.
71. J. Tan, R. Zou, J. Zhang, W. Li, L. Zhang and D. Yue, Nanoscale, 2016, 8, 4742–4747.
72. Z. Zhu, Z. Sun, Z. Guo, X. Zhang and Z. Wu, J. Colloid Interface Sci., 2019, 552, 572–582.
73. S.-H. Yang, S.-M. Liao, Y.-Y. Tsai, C.-H. Wang and C.-C. Ho, J. Alloys Compd., 2023, 933, 167761.
74. A. Zhang, Z. Sun, Z. Wang, M. Jia, B. C. Choi, Z. Fu, J. H. Jeong and S. H. Park, Scr. Mater., 2022, 211, 114515.
75. Y. Ding, Y. Lu, K. Yu, S. Wang, D. Zhao and B. Chen, Adv. Opt. Mater., 2021, 9, 2100945.
76. T. Xiao, Y. Li, T. Wang, Y. Fan, F. He, Q. Wang, J. Han, Z. Yin, Z. Yang, J. Qiu and Z. Song, Mater. Chem. Front., 2021, 5, 4280–4290.
77. V.-T. Peltoketo, J. Imaging Sci. Technol., 2015, 59, 010401–1–010401–7.

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Footnote

† Electronic supplementary information (ESI) available: Additional details on the synthesis procedures, material characterization, photoluminescence spectra, and the scripts used for thermometric analysis are provided. See DOI: https://doi.org/10.1039/d4nr03155e

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