Nanoplasmonic biosensors for environmental sustainability and human health

Abstract

Monitoring the health conditions of the environment and humans is essential for ensuring human well-being, promoting global health, and achieving sustainability. Innovative biosensors are crucial in accurately monitoring health conditions, uncovering the hidden connections between the environment and human well-being, and understanding how environmental factors trigger autoimmune diseases, neurodegenerative diseases, and infectious diseases. This review evaluates the use of nanoplasmonic biosensors that can monitor environmental health and human diseases according to target analytes of different sizes and scales, providing valuable insights for preventive medicine. We begin by explaining the fundamental principles and mechanisms of nanoplasmonic biosensors. We investigate the potential of nanoplasmonic techniques for detecting various biological molecules, extracellular vesicles (EVs), pathogens, and cells. We also explore the possibility of wearable nanoplasmonic biosensors to monitor the physiological network and healthy connectivity of humans, animals, plants, and organisms. This review will guide the design of next-generation nanoplasmonic biosensors to advance sustainable global healthcare for humans, the environment, and the planet.

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Wenpeng Liu

Wenpeng Liu is a postdoctoral fellow at Harvard Medical School. He received his BS and PhD in Instrument Science and Technology from Tianjin University in 2017. His main research interests are integrated molecular diagnostic systems, nanoplasmonic materials, protein engineering, and biosensing.

Kyungwha Chung

Kyungwha Chung is a Research Professor at the Institute of Quantum Biophysics at SKKU in South Korea and a Research Scholar at Harvard Medical School, working with Prof. Luke Lee. She received her PhD at Ewha Womans University in 2018 under the supervision of Prof. Dong Ha Kim. Her current research focuses on exploring biological systems with nanoplasmonics and inorganic nanomaterials.

Subin Yu

Subin Yu is currently working as a postdoctoral fellow at Harvard Medical School. She received her PhD at Ewha Womans University in 2023 under the supervision of Prof. Dong Ha Kim. Her research interests are molecular diagnostics, organs/organoids-on-a-chip integrated with real-time biosensors for monitoring disease pathogenesis, and therapeutic and its mechanism of action/interactions to replace the current in vivo model.

Luke P. Lee

Luke P. Lee is currently a Professor of Medicine at Harvard Medical School, Harvard University, and Brigham and Women's Hospital. He received his BA in Biophysics from UC Berkeley and PhD in Applied Physics and Bioengineering from UC Berkeley. His current research interests are integrated molecular diagnostic systems for the early detection of infectious diseases, cancer, and neurodegenerative diseases, the human brain on chips, in vitro neurogenesis, organoids on chips, and quantum biological electron tunneling in living organisms.

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1. Introduction

Environmental health is a field that studies how environmental factors affect human health. It focuses on assessing and safeguarding human health. Creating healthier environments can prevent almost one-quarter of the global disease burden.¹ Environmental pollution has become a global threat that endangers humanity and ecological sustainability due to industrial development and human activities.^2–4 Environmental pollution is the leading cause of death in low- and middle-income nations, causing over 9 million fatalities annually, which accounts for one in six global deaths.⁵ Pollution has infiltrated the deepest ocean and highest mountain peaks and seeps into our food chain.⁶ The escalation of chronic respiratory diseases, lung cancers, and heart disease can be attributed to the alarming statistic that over 99 percent of the population live in areas where the air pollution is above World Health Organization (WHO) air quality guidlines.^7,8 Moreover, the warming climate has propelled disease-carrying mosquitoes faster than ever before.⁹ The COVID-19 pandemic and other infectious diseases have highlighted the intricate relationship between humans and the environment.¹⁰ In light of these pressing concerns, the imperative for collective actions to safeguard human health and the planet's well-being becomes evident. It is mandatory to support the United Nations Sustainable Development Goals¹¹ and forge a healthier and more sustainable future for humanity and the earth, our home.

In order to safeguard environmental and human health, it is crucial to actively monitor the impact of pollutants on the environment, plant life, and animals. Among numerous technologies devoted to monitoring environmental health,^12–15 nanoplasmonic optical sensors have emerged as powerful tools due to their unique ability to detect and quantify various analytes with high specificity and sensitivity. Nanoplasmonic optical sensors are based on the interaction of light with nanoscale nanostructures, such as gold nanoparticles (AuNPs),¹⁶ gold nanorods (AuNRs),¹⁷ gold hexaflexagon,¹⁸ silver nanoparticles (AgNPs),¹⁹ silver nanorods (AgNRs),²⁰ aluminum nanoparticles (AlNPs),²¹ aluminum nanopillars,²² gold nanofilm,²³ gold nanopillar array,²⁴ gold nanohole array,²⁵ and AuNP on a mirror (Fig. 1),²⁶ that exhibits localized surface plasmon resonance (LSPR) with an enhanced localized electromagnetic field. Compared with the conventional surface plasmon resonance (SPR), which occurs when electrons in a thin metal sheet are excited by the incident light that is directed to the sheet with a particular angle of incidence and travels parallel to the sheet (Fig. 2a), LSPR occurs when confined free electrons oscillate with the same frequency as the incident light on the surface of plasmon nanostructures with a size comparable to or smaller than the wavelength of the incident light, resulting in a localized electromagnetic field (Fig. 2b).^27,28 LSPR has unique advantages that make it as the next-generation sensor technology.^29–32 First, the change of the local refractive index in the vicinity of the nanoplasmonic structure surface can be measured due to the refractive index-dependent property of LSPR. The collective oscillation of surface electrons of metallic nanostructures resonates with light at a specific frequency, producing a highly confined electromagnetic field near the surface that is sensitive to small amounts of analytes. Second, diverse designs of nanoplasmonic biosensors are possible. Tremendous efforts have been made to synthesize nanoplasmonic structures with various shapes and compositions and fabricate nanoplasmonic substrates using lithography. Third, the high tunability of absorption and scattering wavelengths of nanoplasmonic structures extends the broad application of plasmonic nanoparticles as optical labels at different wavelengths. Optical properties can be accurately tuned based on the optical design, shapes, composition, and distance between the particles/patterns. Fourth, the robust optical properties and high stability of nanoplasmonic structures, mainly consisting of noble metals, exhibit superior performance compared with conventional optical labels, such as organic fluorophores (Table 1). Unlike organic fluorophores or quantum dots, the optical properties of plasmonic nanoparticles remain consistent without suffering from photobleaching or blinking. Fifth, the surface functionalization of nanoplasmonic materials is simple, flexible, and stable, which is favorable for specific and versatile sensing.³³ Moreover, since nanoplasmonic structures concentrate the light from an area exceeding their physical dimensions to a significantly smaller volume and generate an enhanced electromagnetic field with a high scattering cross-section, they exhibit substantially higher signals than other optical materials. Therefore, nanoplasmonic biosensors possess exceptional merits for environmental monitoring, where detecting small molecules and sensing diverse biomarkers of different sizes are necessary.

Fig. 1 Overview of advanced nanoplasmonic biosensors for environmental sustainability and human health. A variety of plasmonic nanostructures, such as colloid nanoparticles, plasmonic dimers, plasmonic nanofilms, plasmonic nanopillar arrays, plasmonic nanohole arrays, and plasmonic nanoparticles on a mirror, are utilized to detect different size-scale analytes, including molecules, EVs, pathogens, and cells for environmental sustainability and human health monitoring.

Fig. 2 Basics of different sensing mechanisms of nanoplasmonic biosensors. (a) Schematic showing the principle of the surface plasmon resonance (SPR). The plasmon propagates in the x- and y-directions along the metal–dielectric interface and decays evanescently in the z-direction. The reflectance of light is measured as a function of incidence angle. (b) Schematic showing the principle of the localized surface plasmon resonance (LSPR). The plasmon oscillates locally around metal nanoparticles at the resonant frequency. The absorbance of light is measured as a function of the wavelength. (c) Refractive index (RI)-dependent LSPR. The binding of analytes to nanoplasmonic structures leads to the change of refractive index (RI) and the subsequent shift of LSPR. (d) Coupled LSPR. The binding of analytes to nanoplasmonic structures leads to the change in the distance and the subsequent coupling between adjacent nanoplasmonic structures, which can be detected as a shift in the extinction spectrum. (e) Surface-enhanced Raman scattering (SERS). Surface plasmons of the nanoplasmonic structures enhance the Raman scattering, a unique spectral fingerprint of analytes. (f) Extraordinary optical transmission (EOT). The binding of analytes to the nanoplasmonic metasurface leads to the change of the local refractive index and the subsequent shift of the EOT spectrum. (g) Photothermal conversion through nanoplasmon excitation (①), and heat generation and dissipation (②). (h) Plasmon-quenched fluorescence. The energy is transferred from the excited state of the fluorophore to the surface plasmons of adjacent nanoplasmonic structures when the fluorophore's emission spectrum overlaps with the nanoplasmonic structure's extinction spectrum. (i) Plasmon-enhanced fluorescence. The energy is transferred from the nanoplasmonic structure to the fluorophores with enhanced fluorescence emission when the fluorophores’ excitation spectrum overlaps with the adjacent nanoplasmonic structure's extinction spectrum. (j) Plasmon resonant energy transfer (PRET) or quantum biological electron tunneling (QBET). The energy is transferred from the collective movement of free electrons in the nanoplasmonic structures, such as AuNP, to adsorbed analytes when the plasmon resonance frequency of nanoplasmonic structures overlaps with the frequency of the absorption spectrum of analytes, leading to resonant quenching dips in the scattering spectrum of the plasmonic nanoparticle. When the gap between nanoplasmonic structures and biomolecules decreases to the threshold of tunnel distance at the sub-nanometer scale, the spill-out-of plasmon-induced surface electrons tunnel across the potential barrier and transfer from nanoplasmonic structures to biomolecules via linkers. (k) Quantum electrodynamics (QED). In nanocavities, biomolecules are excited by the plasmonic resonance energy, leading to energy exchange from nanoplasmonic structures to biomolecules and vice versa through the linker layer and the energy level splitting of the coupled system. (l) Catalytic activation via nanoplasmonics-induced electron (①), photon (②), and heat (③) redistribution.

Table 1 Comparison of AuNPs, quantum dots, and organic fluorophores as optical probes

	AuNPs	Quantum dots	Organic fluorophores
Photobleaching	No	Yes	Yes
Blinking	No	Yes	Yes
Excitation/emission wavelength	Tunable	Tunable	Fixed
Surface functionality	Good	Poor	Poor
Cytotoxicity	Low	High	High
Brightness	High	Low	Low

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This review provides an overview of recent advancements in nanoplasmonic optical sensors for monitoring environmental sustainability and human health based on target analytes of different sizes and scales. It discusses the detection of common analytes, such as environmental pollutants and biomolecules. We explore the potential of nanoplasmonics-based analysis for monitoring ecological and human health by studying extracellular vesicles (EVs). We investigate recent advances in nanoplasmonic optical sensors for detecting environmental pathogens and analyzing cells. Additionally, we examine the possibility of using wearable nanoplasmonic biosensor technologies to monitor human physiological parameters, providing new insights for healthcare management. This review will provide a framework for comprehending the latest advancements in nanoplasmonic optical sensors to monitor environmental sustainability and human health. Furthermore, we aim to raise awareness among government organizations, researchers, and the general public and encourage communities to take further steps towards improving both environmental and human health. Innovative biosensors are critical in accurately monitoring health conditions, discovering the intricate links between the environment and human well-being, and comprehending how environmental factors can trigger autoimmune, neurodegenerative, and infectious diseases.

2. Fundamental principles and mechanisms of nanoplasmonic biosensors

Different properties of nanoplasmonics can be applied to environmental and human health monitoring. For the refractive index-dependent LSPR nanoplasmonic sensor, nanoplasmonic structures are typically functionalized with molecules that bind to target analytes. The binding of analytes on the surface of nanoplasmonic structures leads to the change of local refractive index and the subsequent shift of the extinction spectrum (Fig. 2c). For the coupled-LSPR nanoplasmonic optical sensor, the nanoplasmonic structures are functionalized with ligands specific to a particular analyte of interest (Fig. 2d). The binding of analytes to the nanoplasmonic structures leads to the change in the distance and the subsequent coupling between the adjacent nanoplasmonic structures, which can be detected as a shift in the extinction spectrum.^34,35

Surface-enhanced Raman scattering (SERS) is a molecular vibrational spectroscopy approach that enhances Raman scattering signals close to nanoplasmonic structures (Fig. 2e).^36–38 Raman scattering is the inelastic scattering of photons by molecules with scattered photons having a lower (Stokes scattering) or a higher (anti-Stokes scattering) energy than the incident photons through the light interaction with intra-molecular vibrations and rotations, thus Raman scattering provides a structural fingerprint to identify target molecules. The excitation of surface plasmons on the nanoplasmonic structure surface can create a strong local electromagnetic field interacting with the Raman-active molecule, thus enhancing the Raman signal by several orders of magnitude.

Extraordinary optical transmission (EOT) is an optical phenomenon characterized by a significant increase in light transmission through a subwavelength aperture on a nanoplasmonic metasurface,³⁹ such as plasmonic nanocup arrays,⁴⁰ and plasmonic nanohole arrays (Fig. 2f).⁴¹ This enhanced transmission originates from the constructive interference between the scattered surface plasmon polaritons and the directly transmitted light. The binding of analytes to the nanoplasmonic metasurface changes the local refractive index and the subsequent shift of the transmission spectrum.

Upon the LSPR excitation in the nanoplasmonic structures, electromagnetic decay occurs through either the radiative decay by re-emitting photons with the same wavelength as the incident light or the non-radiative decay by transferring the energy to hot electrons. Non-radiative decay in nanoplasmonic structures offers unique advantages by absorbing photonic energy and converting it to localized heat by photon–electron–phonon coupling (Fig. 2g).^42–44 Specifically, the light with a specific wavelength excites free electrons in a highly athermal distribution at the nanoplasmonic structure surface via photo–electron coupling. Then, the energy is redistributed through electron–electron scattering, leading to the subsequent electron thermalization at a new Fermi distribution and the rapid temperature increase at the nanoplasmonic structure surface. Afterward, the electron thermalization gives rise to the heat transfer from the electrons to the nanoplasmonic structure lattice through electron–phonon coupling and the subsequent heating of entire nanoplasmonic structures. Finally, the surrounding media is heated by heat dissipation from the nanoplasmonic structures via the phonon–phonon coupling. A key advantage of plasmon-based photothermal conversion is the localized heating of medium around nanoplasmonic structures.

Plasmon quenching is a phenomenon that occurs when the fluorescence emission from a fluorophore is suppressed by the presence of nanoplasmonic structures (Fig. 2h).⁴⁵ This effect arises when the nanoplasmonic structures are close to the fluorophores whose emission spectrum overlaps with the nanoplasmonic structure's extinction spectrum, leading to energy transfer from the excited state of the fluorophore to the surface plasmons of the nanoplasmonic structures. In contrast, plasmon-enhanced fluorescence refers to the significant enhancement of a fluorophore's fluorescence emission when it couples with the LSPR of nanoplasmonic structures (Fig. 2i).^46,47 This effect arises when nanoplasmonic structures are close to the fluorophores whose excitation spectrum overlaps with the nanoplasmonic structure's extinction spectrum, leading to energy transfer from the nanoplasmonic structure to the fluorophores, which increases the excitation rate and subsequent fluorescence emission. If the nanoplasmonic structure's extinction spectrum overlaps with both the fluorophore's emission and excitation spectra, the practical fluorescence enhancement is dependent on the competition between enhancement and quenching effects. A spacer with a modest thickness is generally included between the fluorophore and nanoplasmonic structure to avoid fluorescence quenching.

Plasmon resonant energy transfer (PRET) is a spectroscopy technique that involves transferring energy by matching the plasmon resonance frequency of nanoplasmonic structures with the electronic transition energy frequency of a biomolecule. This results in resonant quenching dips in the particle's Rayleigh scattering spectrum (Fig. 2j).^48,49 This technology allows for capturing quantum biological electron transfer (QBET) in biological systems. QBET involves many critical biological processes, such as photosynthesis, cellular respiration, DNA repair, cellular homeostasis, and cell death. QBET spectroscopy can monitor real-time electron transfer dynamics in cytochrome c during cellular apoptosis and necrosis in living cells.⁵⁰ This non-invasive real-time QBET spectroscopic imaging of electron transfer dynamics in live cells can revolutionize environmental sciences and healthcare by capturing spatiotemporal electron transfer dynamics, revealing quantum biological mechanisms in ecological pathogens, and monitoring electron transfer dynamics of plant systems in real-time.

When the light–matter coupling occurs in the nanocavity with a subnanometer gap within nanoplasmonic structures such as AuNP-on-mirror^51–53 and gold nanocavity between gold nanocages and gold film,⁵⁴ the coupling between the nanoplasmonic structures and biomolecules via linker interaction can enter the regime of quantum mechanics containing a strong coupling regime, which can be elaborated via cavity quantum electrodynamics (QED) (Fig. 2k). The strong coupling between the nanoplasmonic structures and molecules in nanocavity leads to the energy exchange and level splitting of the molecules at room temperature.^49,54 Plasmonic nanoparticles-based cavity QED spectroscopy, an experimental technique that studies the interaction between light and matter, could be used to monitor environmental health.⁵⁴ For instance, investigating the effect of cytochrome enzymes (CYPs) on plants under stress such as heat, cold, heavy metals, ozone, radiation, and nutrient deficiencies is essential.⁵⁵ CYPs are critical enzymes that are crucial in detoxifying harmful substances (xenobiotics) and producing plant metabolites, antioxidants, and phytohormones. Real-time monitoring of the electron transfer dynamics of CYPs in plants and similar correlation studies in humans and animals can provide insight into sustainable health.

The photocatalytic activities of nanoplasmonic structures can promote molecular interactions via the redistribution of electrons, photons, and heat over time and space under light illumination (Fig. 2l).^56,57 This catalyzes the molecular interactions and helps improve the detection performance. In the case of electron redistribution, the excited electrons transfer from the surface of plasmonic nanostructures to the adjacent molecule, thus mediating the molecular interactions. This process requires overlapping the excited electron's energy and the molecule's electronic band structure. In the case of photon redistribution, the enhanced electromagnetic field around plasmonic nanostructures promotes molecular excitation for the adjacent molecule whose absorption spectrum overlaps with the plasmonic nanostructure's absorption spectrum; thus, the molecular interaction rate is greatly improved. In the case of heat redistribution, the photothermal effect of nanoplasmonic structures can increase the ambient temperature to accelerate molecular interactions. Nanoplasmonic structures also offer a desirable physical interface for an active surface-mediated catalytic reaction to detect analytes.⁵⁸

3. Nanoplasmonic biosensors for the detection of molecules from environments

The predominant environmental pollutants include heavy metals, volatile organic compounds (VOCs), antibiotics, persistent organic pollutants (POPs), pesticides, and toxins, which are all small molecules with sizes at nano/sub-nano scale and lead to severe consequences and long-term effects of health after exposure via air, water, food, etc.^59,60 Regular surveillance of these molecular environmental pollutants is indispensable for safeguarding human health. Plasmonic nanomaterials provide an advancing platform for molecular detection with high sensitivity owing to their enhanced interaction with nano/sub-nanoscale molecules.⁶¹ This contributes to early warning of potential environmental risks from pollutant molecules. In this section, we will evaluate recent advances in nanoplasmonics for monitoring molecular pollutants from the environment, which enables us to establish a preventive and predictive paradigm to enhance environmental and human health.

Heavy metals, such as mercury, lead, chromium, cadmium, and arsenic, can be progressively accumulated in the human body from the environment through the food chain and threaten human health even in trace amounts by causing acute or chronic illness.⁶² Mercury is regarded as the most toxic heavy metal element found in air, water, soil, cosmetics, and food.⁶³ The exceptional and specific galvanic reaction/amalgamation of mercury ions toward AgNPs can lead to the blue shift of the LSPR peak by forming a mercury shell on the surface of the AgNP, which can be used to develop low-cost and portable nanoplasmonic sensors for the colorimetric detection of mercury ions.^64–66 Besides, the plasmonic resonant energy transfer (PRET) can be employed for sensitively detecting heavy metals based on the selective interplay between AuNPs and the conjugated metal–ligand complex, which offers a high spatial resolution and excellent sensitivity higher than organic fluorophore-based methods (Fig. 3a).⁶⁷ The overlap between the absorption spectrum of the metal–ligand complex and the scattering spectrum of AuNP induces energy transfer and distinguishable quenching of the AuNP's scattering spectrum. In another report, AuNP and urine were employed as the active components for the selective colorimetric assay of mercury ions.⁶⁸ When urine is added to the AuNP solution, nitrogen-rich components are adsorbed on the AuNP surface via an electrostatic effect to induce minor color change. In the presence of mercury ions, the coordination chemistry between uric acid, creatinine, and mercury ion induces aggregation of AuNPs, resulting in an instant and significant red-to-blue color change.

Fig. 3 Nanoplasmonic biosensors for detecting nano/sub-nanoscale molecules for environmental monitoring. (a) Detecting heavy metals based on the PRET between conjugated metal–ligand complexes and a single AuNP. The overlap between the absorption spectrum of the metal–ligand complex and the scattering spectrum of AuNP induces the energy transfer and the distinguishable quenching of the scattering spectrum of AuNP. Reproduced with permission from ref. 67. Copyright 2009 Springer Nature. (b) Plant VOC detection via cysteine (Cys)-functionalized AuNRs as plasmonic aggregative colourants. Reproduced with permission from ref. 69. Copyright 2019 Springer Nature. (c) Nanoplasmonic optical fiber sensors to detect antibiotics. Reproduced with permission from ref. 70. Copyright 2020 Springer Nature. (d) Plasmonic chirality-based aptasensor for detecting BPA. The presence of BPA leads to the disaggregation of asymmetric dimers into dispersed nanoparticles with a decreased circular dichroism intensity. Reproduced with permission from ref. 71. Copyright 2013 American Chemical Society. (e) Flexible and adhesive SERS active tape for detecting pesticide residues from fruit peel surface. The substrate is constructed by decorating the commercial tape with colloidal AuNPs as SERS probes. Reproduced with permission from ref. 72. Copyright 2016 American Chemical Society. (f) Colorimetric detection of cholera toxin in lake water using colloid AuNPs. The presence of cholera toxin leads to the aggregation of antibody-conjugated AuNPs and results in discernible color change. Reproduced with permission from ref. 73. Copyright 2015 Elsevier B.V.

Volatile organic compounds (VOCs) are organic compounds with high vapor pressure that are readily vaporized at room temperature. VOCs originate from various sources, including natural origin and human activities. Since VOC exposure can adversely affect human health, such as headaches, nose, eye, and throat irritation,⁷⁴ monitoring environmental VOC levels is beneficial in various applications, including agriculture, air quality control, and public safety. Nanoplasmonic materials are promising candidates as electronic noses for optical VOC sensing applications. Close-packed AuNPs have been self-assembled as the nanoplasmonic film in the LSPR-based system to detect methanol vapor up to 4.63% of concentration via the refractive index change originating from the adsorption of methanol molecules.⁷⁵ Nanoplasmonic arrays are also promising candidates, such as the artificial nose for multiplex detection of VOCs, which mimics natural olfactory systems via various sensitive membranes on nanoplasmonic substrates, and the advanced algorithms to generate distinct response patterns. Recently, bimetallic nanocubes were integrated as SERS substrates into a microfluidic chip and applied as a plasmonic VOC sensor for indoor air pollution monitoring with high sensitivity at ppb level.⁷⁶ In this study, the plasmonic gas microsystem was designed as a 3 × 3 array to detect an arbitrary combination of adsorbed VOCs. Besides, the microfluidic design of the chip can improve the detection sensitivity by capturing and enriching VOCs on the surface of the sensing array. A set of cylindrical premixers is also patterned ahead of the sensing array to guarantee consistent flow throughout the array. Advanced materials, such as metal–organic frameworks,^77–79 graphene,^80,81 and polymers,⁸² can be combined with nanoplasmonics as high-performance VOC sensors.

Plants can also release VOCs, which are ecological in attracting species-specific pollinators and the aerial communication between plants.⁸³ Plant VOCs can also be released as biomarkers from leaves, flowers, and other tissues to the environment via complicated mechanisms to reflect their health status.⁸⁴ Therefore, advanced sensors that can detect plant VOCs to safeguard plant health are needed to ensure global food security, sound agricultural practices, and natural ecosystem sustainability. Cysteine-functionalized AuNPs and AuNRs were integrated as plasmonic aggregative colorants into a non-invasive and smartphone-based colorimetric sensor for specific recognition of VOC biomarker ((E)-2-hexenal) emitted from tomato leaves during pathogenic infection with the detection limit at the ppm level within 1 min (Fig. 3b).⁶⁹ In another report, a gold nanoisland sensor coated with molecularly imprinted sol–gel was developed for detecting cis-jasmone vapor, one of the plant VOCs, as a biomarker for herbivorous insect infestation.^85,86 The plant VOC sensor can further be engineered as a wearable device for continuously monitoring plant health for smart agriculture.^87,88

The widespread overuse of antibiotics and their release to the environment has led to the “slow-moving pandemic” of antimicrobial resistance (AMR), which occurs when changes in bacteria cause the drugs used to treat infections to become less effective.⁸⁹ According to some estimates, there were 4.95 million deaths associated with bacterial AMR in 2019.^90,91 Since most antibiotics and other pharmaceuticals penetrate the natural environment via toilets, wastewater, runoff water, landfills, yards, and industrial discharges after use for human and animal health purposes,⁹² the reliable surveillance of antibiotics from these sources can prevent and control AMR in the environment.^93,94 Nanoplasmonic optical fiber sensors can realize the detection of antibiotics based on the nanoplasmonic absorption of the fiber-optical evanescent wave by attached AuNPs and the multiple total internal reflections to increase the light–matter interaction, which leads to the decrease of transmission light intensity. For example, a nanoplasmonic optical fiber was developed for detecting ampicillin in spiked milk samples with high sensitivity (Fig. 3c).⁷⁰ This biosensor is based on the competitive assay where free ampicillin molecules compete with ampicillin-conjugated AuNPs to bind with capture antibodies immobilized on the fiber core surface. Without free ampicillin molecules, ampicillin-conjugated AuNPs will bind to capture antibodies with the maximum amount, thus leading to a tremendous decrease in the transmitted light intensity. In the presence of free ampicillin molecules, the competition between the free ampicillin molecules and ampicillin-conjugated AuNPs to bind to capture antibodies hinders the decrease of the transmitted light intensity. Nanoplasmonic fiber Bragg gratings, fabricated by coating metal nanolayers on optical fiber containing short segments of perpendicular,⁹⁵ or tilted Bragg reflector,^96–98 have attracted more interest as nanoplasmonic biosensors due to their miniaturization and superior performance. Tilted fiber Bragg gratings (TFBGs) excite the cladding modes of the fiber, favoring the interaction with the analytes in the external medium. This enhanced interaction induces changes in the spectral features of SPR, making TFBGs effective for biosensing applications.⁹⁶

Persistent organic pollutants (POPs) are organic compounds resistant to environmental degradation and cause a “silent” threat to environmental sustainability and human health. POPs can lead to serious health issues, including endocrine disruption, reproductive problems, cancer, diabetes, obesity, and cardiovascular problems.⁶⁰ Generally, POPs are analyzed via a mass spectrophotometer, which suffers from high cost and complexity. One type of widely-used POPs is industrial chemicals, such as polychlorinated biphenyls, hexachlorobenzene, hexachlorobutadiene, and short-chain chlorinated paraffins.⁹⁹ Nanoplasmonics offers a promising choice for the on-site detection of POPs. For example, a flexible membrane of polyamide nanofibers grafted with silver nanosheets was employed as a 3D SERS substrate for detecting polychlorinated biphenyls in the environment.¹⁰⁰ The high density of 3D SERS hotspots within the membrane ensures high SERS activity. Bisphenol A (BPA) is one of the most commonly produced industrial chemicals primarily used as a co-monomer in manufacturing various plastics.¹⁰¹ Ironically, BPA was originally invented as an artificial estrogen in 1891, and the first evidence of BPA toxicity was confirmed in the 1930s.¹⁰² While not technically a POP owing to the short half-life, BPA is usually cataloged as a POP due to its omnipresence in the environment. Studies have indicated that BPA can disrupt normal endocrine function,¹⁰³ and continuous exposure to BPA from daily supplies such as food chains and food packaging may influence sexual development and reproduction in humans.¹⁰⁴ A plasmonic chirality-based aptasensor was developed to monitor environmental BPA with a detection limit of 0.008 ng mL⁻¹, which is based on the formation of an asymmetric AuNP dimer by hybridizing anti-BPA aptamer and its complementary sequence attached to two different AuNPs, respectively (Fig. 3d).⁷¹ The anti-BPA aptamer will competitively bind to BPA, resulting in the disaggregation of AuNP dimers into dispersed AuNPs and the subsequent decrease of circular dichroism intensity. Besides the anti-BPA aptamer, anti-BPA antibody can also form an asymmetric AuNP dimer as a plasmonic chirality-based sensor for BPA detection.¹⁰⁵ The molecularly imprinted SPR sensor is an alternative for BPA detection using BPA imprinted polymer nanofilm as the recognition element, which avoids the requirement of expensive antibodies or aptamers.¹⁰⁶

Another type of POPs is pesticides, which are used in agriculture to kill unwanted plants, insects, fungi, and other organisms that are harmful to crops. Pesticide residues on food surfaces and in surface water/groundwater pose a significant threat to human health. Continuous exposure to pesticide residues causes headaches, insomnia, dizziness, hand tremors, fatigue, and other neurological symptoms in adults and children.¹⁰⁷ A flexible SERS tape was used to rapidly detect pesticide residues from fruit peel surfaces by immobilizing colloidal AuNPs as SERS probes on the commercial tape (Fig. 3e).⁷² This flexible SERS tape can collect pesticide residues from fruit peel surfaces through a simple “paste and peel off” step. Other nanoplasmonic structures, such as AuNPs,¹⁰⁸ Au/Pt nanoflower,¹⁰⁹ Au nanoslit,¹¹⁰ AuNP–AgNP,¹¹¹ Ag-decorated PDMS nanotentacle array,¹¹² Ag-coated AuNPs,¹¹³ and Ag/Au hollow hexagonal nanoplates,¹¹⁴ are also employed for detecting pesticide residues. Colorimetric sensor arrays, consisting of a group of plasmonic nanoparticles spotted on the origami paper, offer a method for multiplex detection of various pesticide aerosols based on the pesticide-induced aggregation of capping agent-functionalized AuNPs.¹¹⁵ To simplify the analysis, multiplex detection of pesticides can be achieved by incorporating AuNPs and multiple fluorescent components into a single well. This allows for simultaneous measurement of colorimetric and fluorescent responses.¹¹⁶ This variation in responses is modulated by the inner filter effect mechanism, which is triggered by AuNP aggregation in the presence of pesticides.

Toxins are also one of the analytes secreted from pathogens into the environment. Toxins cause diseases by inhibiting enzymes and undermining body functions, displacing structural minerals, damaging the organs, DNA, and cell membranes, modifying gene expression, and interfering with hormones.¹¹⁷ Therefore, toxins pose a threat worldwide, particularly in resource-limited settings with unsafe drinking water and sanitation.¹¹⁸ Rapid analysis of toxins is one of the most challenging topics in food/water quality and safety. To solve this issue, an AuNP-based colorimetric assay was developed to rapidly and sensitively detect cholera toxin in lake water (Fig. 3f).⁷³ The presence of cholera toxin causes the aggregation of antibody-conjugated AuNPs due to the specific recognition between the B-subunit of cholera toxin and antibody. Since each cholera toxin has five B-subunits, the binding between antibody and cholera toxin will result in a network of aggregated AuNPs, causing a significant color change that can be visually observed. In another report, plasmon coupling enhanced Raman scattering nanobeacon was developed for detecting cholera toxin in a one-step manner via a monosialoganglioside-incorporated phospholipid-coated AuNP probe with better sensitivity and wider dynamic range than colorimetric assay.¹¹⁹ This approach relies on the monosialoganglioside-incorporated phospholipid-coated AuNPs, which aggregate and generate a significantly enhanced SERS signal in the presence of cholera toxin. The phospholipid bilayer coated on the AuNP surface allows the anchor of many monosialoganglioside molecules on the AuNP surface to bind to cholera toxin and improve the stability and biocompatibility of AuNPs.

Several examples of nanoplasmonics-based detection of organic molecules discussed here employed highly sensitive SERS techniques or colorimetric assays using plasmonic nanoparticles as optical labels. These detection techniques are easier and more straightforward than conventional molecule identification methods, such as nuclear magnetic resonance and mass spectrometry coupled with liquid/gas chromatography. As a result, nanoplasmonic optical sensors offer an excellent alternative for monitoring and mitigating the threat posed by heavy metals, VOCs, antibiotics, POPs, toxins, and other environmental molecular pollutants.

4. Repurposable nanoplasmonic biosensors for monitoring environmental sustainability and human health

Apart from the above-mentioned molecular pollutants, various biomolecules exist as biomarkers in large amounts in the environment. For example, DNA/RNA from environmental sources, such as air, water, soil, and sediment, can provide information to assess ecological health.^120,121 Different nanoplasmonics-based approaches have been developed as highly sensitive biomolecular sensing platforms^122,123 and have shown potential as innovative solutions for the early and accurate detection of biomolecules from the environment.

Lateral flow assay is the most successful technique using colloidal gold as the optical label for point-of-care (POC) detection of biomolecules with naked eye readout. The advantages of lateral flow assays include speed, simplicity, and ease of use. Thermal contrast amplification is applied to lateral flow assay to improve the sensitivity by utilizing the photothermal conversion effect and quantifying the temperature increase of colloidal gold accumulated at the test line under the light illumination.¹²⁴ Thermal contrast amplification of colloidal gold can also be integrated into the programmed photothermal plate reader for enzyme-linked immunosorbent assay (ELISA) with high sensitivity and throughput.^125,126 Apart from the paper-based lateral flow assay and well plate-based ELISA, thermal contrast amplification-based on-chip biosensors fabricated by semiconductor manufacturing technology realize the integration of a thin-film platinum resistor as the temperature sensor.¹²⁷ High-throughput and multiplexing lateral flow assay is another trend for sensing that allows for the parallel detection of different analytes.¹²⁸

Nanoplasmonics enhance the sensitivity of immunoassay by amplifying output signals based on various mechanisms. First, ELISA reagents/by-products can modulate the growth of plasmonic nanoparticles to amplify the signal in the solution-based assay. A nanoplasmonic ELISA was developed to detect cancer biomarkers by utilizing the enzymatic label of ELISA to control AuNP growth (Fig. 4a).¹²⁹ Without the prostate-specific antigen, hydrogen peroxide rapidly reduces gold ions to form quasi-spherical and non-aggregated AuNPs, leading to the red-colored solution. In the presence of the prostate-specific antigen, the enzyme catalase, which specifically binds to prostate-specific antigen, consumes hydrogen peroxide and slows down the AuNP growth, generating aggregated AuNPs with ill-defined morphologies, resulting in the blue-colored solution. Second, plasmonic nanostructures can boost the fluorescent signal in immunoassay via the plasmon-enhanced fluorescence effect. For instance, the plasmonic-fluor, which is composed of a nanoplasmonic core, a polymer shell as the spacer layer, and molecular fluorophores, can serve as an ‘add-on’ label that exhibits a significant improvement of detection limit in fluorescence-linked immunosorbent assays and lateral flow assays (Fig. 4b).^130,131 The plasmon-enhanced fluorescence effect also facilitates the continuous tracking of analytes at the single molecule level via the repeated binding of analytes to the AuNR array with low-affinity interaction in complex matrices.¹³² Third, nanoplasmonic substrates can improve the sensitivity by enhancing the fluorescence signal in the surface-based immunoassay. Based on this, plasmonic chips comprising abundant neighboring gold nanoislands were explored to enhance the near-infrared fluorescence and detect islet cell-targeting autoantibodies for type 1 diabetes diagnostics from blood samples via the plasmon-enhanced fluorescence (Fig. 4c).¹³³ Fabricated by “bottom-up”^122,133 or “top–down” approach,¹³⁴ these plasmonic chips generate hotspots within nanogaps between nanoislands to enhance the electric field and fluorescence signal. They were further applied for the multiplex detection of lung cancer biomarkers¹³⁵ and the diagnosis of myocardial infection.¹³⁶ In-plane plasmonic AuNP dimer arrays with nanogaps can also enhance the fluorescence for single-molecule detection.¹³⁴ Fourth, the combination of plasmon-enhanced fluorescence and tyramide signal amplification, which employ horse radish peroxidase to catalyze the binding of fluorophore tyramides to tyrosine residues of analytes, possesses the potential to design dual-enhanced nanoplasmonic biosensors for detecting low-abundance targets.¹³⁷

Fig. 4 Repurposable nanoplasmonic biosensors for detecting nano/sub-nanoscale molecules for environmental sustainability and human health monitoring. (a) Plasmonic ELISA for colorimetric detection of prostate-specific antigen by controlling AuNP growth. Reproduced with permission from ref. 129. Copyright 2012 Springer Nature. (b) Plasmonic fluor as an “add-on” label with enhanced fluorescence emission for immunoassays. Plasmonic fluor consists of an AuNR, a polymer shell as a spacer layer, fluorophores, and biotins as universal biorecognition elements. BSA is employed to assemble all components into the functional nanoconstruct and resist non-specific binding. Reproduced with permission from ref. 130. Copyright 2020 Springer Nature. (c) Plasmonic nanoisland-based chip for type 1 diabetes diagnostics via plasmon-enhanced fluorescence (PEF). Reproduced with permission from ref. 133. Copyright 2014 Springer Nature. (d) Plasmonic nanosensor for alkaline phosphatase (ALP) and human immunoglobulin G detection via enzyme-triggered click chemistry. Reproduced with permission from ref. 138. Copyright 2014 American Chemical Society. (e) Core–satellite substrate for colorimetric detection of trypsin. Reproduced with permission from ref. 139. Copyright 2011 American Chemical Society. (f) Interferometric reflectance imaging sensing (IRIS)-based chip with plasmonic nanorod labels. Reproduced with permission from ref. 140. Copyright 2018 American Chemical Society. (g) Quantification of Aβ fibrogenesis by tracking AuNP's Brownian movements. Reproduced with permission from ref. 141. Copyright 2014 Royal Society of Chemistry. (h) Plasmon-accelerated electrochemical reaction for glucose detection. Reproduced with permission from ref. 142. Copyright 2017 American Chemical Society. (i) SERS-based VOC sensor comprising AuNPs for diagnosing gastric cancer from exhaled breath. RGO: reduced graphene oxide. Reproduced with permission from ref. 143. Copyright 2016 American Chemical Society.

The distribution of hot spots on the nanoplasmonic substrate plays a key role in SERS-based sensors. The substrate with nanoplasmonic structures that generate abundant and high-intensity hot spots can enhance the SERS signal.^144–152 Since the electromagnetic field is higher at the sharp curvature edges or tips, surface-roughened AuNPs are great candidates as sensitive optical probes in molecular detection. The sub-nanoscale topology of virus capsids was used as the template to form surface-roughened AuNPs with reasonable regularity and uniformity by depositing gold onto the surface as SERS sensors.¹⁵³ More hot spots were created by the crests and valleys of capsid topology than by AuNP's smooth surface under light illumination, resulting in enhanced Raman scattering and improved sensitivity for cytochrome c detection. The gold nanoflowers with branched tips were also synthesized to provide abundant hot spots at the surface with SERS sensitivity ten times better than that of gold nanospheres.¹⁵⁴

The sensitivity of AuNP-based colorimetric assay can be improved via click chemistry, which allows for simple, rapid, and efficient conjugation of reagent pair by carbon–heteroatom link (C–X–C),^155,156 thus leading to AuNP aggregation for plasmonic readout. As one of the frequently-used reactions of click chemistry, Cu-catalyzed azide/alkyne cycloaddition (CuAAC) was used to design a plasmonic nanosensor for immunoassay of alkaline phosphatase and human immunoglobulin G with enhanced sensitivity via the enzyme-triggered click chemistry (Fig. 4d).¹³⁸ The sensitivity and selectivity of this plasmonic nanosensor are enhanced by (1) dephosphorylation of ascorbic acid–phosphate where one alkaline phosphatase enzyme generates many ascorbic acid molecules to reduce Cu(II) to Cu(I), (2) CuAAC reaction via the catalysis of Cu(I) to amplify the detectable signals, and (3) the AuNP aggregation as naked-eye plasmonic readout. The stabilizing agent and the inertness of click chemistry at ambient conditions also ensure the excellent stability of AuNPs. Apart from the conventional click chemistry, the click chemical ligation, which enables covalent bond formation between two oligonucleotide strands,^157,158 can also be integrated into the AuNP-based colorimetric assay for ultrasensitive detection of nucleic acids without the requirement of a metal catalyst.¹⁵⁹

Core–satellite nanoplasmonic structures, comprising one large nanoplasmonic core surrounded by several small nanoplasmonic satellites, are promising candidates for designing versatile nanoplasmonic sensors. Core–satellite nanostructures possess an enhanced plasmonic coupling resonance with high scattering intensity due to a series of hot spots between the cores and satellites, thus offering an opportunity to maximize the shift of the plasmonic resonance peak upon assembly and disassembly of the satellite particles around the core. In addition, core–satellite nanostructures feature multiple design capabilities of nanoplasmonic sensors by adjusting various parameters, including the size of the core and satellite, the number of satellites, and the interparticle distance. A label-free and on-chip colorimetric method was proposed to detect miRNA at the picomolar level based on assembling AuNP core–satellite nanostructures.¹⁶⁰ In this assay, AuNP cores were initially anchored on the glass slide. Then, the hybridization of two oligonucleotides and target miRNA-21 resulted in the assembly of core–satellite nanostructures and scattering color change. In another report, core–satellite nanoplasmonic structures were attached to a glass slide for monitoring protease trypsin activity (Fig. 4e).¹³⁹ The presence of the protease trypsin led to the disassembly of core–satellite nanostructures, and the color changed from orange to green with a wavelength shift of more than 70 nm that was observed with the naked eye.

The combination of nanoplasmonics and interferometric reflectance allows localizing molecules in bright fields based on the superposition of a reference and scattered light fields. Interferometric reflectance imaging sensing (IRIS) technique was proposed to design a digital microarray on a polished silicon substrate with a transparent silicon dioxide (SiO₂) thin film to detect the hepatitis B virus surface antigen (Fig. 4f).¹⁴⁰ The light (E_sca) scattered by functionalized AuNR interferes with the reflected light (E_ref) to form a faint diffraction-limited interference pattern, which can be imaged by an interferometric scanner using a 10× objective lens.

Tracing the trajectories of plasmonic nanoparticles by recording the scattering is an advantageous method to monitor the dynamic fluctuation of target molecules. The kinetics of amyloid fibril formation and growth were analyzed by tracing the Brownian movement of AuNPs in a noninvasive and label-free manner to investigate the pathological mechanism of Alzheimer's disease (Fig. 4g).¹⁴¹ AuNPs suspended in the aqueous solution without amyloid β (Aβ) moved unrestrictedly and showed broadly spread trajectories. In contrast, AuNPs suspended in the aqueous solution containing Aβ showed restricted trajectories due to fibrillogenesis, an indicator of Alzheimer's disease. In another assay, the temporal coincidences between the trajectories of AuNRs and AgNPs were analyzed via dual-color light scattering with photon cross-correlation spectroscopy for DNA detection.¹⁶¹ Without target DNA, AuNRs and AgNPs moved independently with uncorrelated respective time traces of their scattering signals. Upon exposure to target DNA, AuNRs and AgNPs formed aggregates and diffused, yielding temporal coincidences between the Rayleigh scattering signals. These trajectory studies were enabled due to the stability of the plasmonic nanoparticles. Besides, the misfolding of proteins into oligomeric and fibrillar aggregates can also be monitored by the AuNR array-based infrared sensors for assessing the development and progression of neurodegenerative disorders.¹⁶²

Nanoplasmonic structures can enhance sensing performance by promoting molecular interactions through their catalytic activities, including photocatalytic and active surface-mediated catalytic reactions. A plasmon-enhanced electrochemical sensor was reported for glucose detection via the photocatalytic property of AuNPs to excite hot carriers and accelerate the electrochemical reactions (Fig. 4h).¹⁴² Upon the light illumination, the energetic charge carriers excited by LSPR accelerated the electrocatalytic oxidation of glucose owing to their matched energy levels, and hot electrons were driven to the external circuit to generate recordable currents. AuNPs can also serve as nucleation sites to modulate the self-assembly of analytes. For example, the surface-mediated catalytic property of AuNPs was employed to monitor the Aβ aggregation and inhibition.⁵⁸ Owing to the electrostatic interactions between negatively-charged AuNPs and positively-charged Aβ, AuNPs acted as nucleation sites to promote the self-assembly of Aβ. AuNPs also served as colorimetric nanoplasmonic optical reporters for rapidly detecting Aβ aggregation. Platinum-coated gold (Au@Pt) nanoflowers are efficient artificial nanozymes for catalytic amplification and plasmonic coupling enhancement in the nanoplasmonic immunosensor.¹⁶³

Exhaled breath contains various VOCs that reflect health conditions.¹⁶⁴ VOCs in the body migrate through the tissues, are released into the bloodstream, and are finally excreted into the exhaled breath by the gas change in the lungs, making it a unique exhaled breath print of diseases.¹⁶⁴ Therefore, exhaled breath has been widely investigated for monitoring body health in a convenient and noninvasive manner. For instance, a SERS sensor comprising AuNPs and reduced graphene oxide film was employed to detect VOC biomarkers in exhaled breath for gastric cancer diagnostics (Fig. 4i).¹⁴³ The reduced graphene oxide film selectively adsorbed the VOC biomarkers from exhaled breath, while AuNPs were used to detect adsorbed VOC biomarkers through SERS signals. Plasmonic nanoparticles can also be embedded in the porous filter paper as a colorimetric nano-optoelectronic nose to detect VOCs from human exhaled breath for gastric cancer diagnosis.¹⁶⁵

Nanoplasmonic metasurfaces, which comprise arrays of plasmonic meta-atoms such as nanorods, nanopillars, nanoholes, nanodiscs, and nanoengravings with subwavelength scale,¹⁶⁶ have emerged as promising nanoplasmonic biosensors with unique optical properties for environmental monitoring. In nanoplasmonic metasurfaces, the strong light–matter interaction can be effectively modulated by controlling the geometry, periodicity, and materials of the meta-atoms to optimize the sensitivity. Pixelated nanoplasmonic metasurfaces have been designed for the multiplex detection of different biomolecules by metasurface-driven PRET hyperspectral imaging.¹⁶⁷ This pixelated nanoplasmonic metasurface sensor comprises clustered aluminum nanodisks with different periods that modulate the plasmonic resonance frequency of each pixel, thus the scattering peak of the metapixels can cover the entire visible regime with a wide spectral range for the multiplex PRET-based detection of chlorophyll a, chlorophyll b, and cytochrome c. Besides, interface self-assembly of AuNRs,¹³⁴ and silver nanocubes,¹³⁵ was reported to fabricate nanoplasmonic metasurface as SERS substrate for detecting the bacterial quorum sensing molecule pyocyanin with the detection limit of 10⁻¹² M and organophosphorus pesticides residues with the detection limit of 0.15 μg L⁻¹ from the environmental water, respectively.

Expectedly, by repurposing the nanoplasmonic-based biomolecular detection technologies, more innovative solutions will emerge for detecting environmental biomolecules, such as proteins, lipids, carbohydrates, nucleic acids, phytoplankton pigments, secondary metabolites, and other bioindicators, all of which indicate the environmental health and help us understand the impact of human activities, pollution, climate change, and other factors on environment sustainability.

5. Nanoplasmonic biosensors for EVs from environments

Exposure to environmental pollutants induces cellular stress in plants, animals, and humans, which triggers various cellular responses, resulting in the release of EVs.^168,169 EVs are lipid bilayer particles secreted from almost all types of cells into the extracellular space. The three main subtypes of EVs are exosomes, microvesicles, and apoptotic bodies based on the mechanism by which they are released from the cells and differentiated via their size and content.¹⁷⁰ Exosomes are the smallest EVs with a size of 30–150 nm and are generated by fusing intracellular multivesicular bodies with the plasma membranes to release the vesicular contents into the extracellular space. In comparison, microvesicles are EVs with a size of 150–1000 nm and formed by budding of the plasma membrane, whereas apoptotic bodies are cell fragments during programmed cell death. EVs mediate intercellular communication by delivering molecular contents, such as microRNAs, message RNAs (mRNAs), proteins, enzymes, growth factors, and cytokines.¹⁷¹ EV analysis is a rapidly growing field for monitoring environmental and human health with unique advantages. First, since EVs have emerged as novel intercellular mediators in pathophysiological conditions and the progression of various diseases, including cardiovascular disease,^172,173 Alzheimer's disease and Parkinson's disease,¹⁷⁴ kidney disease,¹⁷⁵ and cancer,^176,177 they are promising candidates for early-stage disease diagnostics.^178,179 Second, EVs are abundant in nearly all biofluids, such as saliva, blood, tears, urine, breast milk, and cerebrospinal fluid, which are favorable for sample collection. Third, EVs comprise a lipid bilayer membrane to ensure stable encapsulation of biomarkers by protecting them from the extracellular environment.^180,181 Fourth, molecular contents are enriched within EVs with higher concentrations than the free-floating molecules in the extracellular environment.¹⁸²

Although diverse populations of EVs are known to be prevalent in many natural ecosystems where they mediate complex networks of interactions between microbes and their local environment, their roles within the natural ecosystems and their relationship with environmental health have been overlooked. For example, researchers report that marine microorganisms continuously release plentiful exosomes containing a set of substances into seawater, and analyzing these environmental exosomes provides information on marine microbial communities.¹⁸³ Meanwhile, EVs derived from medicinal plants are often rich in various biologically active contents for disease treatment.¹⁸⁴ Monitoring the EVs of plants, pathogens, microbes, animals, and humans can offer a new perspective on environmental and human health stress. Since the size of EVs falls within the decay length (which is approximately half the wavelength of the incident light) of the evanescent field of surface plasmons, nanoplasmonics can be promising candidates for detecting EVs. In this section, we discuss the nanoplasmonics detection of EVs, which can be applied for environmental and human health monitoring.

Plasmonic nanoparticles can be functionalized with the antibodies that bind to the EV membrane markers like endosome-specific tetraspanins (CD3, CD9, CD63, CD81) for EV detection. A nanoplasmonics-enhanced scattering (nPES) assay was developed using antibody-conjugated AuNPs and AuNRs to quantify the tumor-derived EVs in plasma samples (Fig. 5a).¹⁸⁵ In the presence of target EVs, both the antibody-conjugated AuNPs and AuNRs bind to EVs at a close distance, leading to the coupled scattering that shifts from green (gold nanosphere, AuNS) and red (AuNR) to yellow color with markedly increased scattering intensity. The biomimetic cell membrane-coated surface possesses high antigenic diversity, which is favorable for capturing many EVs from biofluid and enhancing the signal from plasmonically encoded nanoprobes.¹⁸⁶

Fig. 5 Repurposable nanoplasmonic biosensors for detecting sub-microscale EVs for environmental and human health monitoring. (a) Nanoplasmon-enhanced scattering (nPES) assay for tumor-derived EV quantification. EVs are captured by EV-specific antibodies immobilized on a sensor chip, and labeled by the antibody-conjugated gold nanospheres and nanorods to produce a local plasmon effect with enhanced sensitivity and specificity. Reproduced with permission from ref. 185. Copyright 2017 Springer Nature. (b) Templated plasmonics for exosomes (TPEX) technology using exosomes as templates for gold nanoshell growth to quench the fluorescence on the membrane for cancer diagnostics. Reproduced with permission from ref. 187. Copyright 2020 American Association for the Advancement of Science. (c) Multicolor visual method for exosome detection via magnetic bead (MB)-based exosome isolation, hybridization chain reaction, and enzyme-induced silver deposition on AuNRs for signal amplification. Reproduced with permission from ref. 188. Copyright 2019 American Chemical Society. (d) EOT-based detection of exosomes using plasmonic nanohole array sensor for cancer diagnostics. Reproduced with permission from ref. 189. Copyright 2014 Springer Nature. (e) Elliptically-shaped plasmonic nanopillar sensors for single exosome detection. Reproduced with permission from ref. 190. Copyright 2018 Public Library of Science. (f) Interferometric plasmonic microscopy (iPM) for single exosome detection. The image results from the interference between the reflective light (E_ref) and the SPs scattered by the object at the near field (E_sc), which back-propagates onto the far-field detector. The right images show the snapshots of exosome adsorption onto a positive-charge-modified gold film. Reproduced with permission from ref. 23. Copyright 2018 National Academy of Sciences, USA.

EV-templated gold growth provides a method for directly analyzing EVs in native clinical biofluid. For instance, templated plasmonics for exosomes (TPEX) technology was developed for optical absorbance analysis of exosomes in clinical biofluids (Fig. 5b).¹⁸⁷ In this report, exosomes are first labeled with small AuNPs serving as seeds, followed by in situ reduction of gold ions to generate plasmonic nanoshells with a significant red shift of the plasmonic resonance. Furthermore, TPEX technology is optimized by combining exosome-templated gold growth and plasmon-quenched fluorescence to realize the multiparametric molecular profiling of exosomal biomarkers. Specifically, the exosome surface was first labeled with fluorescent molecular probes and AuNPs, followed by in situ formation of gold nanoshells to quench the fluorescence of probes bound to exosomes. Since the gold nanoshell formation is templated by the exosome dimensions and the gold nanoshell plasmonics locally quenches fluorescence probes only when they are target-bound on the same exosomes, this assay design possesses multiselectivity to exosome biophysical properties and colocalized biomolecular contents of the same exosomes. Meanwhile, this assay remains no response to free molecules during the detection of various exosomal biomarkers.

Isolation and enrichment of EVs from the biofluids are necessary steps for obtaining accurate biological information with high sensitivity.¹⁹¹ Antibody-functionalized magnetic beads can be integrated into nanoplasmonic sensors to capture and enrich EVs on the nanoplasmonic surface, breaking the limitation in mass transfer and binding affinity with enhanced sensitivity.^192,193 A multicolor sensor was developed for detecting exosomes with high sensitivity using the magnetic bead-based exosome enrichment, dual signal amplification strategy of hybridization chain reaction, and enzyme-catalyzed metallization of AuNRs (Fig. 5c).¹⁸⁸ In this assay, exosomes were captured and enriched by CD63 aptamer-functionalized magnetic beads, followed by inserting cholesterol-modified DNA probes into the lipid membrane of exosomes through hydrophobic interaction. The sticky end of DNA probes triggered the hybridization chain reaction process to enhance the alkaline phosphatase loading and boost the production of ascorbic acids. The reaction between silver ions and ascorbic acid motivated the deposition of silver shells on AuNRs, leading to the blueshift of the nanoplasmonic resonance peak that can be observed by naked eyes.

The EOT effect of nanoplasmonic metasurface has become a promising technology for EV detection. In the presence of EVs, the transmission spectrum through the nanoplasmonic metasurface features a red shift as the surface-bound EVs increase the interfacial refractive index (Fig. 5d).^189,194 The multi-functionalization of the plasmonic nanohole array with a group of EV-specific antibodies enables the simultaneous profiling of EV membrane proteins. Double-layered plasmonic nanohole array and enzymatic transformation of localized insoluble deposits over sensor-bound EVs allow increased sensitivity for EV detection.¹⁹⁵

The plasmonic nanopillar array can be optimized with the approximate size of a single EV, thus allowing the differentiation between small free molecules and large EVs by the signature of localized surface plasmon resonance imaging substrate (Fig. 5e).¹⁹⁰ Meanwhile, the gold sensing elements are raised above the nanopillar to reduce background interference caused by the nonspecific binding of free molecules to the underlying substrate.

Nanoplasmonic structures can enhance the interferometric scattering of EVs, thus holding great potential for the detection of a single EV. For instance, a nanoplasmonic interferometer array was developed for analyzing exosomes via ring-hole nanoplasmonic structures.¹⁹⁶ The nanoplasmonic interferometer array comprises a nanohole surrounded by concentric circular nanogrooves in a gold nanofilm. Upon illuminating the ring-hole nanoplasmonic structure, SPR is generated within the grooves and the surface plasmon waves travel radially along the rings toward the central nanohole, where they interact with the directly transmitted light through the central nanohole. The immune-capture of exosomes on the surface leads to a change in the intensity of the transmitted light. Interferometric plasmonic microscopy (iPM) can monitor the real-time absorption of a single exosome onto a plasmonic nanofilm based on the common-path interferometry (Fig. 5f).²³ The laser light (E_inc) with an inclined incident angle illuminates a gold nanofilm to stimulate SPR around the gold surface. The interference between the reflective light (E_ref) and the scattered light of surface plasmons (E_sca), which is affected by the presence of exosomes at the near field, is collected by the far-field camera and reconstructed to manifest the real-time exosome adsorption.

6. Nanoplasmonics biosensors to identify pathogens from environments

Environmental pathogens are the primary contaminants that result in waterborne, foodborne, and airborne diseases.¹⁹⁷ Diseases caused by waterborne pathogens pose significant global health concerns, leading to more than 2.2 million deaths per year.¹⁹⁸ The airborne pathogens can also be released via exhalation, talking, coughing, or sneezing into the environment.^199,200 Hence, there is a tremendous rise in demand for POC detection of pathogens in the environment, such as wastewater,²⁰¹ and aerosol,²⁰⁰ for preventing and controlling infectious disease outbreaks. Viruses and bacteria are two typical pathogens that can lead to infectious diseases in humans. Viruses are submicroscopic infectious agents with a size of 20–200 nm in general, and they replicate inside living cells, leading to viral diseases such as AIDS, COVID-19, influenza, and hepatitis. Since there are structural similarities between viruses and exosomes, nanoplasmonics for exosome assay can be applied for virus detection. For instance, the nanoplasmonic biosensor with EOT effect can realize one-step rapid quantification of SARS-CoV-2 virus particles,²⁰² and a single influenza viral particle can be imaged by the interferometric plasmonic microscopy.²⁰³ Bacteria are small single-celled organisms with a typical length of 0.5–2 μm. Some bacteria cause human illness by replication or toxin release, resulting in bacterial infections. The conventional approach for bacteria identification is the bacterial culture test, but it requires a lengthy processing time. Integrated POC molecular diagnostics devices are becoming a trend for pathogen identification and infectious disease diagnostics at home.^204,205 As an alternative, nanoplasmonic biosensors provide superior advantages over conventional methods for POC detection of environmental pathogens with high sensitivity.

The virus-induced aggregation of plasmonic nanoparticles represents a typical colorimetric technique for virus detection. Plasmonic nanoparticles are commonly functionalized with antibodies that bind to viral surface proteins, thus forming nanoplasmonic aggregates on the viral surface with strong plasmon coupling.^206,207 To efficiently form nanoplasmonic aggregates with a pronounced color change, plasmonic nanoparticles can be functionalized with multiple antibodies targeting several types of viral surface proteins.²⁰⁸ Apart from viral surface proteins, viral nucleic acids can also be selected as targets to induce the aggregation of plasmonic nanoparticles capped with aptamers like antisense oligonucleotides.^209,210

Combining nanoplasmonic sensors with the aerosol sampler allows the rapid and on-site detection of airborne pathogens in environmental aerosols with high sensitivity.^211–214 A nanoplasmonic bioaerosol sensing system was developed for detecting environmental SARS-CoV-2 viruses via a moisture exchanger for aerosol sampling and a gold nanoisland biosensor for isothermal amplification and on-site detection of viral nucleic acids with a detection limit of 0.25 copies per μL (Fig. 6a).²¹⁵ In this system, a hygroscopic growth unit comprising a heating-based and tube-in-shell moisture exchanger was incorporated into the aerosol-to-hydrosol sampler to enlarge the aerosols via vapor condensation, thus enhancing sampling efficiency toward nanoscale aerosols. Specifically, the water vapor induced by heating will condense and attach to the non-hygroscopic virus aerosols in a supersaturated laminar flow tube, resulting in hygroscopic virus aerosols with enlarged size and mass. The hygroscopic virus aerosols will be collected by the aerosol-to-hydrosol sampler with high efficiency due to the increased inertia. In the next step, a gold nanoisland biosensor was employed to amplify viral nucleic acids via its photothermal effect and quantify the immobilized viral sequences via the plasmonic phase changes. Nanoplasmonic sensors can also be embedded in the outer membrane of the face mask, which can capture the potential airborne pathogens from the ambient environment via continuous breath.²¹⁶

Fig. 6 Nanoplasmonic biosensors for identifying micro/sub-micro scale pathogens from environments. (a) Nanoplasmonic biosensing system for on-site quantification of SARS-CoV-2 viruses from environmental aerosols. The thermoplasmonic property of gold nanoisland (AuNI) is used for cyclic amplification of the SARS-CoV-2 sequence. Reproduced with permission from ref. 215. Copyright 2022 Wiley. (b) Ultrafast photonic PCR using a gold nanofilm as a light-to-heat converter. Reproduced with permission from ref. 217. Copyright 2016 Wiley. (c) 3D nanoplasmonic antenna array sensor for ultrasensitive Ebola virus antigen sensing. The sensor utilizes gold disks, gold planes, and gold nanodots to enhance the fluorescence signal emission from the molecules. Reproduced with permission from ref. 218. Copyright 2019 Wiley. (d) Waterborne pathogen detection via the hydrodynamic enrichment of AuNP-coated bacteria and amplified SERS. The constructive interference between incident light and diffracted light at the fringe of the nanopore leads to strong near-field enhancement between the AuNPs on the plasmonic bacteria as well as the AuNPs and the gold mirror. Reproduced with permission from ref. 26. Copyright 2018 Springer Nature. (e) Liposome-amplified plasmonic immunoassay for colorimetric detection of single-digit pathogens. The amplified response is initiated by a triggered breakdown of cysteine-loaded nanoliposomes and subsequent aggregation of AuNPs. Reproduced with permission from ref. 219. Copyright 2015 American Chemical Society. (f) Nanoplasmonic optical antennas for the dynamic monitoring of oscillatory enzyme activity of bacteria. AzoR: azoreductase, OMVs: outer membrane vesicles. Reproduced with permission from ref. 220. Copyright 2023 Springer Nature.

Apart from the isothermal amplification, nanoplasmonics can also realize the ultrafast photonic PCR, where the nanoplasmonic structures are used for thermal cycling to amplify nucleic acids under the illumination of a laser or LED for viral nucleic acid amplification via the photothermal effect. Ultrafast photonic PCR possesses unique advantages over the conventional approach. Direct volumetric heating using mono-disperse plasmonic nanoparticles can rapidly transfer the heat energy to the liquid without heating up the PCR tube wall, which is favorable to rapid thermal cycling. Gold nanospheres,²²¹ gold nanobipyramids,²²² and AuNRs,²²³ were used for the ultrafast photonic PCR and the 30-cycling assay time was reduced from more than one hour to less than several minutes. Plasmonic magnetic nanoparticles, which consist of a magnetic core and a plasmonic gold shell, possess the merit of in situ fluorescence detection of PCR products via magnetic clearance.^224,225 Nanoplasmonic colorimetric PCR was developed for quantitative nucleic acid detection by harnessing the photothermal properties of plasmonic magnetic nanoparticles and inducing a color change by oxidating chromogenic substrates, which originated from the fluorescence dye's photocatalytic activity.²²⁶ Plasmonic nanofilm array is another promising candidate for the on-chip ultrafast photonic PCR with real-time quantification of nucleic acids for POC sensing applications (Fig. 6b).^24,217 Besides, the plasmonic nanopillar array presents an excellent photothermal effect for photonic PCR via gold nanoislands which are randomly distributed on the surface of the nanopillar array to generate strong electromagnetic hotspots and enhance broadband light absorption.^24,227,228 Nanoplasmonic structures can also accelerate the amplification of nucleic acids via the injection of hot electrons from the surface of the nanoplasmonic sensor into the nanoliter volume of the media, thereby facilitating the electron-driven nucleophilic phosphodiester bond formation during the nucleic acid amplification.²²⁹

The photothermal effect of nanoplasmonic structures can also heat up the surrounding solution and generate bubbles, which can be used for detecting viruses with exceptional sensitivity. Digital plasmonic nanobubble detection (DIAMOND) was reported to detect SARS-CoV-2 viruses based on bubble generation by photothermal conversion of AuNPs with the detection limit of 1 RNA copy per μL.²³⁰ Within DIAMOND, an optofluidic system is employed to guide the flow of the AuNP suspension through a micro-capillary, where two laser beams are aligned to synchronically generate and detect the plasmonic bubbles, which represent the amount of heating and absorption cross-section of AuNPs. The presence of viruses in the AuNP suspension generates a core-satellites structure with increased average size, leading to larger plasmonic nanobubble signals.

3D nanoplasmonic substrates possess enhanced sensitivity for pathogen detection compared to 2D nanoplasmonic substrates, owing to the abundance of hot spots and biomarker binding sites that enhance the light–matter interaction within the 3D nanoplasmonic structures. Advanced nanofabrication technologies, such as nanoimprinting,²¹⁸ laser interference lithography patterning,²³¹ and metal-assisted chemical etching,^232–234 provide opportunities to construct large-scale and highly-ordered 3D nanoplasmonic structures for pathogen detection. For example, scalable nanoimprinting processes were applied to fabricate 3D plasmonic nanoantenna arrays for the ultrasensitive immunoassay of Ebola virus antigens at ultralow concentration in human plasma (Fig. 6c).²¹⁸ The sensor comprises a 3D plasmonic nanopillar array with a gold nanodisk atop each pillar, gold nanodots on the pillar sidewall, and a gold backplane at the base of nanopillars. The nanodisks and backplanes form a plasmonic nanocavity to enhance light absorption at the nanoantenna's resonance wavelength. The gold nanodots within the nanocavity can additionally boost the local electromagnetic field to interact with the Ebola virus antigens and significantly enhance the fluorescent signal by 130 folds compared to a 2D gold nanofilm. A 3D periodic plasmonic nanocup array has also been fabricated by laser interference lithography patterning for one-step POC detection of SARS-CoV-2 viruses with a detection limit of 370 vp per mL.²⁰² Electron irradiation-induced self-assembly allows the plasmonic nanoribbon layer to curve and form 3D nanosplit rings as optofluidic sensors.²³⁵

Integration of nanoplasmonic biosensors with microfluidic systems provides a promising opportunity for automatic sample preparation and multiplex sensing in resource-limited settings. Microfluidics allows for the precise manipulation and analysis of small volumes of fluids, making it an ideal platform for sample preparation and processing. For example, an integrated chip, named LIGHT, was developed for rapid and precise identification of E. coli based on the photothermal effect of nanoplasmonics for cell lysis and photonic PCR.²³⁶ Within the LIGHT chip, a plasmonic nanoporous membrane was used to realize on-chip E. coli enrichment, photothermal cell lysis, and photonic PCR with the detection limit of 10³ CFU mL⁻¹ in 10 min. In another report, an integrated optofluidic platform comprising an AuNP-on-mirror structure was demonstrated for effective hydrodynamic trapping of waterborne pathogens and SERS signal amplification (Fig. 6d).²⁶ In this integrated optofluidic platform, bacteria were trapped and enriched around nanopores via the hydrodynamic force. For the optical detection, the SERS signal was amplified by the enhanced Raman scattering, which originates from the plasmonic coupling between the AuNPs and the gold. In addition, the constructive interference between incident light and diffraction light at the edge of the plasmonic nanohole enhances the near field between AuNPs on the bacteria and the gold nanofilm, thus leading to the further improvement of the SERS signal. AuNP-on-mirror structure is similar to the AuNP dimer, and the plasmonic coupling between the plasmonic AuNP and plasmonic mirror allows for single-molecule detection.^237,238 Another advantage of optofluidic immunoassays is the feasibility of multiplex detection by functionalizing multiple sensing areas with different capture antibodies targeting different analytes in microfluidic channels.²³⁹

Liposome-amplified nanoplasmonic immunoassay offers an ultrahigh sensitivity for pathogen identification. In this system, a small amount of analytes triggered the release of a large amount of the media from the liposome to induce AuNP aggregation, thus amplifying the signal. A colorimetric immunoassay of single-digital pathogen was demonstrated using cysteine-loaded liposomes to amplify the signal from AuNPs (Fig. 6e).²¹⁹ The presence of one bacteria induced the formation of the immunocomplex decorated with cysteine-loaded liposomes, followed by adding AuNP colloids. Then, a buffered surfactant was added to the assay to immediately hydrolyze the liposomes and subsequently release the encapsulated cysteine molecules. The cysteine acts as a cross-linker to induce rapid AuNP aggregation by binding to the AuNP surface via thiol groups, and the amine and carboxyl groups of cysteine promote the intermolecular crosslink between cysteine molecules through hydrogen bonds. Since liposomes offer a cell–membrane-mimicking environment, the cysteine-loaded liposomes are also used for the direct colorimetric detection of enzyme sphingomyelinase activities.²⁴⁰

Spatiotemporal monitoring of protein/nucleic acid secretions from environmental bacteria at the single-cell level can reveal the signaling pathways for cellular communications and identify the biomarkers from the environment. AuNRs were employed as nanoplasmonic optical antennas for the long-term and label-free dynamic monitoring of oscillatory enzyme activity of individual live bacteria (Fig. 6f).²²⁰ In this report, the enzyme azoreductase release via bacterial outer membrane vesicles was monitored by the nanoplasmonic optical antennas according to their scattering intensity change from the suppression state to the recovery state after enzyme detection, revealing the periodic oscillatory enzyme activity in different living environments and at different stages of bacterial growth.

Overusing antibiotics leads to the spread of antibiotic-resistant pathogens,²⁴¹ posing severe environmental and human health problems.²⁴¹ Antimicrobial susceptibility testing (AST) is a commonly used technique to guide appropriate antibiotic treatment. It is based on slow bacterial culturing, which usually takes 48–72 hours,^242–244 thus posing a risk to the patient's life. To address the need for rapid and POC AST, nanoplasmonic biosensors have been developed to detect bacterial metabolic activity. A colorimetric platform was constructed for the AST of E. coli and K. pn within 100 minutes based on their catalase expression. It catalyzes H₂O₂ hydrolysis and modulates the reduction of gold ions to AuNPs with different morphologies.²⁴⁵ Moreover, antibiotic susceptibility influences the metabolic activity of cultured bacteria towards Cu²⁺, which induces aggregation of cysteine-modified AuNPs and generates a color change in approximately 3 hours.²⁴⁶ Another phenotypical difference between antibiotic-resistant and antibiotic-susceptible pathogens is the surface potential and hydrocarbon binding ability originating from the antibiotic hydrolases expressed on the cell membrane. Based on this, a nanoplasmonic sensor array was developed via the interaction of peptide-functionalized AuNPs and bacteria, thus leading to bacteria fingerprints in the SPR spectrum within 20 minutes.²⁴⁷ Furthermore, antibiotic action can affect bacterial motility, which has been quantified using plasmonic imaging and tracking microscopy at a single bacteria level and nanometer scale.²⁴⁸ These approaches highlight the versatility and potential of nanoplasmonic biosensors in rapidly assessing antibiotic susceptibility and understanding bacterial behavior at a molecular level.

7. Nanoplasmonics-based analysis of cells for environmental and human health

Cells perform specific functions that contribute to the maintenance of the system's intricate structure. Since heterogeneity among cell populations exists by differential expression at genomic, transcriptomic, proteomic, and metabonomic levels, analysis of cells from the environment and humans can reveal both ecological and human health,^249–251 respectively. Owing to the inert nature and good biocompatibility, AuNPs are excellent candidates for penetrating into the cell through the cell membrane via the endocytosis process with minimum intracellular toxicity for cell analysis.^252,253 Unlike single-cell sequencing studies that provide transient genetic information, AuNPs can provide selective information in a real-time manner. Thus, nanoplasmonics offer promising potential for dynamic environmental and human health monitoring.

Nanoplasmonic biosensors serve as an effective tool to detect circulating tumor cells (CTCs), which are shed from the primary tumor and intrastate into the peripheral blood circulation system responsible for metastasis. The direct visualization of gold nanoshell-coated CTCs under dark field illumination was developed to diagnose metastatic breast cancer from whole blood (Fig. 7a).²⁵⁴ Metastatic breast cancer cells transduced with the target molecules were incubated with antibody-coated gold nanoshells and presented overly bright at low magnification, allowing a quick screening of samples and easy visual detection of CTCs.

Fig. 7 Nanoplasmonic biosensors for extracellular and intracellular analysis for environmental and human health monitoring. (a) Detection of CTCs via the gold nanoshell coating on the cell surface and direct visualization in dark field. Reproduced with permission from ref. 254. Copyright 2018 American Chemical Society. (b) Dynamic-SERS optophysiology using plasmonic nanopipettes coated with AuNPs for detecting molecules secreted from cells. Reproduced with permission from ref. 255. Copyright 2016 American Chemical Society. (c) Reversible plasmonic dimer for detecting molecules secreted from cells. The binding of MMP3 to the DNA aptamers generates a reversible change in the interparticle distance between the plasmonic dimer. Reproduced with permission from ref. 256. Copyright 2015 American Chemical Society. (d) Plasmonic dimer for intracellular detection of mRNA splice variants. Hybridization of DNA probes to target mRNA forms a plasmonic dimer with different interparticle distances. Reproduced with permission from ref. 257. Copyright 2014 Springer Nature. (e) RNA tracking in the living cell via plasmon-quenched fluorescence of Nanoflare and Sticky-flare. Reproduced with permission from ref. 258. Copyright 2015 National Academy of Sciences, USA. (f) Nanoplasmonics probes for visualizing QBET in the electron transport chain and intracellular cytochrome c redistribution from mitochondria to the cytosol during the cellular apoptosis. Reproduced with permission from ref. 259. Copyright 2019 Springer Nature.

Except for the free-floating format, AuNPs can also be fixed on a solid substrate like borosilicate nanopipette as a dynamic nanoplasmonic biosensor to monitor metabolite secretion near living cells (Fig. 7b).²⁵⁵ This nanoplasmonic biosensor could detect metabolites in the extracellular medium by locating the nanopipettes near living cells and continuously monitoring molecule diffusion in the AuNP's hot spots, showing the potential for monitoring the intercellular signaling pathways, which usually involve the secretion and transportation of biomolecules at low concentrations. Besides, nanoplasmonic microwell arrays that comprise functionalized gold nanoholes can serve as highly sensitive EOT biosensors for the high-throughput and spatiotemporal monitoring of protein secretion from a single cell.^260–262 Biomimetic nanoplasmonic biosensors can also be designed by depositing an artificial cell membrane on the plasmonic substrate to study the protein–cell interactions.²⁶³

The plasmonic dimer is also a promising candidate for monitoring the secretion of biomolecules from cells with high sensitivity. Pairing two plasmonic nanoparticles with close proximity forms a plasmonic dimer with enhanced scattering, which highly depends on the interparticle distance. The plasmonic dimer is regarded as a plasmon ruler capable of providing quantitative information on the biomolecular binding event at nanoscale resolution. It can formed with various structures, such as AuNP dimer,²⁵⁷ in-plane gold nanodisc dimer,^134,264 gold nanotriangle dimer,²⁶⁵ and out-of-plane nanorod dimer.^266,267 The plasmonic dimer can be formed through bottom–up or top–down manufacturing approaches, including drying of droplets containing plasmonic nanoparticle suspension,²⁶⁸ electrophoretic enrichment,^269,270 electron-beam lithography,¹³⁴ and nanoimprint lithography.²⁷¹ Appropriate surface functionalization of plasmonic dimers with molecular linkers plays a key role in constructing the AuNP dimer. Target molecules trigger the cleavage, hybridization, or bending of molecular linkers to generate the interparticle distance change and the subsequent scattering spectrum shift of the plasmonic dimer. DNA/RNA is a promising candidate as the linkers to form a plasmonic dimer with unique merits for cell analysis. First, the versatile design of DNA/RNA sequences and the efficient chemical modification of each base accelerate the extensive applications in various fields. Second, the base-to-base structure of the DNA/RNA strand allows the precise adjustment of the plasmonic dimer's interparticle distance at nanometer-scale resolution.²⁷² Third, the structural reversibility of the DNA/RNA strand contributes to building the plasmonic dimer with dynamic and reconfigurable properties. The hairpin loop is another typical type of single-strand DNA or RNA structure as a molecular linker to construct a plasmonic dimer. The hairpin loop structure allows itself to switch between closed and open conformations based on the presence of competitive target molecules, leading to the change of interparticle distance and scattering spectrum shift of the plasmonic dimer.²⁷³ The aptamer also acts as a bridge connecting two plasmonic nanoparticles as the plasmonic dimer. After capturing the target analytes, the aptamer changes its conformation and shortens the dimer's interparticle distance, thus causing enhanced plasmonic coupling. A reversible DNA aptamer–AuNP dimer was used to detect single matrix metalloproteinase (MMP3) molecules secreted by the mammary epithelial cell with high specificity (Fig. 7c).²⁵⁶ The MMP3 molecules' binding to the DNA aptamer changes the AuNP dimer's interparticle distance, which is colorimetrically and spectroscopically observed using dark-field microscopy. Plasmonic dimer can be immobilized on the surface of optical fibers as cost-effective, flexible, and miniaturized nanoplasmonic biosensors applicable for remote operations.²⁷⁴

Recent advances in nanoplasmonics have led to many breakthroughs in intracellular imaging with high spatiotemporal resolution to enable simultaneous observation of cellular processes. For example, an AuNP dimer was employed for extracellularly detecting variants of mRNA splicing, which is an essential intracellular process of gene regulation (Fig. 7d).²⁵⁷ AuNPs were injected into the living cell and formed AuNP dimer in a sequence-specific manner upon targeting a single mRNA. Meanwhile, the full spectrum of each dimer was recorded by hyperspectral imaging, and the splicing information was analyzed based on the changes in the spectral profile. By measuring the AuNP dimer's hybridization dynamics, the spatiotemporal distribution of various mRNA variants of breast cancer susceptibility genes in living cells was monitored at single-copy resolution.

As one type of fluorophore-labeled spherical nucleic acid AuNP conjugates, Nanoflare consists of AuNP functionalized with the single-strand DNA recognition sequences, which are prehybridized to a short complementary DNA labeled with a fluorescence reporter (Fig. 7e).^275,276 The fluorescence was initially quenched without target sequences due to the proximity between fluorophores and AuNPs. The competitive binding of target sequences to recognition sequences led to the release of the fluorophore-labeled oligonucleotide reporters and the subsequent fluorescence recovery. The combination of Nanoflare and flow cytometry helps to detect mRNA of interest derived from breast cancer cell lines, contributing to the isolation and characterization of CTCs in the whole blood.²⁷⁷ Besides, Stickyflare was also demonstrated for quantifying RNA expression in living cells and the spatiotemporal analysis of RNA transportation.²⁵⁸ Compared with Nanoflare, Stickyflare adopted longer oligonucleotide reporters which are complementary to target sequences. Upon recognition, the recognition sequences transfer a fluorophore-conjugated reporter to the target RNA sequences, resulting in fluorescence recovery and the intracellular tracking of the fluorescence-labeled target RNA sequences.

Nanoplasmonics technology enables real-time observation of the electronic transfer dynamics of biomolecules within living cells. This technology can be used to monitor the electron transport chain of mitochondria, which is one of the most fundamental chemical reactions in cellular respiration and photosynthesis that produces ATP.^278–280 For example, AuNPs were used as optical nanoantennas to visualize the dynamics of oxidation and reduction of cytochrome c from mitochondria during cellular apoptosis (Fig. 7f).^49,259 In a research study, scientists utilized the plasmonic resonance of AuNPs and the absorption spectrum of cytochrome c to demonstrate the phenomenon of quantized selective energy transfer, known as quantum biological electron transfer (QBET).²⁵⁹ In another investigation, researchers employed AuNPs as nanoplasmonic probes to monitor the release of cytochrome c in human neuroblastoma. This study aimed to understand the role of cytochrome c in the pathogenesis of Alzheimer's disease, where it undergoes A-oligomer-induced apoptosis.²⁸¹ Nanoplasmonics-based QBET can also facilitate the selective amplification of DNA sequences during PCR,²⁸² and the remote triggering of cancer cell apoptosis.²⁸³

8. Wearable nanoplasmonic biosensors for monitoring human physiological parameters responding to environmental factors

It is extremely important to comprehend the relationship between environmental factors and various human physiological parameters. These parameters include respiration rate, phonation, body temperature, pulse/heart rate, blood pressure, electrolyte levels, motion, blood oxygen saturation, and various electrophysiological signals. Wearable devices allow for the personalized monitoring of patients at home without visiting clinical settings. It is also useful for healthy individuals who are affected by various factors such as stress, infections, or environmental factors. Monitoring the physiological network of humans, animals, plants, and organisms using wearable nanoplasmonic biosensors is crucial for the advancement of future smart cities. In this section, we will examine the recent advances in nanoplasmonic sensors for monitoring human physiological parameters by integrating nanoplasmonic structures into wearable healthcare devices. These devices provide a new opportunity for assessing human health.

Body temperature measurement is of significance in daily health monitoring. High body temperature, or fever, is a cardinal response to infections, inflammations, and antigenic reactions.²⁸⁴ Wearable colorimetric thermoresponsive sensors have the competitive advantages of being cost-effective and directly visualizing body temperature. For example, a smart colorimetric patch was developed to monitor body temperature based on raspberry-shaped plasmonic microgels encapsulated in a stretchable hydrogel film (Fig. 8a).²⁸⁵ The plasmonic microgels are composed of a thermoresponsive polymer and AuNPs. These microgels exhibit loosely packed structures (swollen state) with red color at low temperatures and densely packed assemblies (shrunk state) with grayish violet color at elevated temperatures. Flexible optical fiber embedded with the hybrids of unconversion nanoparticles and plasmonic semiconductor W₁₈O₄₉ was reported as the ratiometric sensor to monitor the body temperature.²⁸⁶

Fig. 8 Wearable nanoplasmonic biosensors for point-of-care testing of human physiological parameters. (a) Smart colorimetric patch for monitoring the body temperature based on a thermoresponsive plasmonic microgel embedded in a stretchable hydrogel film. Raspberry-shaped plasmonic microgels exhibit a reversible switch between swollen and shrunk states in response to a temperature change. Reproduced with permission from ref. 285. Copyright 2018 Springer Nature. (b) Plasmonic optical tactile sensor mounted inside a mask for respiration monitoring. The sensor comprises plasmonic PDMS optical fiber (POF) doped with AuNPs for tactile sensing. Reproduced with permission from ref. 287. Copyright 2023 Wiley. (c) and (d) Optical strain sensor comprising AuNP-PDMS fiber for monitoring the subtle wrist pulse and large motions to analyze the motor disorders. The tensile strain changes the absorption and scattering of light passing through the AuNP-PDMS fiber. Reproduced with permission from ref. 288. Copyright 2019 American Chemical Society. (e) Nanoplasmonic paper-based microfluidic sensors for sweat collection, storage, and in situ analysis of uric acid in sweat. The sensor comprises the chromatography paper uniformly absorbed with gold nanorods as SERS probes. Reproduced with permission from ref. 289. Copyright 2022 American Association for the Advancement of Science. (f) Plasmonic microneedle patches for monitoring biomarkers in dermal interstitial fluid.

Respiration rate is another fundamental indicator of health status.²⁹⁰ In addition to the respiration rate, abnormal respiration sounds like wheezes are also relevant to some diseases, such as obstructive pulmonary diseases.²⁹¹ Nanoplasmonic optical fiber sensors are an attractive platform for monitoring respiration with unique properties, including lightweight, small size, and flexibility. A plasmonic optical fiber was mounted as a flexible optical tactile sensor inside a wearable mask to monitor respiration simultaneously (Fig. 8b).²⁸⁷ The optical tactile sensor can record different breath states, such as inhalation and exhalation, by converting the mechanical force into interpretable light signals via the LSPR effect of AuNP-doped PDMS optical fiber.

The pulse or heart rate is also an essential indicator in determining individual health, especially cardiovascular health.²⁹² The pulse can be measured using the wrist's radial artery or the neck's carotid artery. A wearable optical strain sensor was created by attaching AuNPs in a stretchable optical fiber to record the wrist pulse (Fig. 8c).²⁸⁸ The sensor can record the periodic pulse waveform, which usually comprises three peaks (P1, P2, and P3) to indicate health status. The nanoplasmonic sensor array also has the potential capability as a multichannel wrist pulse monitoring platform to provide spatiotemporal information of pulse at three wrist pulse positions (Chi, Cun, and Guan), which is an indispensable component of traditional Chinese medicine.^293,294

Apart from measuring the subtle wrist pulse, monitoring the large motions is also of significance in quantitatively analyzing motor disorders like Parkinson's disease. The primary symptoms of Parkinson's disease are bradykinesia (slowness of movements), hand tremor, and muscle rigidity.²⁹⁵ Stretchable plasmonic optical fiber sensors can quantitatively assess motor disorders based on a rapid finger-tapping assessment, which can assist doctors in improving diagnostic accuracy and adjusting medications (Fig. 8d).²⁸⁸

Sweat contains various chemicals, including electrolytes, metabolites, drugs, and hormones, which provide insights into the physiological and pathological status.^296,297 For instance, the quantification of sweat glucose has been extensively explored for non-invasive diabetes management.^298,299 Wearable sweat sensors can provide a non-invasive approach to human health monitoring. Plasmonic paper-based microfluidic systems possess the ability to collect, transport, store, and monitor sweat. These systems can also simultaneously analyze sweat metabolic as a potential biomarker of various diseases such as gout,³⁰⁰ type 2 diabetes,³⁰¹ and kidney diseases.³⁰² For example, serpentine-shaped cellulose chromatography paper functionalized with uniformly AuNRs was employed to collect the excreted sweat by wicking and to monitor the concentration of sweat uric acid using a portable Raman spectroscopy (Fig. 8e).^289,303 Wearable Janus fabric, which features a superhydrophobic side in contact with the skin and patterned superhydrophilic zones on the opposite surface, can facilitate sweat collection by generating the unidirectional flow of sweat toward superhydrophilic zones.³⁰⁴ Other plasmonic platforms, including TiVC MXene-AuNP membranes,³⁰⁵ silver nanomushroom arrays,³⁰⁶ gold nanosphere cone arrays,³⁰⁷ nanoplasmonic grapefruit optical fibers,³⁰⁴ and silver nanocube superlattice,^307,308 are also employed as the SERS probe for monitoring the metabolic, such as methotrexate, urea, lactate, pH, acetaminophen, and nicotine in sweat. To maintain the stability of the SERS signal of the wearable sweat sensor under a wide range of excitation angles caused by body movements, a bioinspired omnidirectional plasmonic nanovoid array was fabricated as the SERS substrate by integrating metal nanoparticles with an artificial plasmonic compoundeye, which contains a microscale semisphere retina decorated with a “pockets” array in a honeycomb pattern.³⁰⁹

Dermal interstitial fluid holds promise as a potentially rich and accessible biofluid containing biomarkers for monitoring human health without the need for blood drawing.³¹⁰ Microneedle-based skin patches serve as wearable biosensors capable of minimally penetrating the skin to monitor biomarkers in dermal interstitial fluid, holding potential for healthcare applications.^311–313 Plasmonic microneedle patches are typically fabricated by coating a nanoplasmonic layer on the microneedle layers made from flexible materials like polystyrene,³¹⁴ PMMA,³¹⁵ PLGA,^316,317 and PEGDA hydrogel.³¹⁸ These patches enable monitoring of human physiological parameters, such as blood glucose,³¹⁵ pH,^317,319 uric acid,³²⁰ and inflammatory biomarkers (Fig. 8f).³¹⁴ Detection results can be obtained via ex situ or in situ approaches, such as SERS,^315–317 or fluorescent signal.^313,318 In ex situ tests, plasmonic microneedle patches are peeled off from the skin for downstream signal readout;³¹⁶ while in situ tests record signals directly from the sensing area, which can be either plasmonic microneedles,³¹⁷ or a separate detection region.³²⁰ Specific designs like hollow microneedle,³¹⁸ microfluidic channel,³²⁰ and plasmonic fluor,³¹⁴ in plasmonic microneedle patches facilitate interstitial fluid collection by capillarity and enhance signal amplification.

9. Challenges and opportunities

There are a few essential things to consider when advancing nanoplasmonic biosensors. These include aspects of engineering and administrative collaboration. By integrating advanced techniques with nanoplasmonic biosensors, we can fully exploit their potential for practical applications in environmental and human health monitoring (Fig. 9).

Fig. 9 Convergence approaches to develop highly efficient biosensors for safeguarding environmental sustainability and human health. With the use of convergence approaches, we can create innovative nanoplasmonic biosensors that can detect and monitor various environmental and medical factors accurately. These nanoplasmonic biosensors have the potential to revolutionize the way to track and manage our environment and human health.

When designing nanoplasmonic biosensors, a primary engineering concern is the cost-effective manufacturing of these sensors. Traditional manufacturing technologies such as complementary metal–oxide–semiconductor (CMOS) can be costly when used to create prototypes of nanostructured devices in academic settings. However, this method becomes more cost-effective when producing large-scale disposable nanoplasmonic biosensors. The use of disposable sensors can prevent cross-contamination and reduce labor-intensive regeneration procedures. Lowering the cost of nanoplasmonic sensors can also promote scaling up by factories and wide application by end-users across various settings. Advanced nanofabrication technologies, such as nanoimprinting,²¹⁸ laser interference lithography patterning,²³¹ metal-assisted chemical etching,^232–234 self-assembly of nanoparticle arrays,^321,322 and wafer-scale nanosphere lithography,^150,323 offer opportunities to manufacture large-scale nanoplasmonic structures with high-quality and customizable morphologies at a reduced cost. Recent developments in CMOS IC electronics manufacturing technology have led to significant cost reduction and miniaturization of CMOS cameras and optoelectronic devices, such as photodetectors and Raman spectrometers.²⁸⁹ These devices can be utilized for on-site environmental monitoring.^324,325 Furthermore, machine learning techniques have enabled low-cost sensor optimization through iterative computation-enabled design processes.³²⁶

Developing innovative surface chemistry for nanoplasmonic structures can enhance the binding of specific analytes and minimize the binding of interfering substances.^327–329 This can significantly improve the sensitivity and specificity of nanoplasmonic biosensors, especially in complex matrices. Nanoplasmonic biosensors rely on molecular interactions between biorecognition elements immobilized on their surfaces and analytes.^29,330 Gold or silver-based nanoplasmonic sensors typically use thiol chemistry to form a self-assembled monolayer on the surface for immobilizing biorecognition elements. To immobilize proteins, peptides, small molecules, and nucleotides on nanoplasmonic structures, a diverse range of thiols like thiolated polyethylene glycol (PEG) and other self-assembled monolayers (SAM) with varying lengths and terminal groups can be used.^331,332 These thiols enable the utilization of different conjugation methods.³³³ Alternatively, depositing a thin layer of silica on the nanoplasmonic structure also enables the well-established silane chemistry to immobilize the biorecognition elements.³³⁴ Supported lipid bilayers can also be formed on the surface of nanoplasmonic biosensors, serving as analytical tools for investigating the cellular membrane,³³⁵ and detecting the biochemical.³³⁶

Integrating sample handling units with nanoplasmonic biosensors can help make sample preparation steps more efficient, thereby reducing the turnaround time.^337,338 Micro/nanofluidic systems can be integrated to minimize manual handling of sample preparation, which includes actuation, pre-concentration, and purification of trace amounts of analytes from complex environmental samples.^204,205 For example, self-powered micro/nanofluidic systems, such as capillary microfluidics,^306,339 and vacuum battery,³⁴⁰ offer a practical solution for passive fluid flow without requiring auxiliary electrical equipment for sample preparations. These integrated systems also have the potential to vastly enhance throughput capabilities by allowing for the simultaneous measurement of multiple targets in a single test, thus meeting the demands of fluidic sample-based POC diagnostics.

The fourth aspect to take into account is how to overcome the mass transfer limitation to improve the detection limit of nanoplasmonic biosensors.^341,342 When analytes move towards the hotspots on the surface of the nanoplasmonic biosensor, a depletion zone is formed where the flow velocity approaches zero at the reaction surface.^192,343 Because of zero flow in the depletion zone, analyte transfer from the bulk solution to the reaction surface mainly occurs through diffusion, which is typically time-consuming. Consequently, only a small fraction of total analytes can bind to the reaction surface to generate signals, while most analytes remain in the bulk solution without contributing to the signal response. This mass transfer limitation fundamentally hinders the improvement of the detection limit of surface-based nanoplasmonic biosensors, especially at low concentrations of analytes. Thermoresponsive polymer coated with a nanoplasmonic layer can be used as the photothermomechanical nanopump for the active transport of target analytes to the sensor surface via the periodic heating-induced repetitive water release and influx through the thermoresponsive polymer.³⁴⁴ Researchers have implemented more innovative strategies, including active stirring via alternating current electroosmosis,³⁴⁵ and active actuation of analytes toward the surface via flow-through micro/nanofluidic channels or nanoholes,³⁴⁶ to overcome the mass transfer limitation and achieve improved sensitivity.

When applying nanoplasmonic biosensors, improving their temporal resolution and spatial coverage expansion is the first consideration for administrative collaboration in environmental monitoring.^347,348 Insufficient monitoring frequency may overlook rapid, transient changes that could lead to environmental and human health crises. Moreover, environmental monitoring often remains limited in scope, focusing on smaller areas while leaving large geographic regions unsupervised. This disparity can create significant gaps in environmental data collection, resulting in a fragmented understanding of environmental conditions across different regions. The advancement of the Internet of Things (IoT) presents an opportunity to address this challenge by integrating numerous nanoplasmonic biosensors into wireless sensor networks for real-time and remote environmental and human health monitoring. This approach enables the real-time and remote gathering of extensive data, significantly enhancing the breadth and efficiency of environmental surveillance efforts. Furthermore, artificial intelligence (AI) holds the potential for rapidly and accurately analyzing vast datasets, thereby improving the reliability of monitoring systems and enhancing decision-making processes related to environmental and human health concerns at their inception.³⁴⁹ AI also facilitates the exploration of correlations between environmental monitoring data and human health through sophisticated data analytics and machine learning algorithms.³⁵⁰ Additionally, AI enables effective design and optimization of nanoplasmonic biosensors via capturing relationships between synthesis/structure and performance in a way that far exceeds conventional simulation and theory approaches.³⁵¹

Natural organisms from the environment provide a wealth of delicate nanostructures that inspire the design of bioinspired nanoplasmonic biosensors, leveraging millions of years of evolutionary optimization. Inspired by moth and Xenos Peckii eye's nanostructures, researchers developed omnidirectional nanoplasmonic biosensors that exhibit angular independence in reflection and SERS spectra,³⁵² ensuring sensor stability under varying incident angles due to environmental conditions. Diatoms, unicellular marine microalgae encased in hierarchical silica micro/nanoporous frustules, feature periodic nanopore structures that are promising SERS substrates to design sustainable nanoplasmonic biosensors capable of enriching and detecting target analytes.^353–356 Colorful butterfly wings have unique periodic nanostructures, such as concavity array,³⁵⁷ chitinous nano-structured conical arrays,³⁵⁸ interlaced vertical nanoplates,³⁵⁹ ridges with lamella structures,³⁶⁰ that can serve as bio-scaffolds for coating with plasmonic materials to design nanoplasmonic biosensors.

An important aspect to consider is the establishment of environmental sample banks and the promotion of global data sharing across various fields of expertise and regions. This initiative seeks to develop new and innovative solutions that tackle the underlying causes of environmental problems, which is a top priority for the global community. Collaboration among government agencies, researchers, and communities is essential for addressing environmental and human health challenges comprehensively. For example, studying the EVs of plants, pathogens, microbes, animals, and humans can offer a new approach to understanding environmental and human health stress, promoting a sustainable society.

10. Conclusion and outlook

It is crucial for both environmental and human health to ensure access to clean water and air, safe food and drink, proper waste disposal, and healthy workplaces, agricultural practices, cities, and natural environments. This review examines the recent developments in nanoplasmonic biosensors as a useful tool for monitoring environmental and human health based on target analytes with different size scales. It includes the detection of biomolecules, EVs, pathogens, and cells from the environment, as well as the examination of human health and environmental sustainability, as summarized in Table 2. Most nanoplasmonic biosensors show exceptional sensitivity compared to electrochemical/electrical or fluorescent biosensors. Another advantage of nanoplasmonic biosensors is their sustainability. AuNPs exhibit inert properties that do not pollute the environment or adversely impact human health. Although noble metal can be expensive, the amount used in each nanoplasmonic biosensor in the form of nanoparticles is typically minimal, thus contributing to low-cost fabrication.

Table 2 Summary of nanoplasmonics biosensors for environmental sustainability and human health

Type	Analytes	Sample source	Detection method	Nanostructure	Detection limit/sensitivity	Ref.
Molecules	Cu^2+	Buffer	PRET	AuNPs	1 nM	67
	VOCs	Plant	Coupled LSPR	AuNRs	0.4 ppm	69
	Antibiotics	Milk	Coupled LSPR	AuNPs	0.74 ppb	70
	BPA	Tap water	Circular dichroism	Asymmetric AuNP dimer	0.008 ng mL^−1	71
	Parathion-methyl, thiram, chlorpyrifos	Fruit peel surface	SERS	AuNPs	2.60 ng cm^−2, 0.24 ng cm^−2, 3.51 ng cm^−2	72
	Cholera toxin	Lake water	Coupled LSPR	AuNPs	10 nM	73
	Prostate-specific antigen	Serum	Coupled LSPR	AuNPs	1 × 10^−18 g ml^−1	129
	Interleukin-6	Buffer	Plasmon-enhanced fluorescence	AuNRs	1 fM	130
	Type 1 diabetes autoantibodies	Serum	Plasmon-enhanced fluorescence	Gold nanoislands	100%	133
	Alkaline phosphatase	Tris–HCl buffer	CuAAC-mediated plasmonic ELIS	AuNPs	0.2 U L^−1	138
	Protease trypsin	Buffer	Coupled LSPR	Core–satellite nanostructures	0.25%	139
	Hepatitis B virus surface antigen	Buffer	Interferometric reflectance	AuNRs	3.2 pg mL^−1	140
	Amyloid-β	DMSO/water	Scattering in dark field	AuNPs	5 μM	141
	Glucose	0.1 M NaOH	Catalytic activator	AuNPs	9 μM	142
	VOCs	Exhaled breath	SERS	AuNPs on RGO	>83%	143
EVs	CD63, CD9	Plasma	Coupled LSPR	AuNPs	0.2 ng μL^−1	185
	CD63, CD24	Serum	Plasmon-quenched fluorescence	AuNPs	1500 particles	187
	CD63	MCF-7 cells	Coupled LSPR	AuNRs	160 particles per μL	188
	CD24, EpCAM	Ascites fluid	EOT	Gold nanohole	3000 particles	189
	CD63, CD9	MCF-7 cells	LSPRi imaging	Elliptically-shaped nanoplasmonic sensors	1 particle	190
	CD63	Buffer	Interferometric plasmonic microscopy	Gold nanofilm	1 particle	23
Pathogens	SARS-CoV-2	Environmental aerosol	Photothermal conversion	Gold nanoislands	0.25 copies per μL	215
	MERS-CoV	Buffer	Photothermal conversion	Au nanopillar array	0.1 ng μL^−1	24
	Ebola virus	Human plasma	Plasmon-enhanced fluorescence	3D gold nanoantenna	220 fg mL^−1	218
	E. coli	Artificial urine	Photothermal conversion	Au nanoporous membrane	10^3 CFU mL^−1	236
	E. coli	Cultured cell	SERS	AuNP on a mirror	10^2 cells per ml	26
	E. coli	Cultured cell	Coupled LSPR	AuNPs	Single-digit particles	219
	Azoreductase	E. coli	PRET	AuNRs	5 nM	220
Cells	CTC	Blood	Rayleigh scattering in dark field	Gold nanoshells	—	254
	Pyruvate/lactate/ATP/urea	Cultured epithelial cell	SERS	AuNP-coated nanopipette	Single molecule	255
	MMP3	Cultured epithelial cell	Coupled LSPR	AuNP dimer	Single molecule	256
	mRNA	Breast cancer cell	Coupled LSPR	AuNP dimer	Single copy	257
	β-Actin mRNA	HeLa cells	Plasmon-quenched fluorescence	AuNPs	—	258
	Cytochrome c	HeLa cells	PRET	AuNPs	—	259
Human physiological parameters	Body temperature	—	Coupled LSPR	Raspberry-shaped plasmonic microgels	0.2 °C	285
	Respiration	—	Coupled LSPR	AuNP-doped PDMS optical fiber	—	287
	Pulse/heart rate	—	Coupled LSPR	AuNP-PDMS fiber	—	288
	Motion	—	Coupled LSPR	AuNP-PDMS fiber	—	288
	Uric acid	Sweat	SERS	AuNRs	1 μM	289
	Glucose	Interstitial fluid	SERS	Plasmonic microneedle patch	0–20 mM	315

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While numerous papers have been published on novel nanoplasmonic biosensors, gaps still need to be overcome for commercialization, which could bring substantial benefits to both the environment and human health. A successful sensor should meet several criteria: simplicity, affordability, sensitivity, specificity, speed, and robustness. Among these, simplicity is the critical factor that determines whether end-users widely adopt the nanoplasmonic biosensors, as Leonardo Da Vinci's teaching, “simplicity is the ultimate sophistication.” As one of the most successfully FDA/CE-approved nanoplasmonic-based diagnostic concepts, the lateral flow assay incorporating AuNPs as the readout labels has been widely adopted for monitoring the environment and human health. Even with compromised sensitivity, the lateral flow assay offers exceptional simplicity and functionality, making it a crucial tool for sustainable global healthcare. Nanoplasmonics is one of the most exciting research areas in modern nanophotonics. It will continue to evolve to design simpler, faster, smaller, and cheaper nanoplasmonic biosensors. These next-generation sensors will have higher sensitivity and specificity, making them practical solutions for monitoring environmental and human health and supporting sustainable global healthcare. The goal is to better understand how environmental factors can trigger human diseases, such as autoimmune, neurodegenerative, and infectious diseases, and to prevent these diseases effectively. We anticipate that advancements in nanoplasmonics will continue to drive sustainable global healthcare for humans, the environment, and the planet.

Author contributions

All authors read and approved the final version of the manuscript. L. P. L. conceived the study and provided guidance. W. L. and S. Y. prepared the figures and wrote the manuscript with assistance from K. C. and L. P. L. All authors confirm that they have full access to all study data and accept responsibility for publication submission.

Data availability

No primary research results, software, or code have been included, and no new data were generated or analyzed as part of this review.

Conflicts of interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgements

This work was supported by the National Institutes of Health (NIH) award (R01DK133864) and the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT, NRF-2021R1C1C2003417) (K. C.).

References

1. A. Pruss-Ustun, C. F. Corvalán and W. H. Organization, Preventing disease through healthy environments: towards an estimate of the environmental burden of disease, World Health Organization, 2006.
2. P. O. Ukaogo, U. Ewuzie and C. V. Onwuka, Microorganisms for sustainable environment and health, Elsevier, 2020, pp. 419–429.
3. L. Fajersztajn, M. Veras, L. V. Barrozo and P. Saldiva, Nat. Rev. Cancer, 2013, 13, 674–678.
4. M. Bergmann, F. Collard, J. Fabres, G. W. Gabrielsen, J. F. Provencher, C. M. Rochman, E. van Sebille and M. B. Tekman, Nat. Rev. Earth Environ., 2022, 3, 323–337.
5. R. Fuller, P. J. Landrigan, K. Balakrishnan, G. Bathan, S. Bose-O'Reilly, M. Brauer, J. Caravanos, T. Chiles, A. Cohen and L. Corra, Lancet. Planetary Health, 2022, 6, e535–e547.
6. B. Toussaint, B. Raffael, A. Angers-Loustau, D. Gilliland, V. Kestens, M. Petrillo, I. M. Rio-Echevarria and G. Van den Eede, Food Addit. Contam., Part A, 2019, 36, 639–673.
7. W. H. Organization, WHO global air quality guidelines: particulate matter (PM2. 5 and PM10), ozone, nitrogen dioxide, sulfur dioxide and carbon monoxide, World Health Organization, 2021.
8. M. O. Raimi, Z. Adio, O. O. Emmanuel, T. K. Samson, B. S. Ajayi, T. J. Ogunleye, R. Morufu Olalekan, A. Zulkarnaini Olalekan, O. Oluwaseun Emmanuel, T. Kayode Samson, A. Bankole Sunday and O. Temitope Jide, Impact of Sawmill Industry on Ambient Air Quality: A Case Study of Ilorin Metropolis, Kwara State, Nigeria, Energy Earth Sci., 2020, 3, p1.
9. J. E. Soverow, G. A. Wellenius, D. N. Fisman and M. A. Mittleman, Environ. Health Perspect., 2009, 117, 1049–1052.
10. S. Cheval, C. Mihai Adamescu, T. Georgiadis, M. Herrnegger, A. Piticar and D. R. Legates, Int. J. Environ. Res. Public Health, 2020, 17, 4140.
11. Y. Lu, N. Nakicenovic, M. Visbeck and A.-S. Stevance, Nature, 2015, 520, 432–433.
12. B. E. Obinaju and F. L. Martin, Environ. Pollut., 2013, 183, 46–53.
13. B. Chaix, Annu. Rev. Public Health, 2018, 39, 367–384.
14. J. Lee, S. Mahendra and P. J. Alvarez, ACS Nano, 2010, 4, 3580–3590.
15. C. K. Ho, A. Robinson, D. R. Miller and M. J. Davis, Sensors, 2005, 5, 4–37.
16. H. Jans and Q. Huo, Chem. Soc. Rev., 2012, 41, 2849–2866.
17. C. Hanske, E. H. Hill, D. Vila-Liarte, G. González-Rubio, C. Matricardi, A. Mihi and L. M. Liz-Marzán, ACS Appl. Mater. Interfaces, 2019, 11, 11763–11771.
18. V. T. N. Linh, H. Kim, M. Y. Lee, J. Mun, Y. Kim, B. H. Jeong, S. G. Park, D. H. Kim, J. Rho and H. S. Jung, Biosens. Bioelectron., 2024, 244, 115779.
19. B. J. Wiley, S. H. Im, Z.-Y. Li, J. McLellan, A. Siekkinen and Y. Xia, J. Phys. Chem. B, 2006, 110, 15666–15675.
20. K. Sun, Q. Huang, G. Meng and Y. Lu, ACS Appl. Mater. Interfaces, 2016, 8, 5723–5728.
21. L. Zhou, Y. Tan, J. Wang, W. Xu, Y. Yuan, W. Cai, S. Zhu and J. Zhu, Nat. Photonics, 2016, 10, 393.
22. F. Jiao, F. Li, J. Shen, C. Guan, S. A. Khan, J. Wang, Z. Yang and J. Zhu, Sens. Actuators, B, 2021, 344, 130170.
23. Y. Yang, G. Shen, H. Wang, H. Li, T. Zhang, N. Tao, X. Ding and H. Yu, Proc. Natl. Acad. Sci. U. S. A., 2018, 115, 10275–10280.
24. Y. Lee, B.-H. Kang, M. Kang, D. R. Chung, G.-S. Yi, L. P. Lee and K.-H. Jeong, ACS Appl. Mater. Interfaces, 2020, 12, 12533–12540.
25. S. Pan, Y. Zhang, A. Natalia, C. Z. Lim, N. R. Ho, B. Chowbay, T. P. Loh, J. K. Tam and H. Shao, Nat. Nanotechnol., 2021, 16, 734–742.
26. K. Whang, J.-H. Lee, Y. Shin, W. Lee, Y. Kim, D. Kim, L. P. Lee and T. Kang, Light: Sci. Appl., 2018, 7, 68.
27. H. Xin, B. Namgung and L. P. Lee, Nat. Rev. Mater., 2018, 3, 228–243.
28. A. Minopoli, A. Acunzo, B. Della Ventura and R. Velotta, Adv. Mater. Interfaces, 2022, 9, 2101133.
29. K. M. Mayer and J. H. Hafner, Chem. Rev., 2011, 111, 3828–3857.
30. J. Homola, Chem. Rev., 2008, 108, 462–493.
31. L. M. Liz-Marzán, C. J. Murphy and J. Wang, Chem. Soc. Rev., 2014, 43, 3820–3822.
32. H. Altug, S.-H. Oh, S. A. Maier and J. Homola, Nat. Nanotechnol., 2022, 17, 5–16.
33. P. M. Tiwari, K. Vig, V. A. Dennis and S. R. Singh, Nanomaterials, 2011, 1, 31–63.
34. Z. Jin, W. Yim, M. Retout, E. Housel, W. Zhong, J. Zhou, M. S. Strano and J. V. Jokerst, Chem. Soc. Rev., 2024, 7681–7741.
35. H. Singh, A. Bamrah, S. K. Bhardwaj, A. Deep, M. Khatri, R. J. Brown, N. Bhardwaj and K.-H. Kim, Environ. Sci.: Nano, 2021, 8, 863–889.
36. J. Langer, D. J. de Aberasturi, J. Aizpurua, R. A. Alvarez-Puebla, B. Auguie, J. J. Baumberg, G. C. Bazan, S. E. J. Bell, A. Boisen, A. G. Brolo, J. Choo, D. Cialla-May, V. Deckert, L. Fabris, K. Faulds, F. J. G. de Abajo, R. Goodacre, D. Graham, A. J. Haes, C. L. Haynes, C. Huck, T. Itoh, M. Ka, J. Kneipp, N. A. Kotov, H. Kuang, E. C. Le Ru, H. K. Lee, J.-F. Li, X. Y. Ling, S. A. Maier, T. Mayerhofer, M. Moskovits, K. Murakoshi, J.-M. Nam, S. Nie, Y. Ozaki, I. Pastoriza-Santos, J. Perez-Juste, J. Popp, A. Pucci, S. Reich, B. Ren, G. C. Schatz, T. Shegai, S. Schlucker, L.-L. Tay, K. G. Thomas, Z.-Q. Tian, R. P. Van Duyne, T. Vo-Dinh, Y. Wang, K. A. Willets, C. Xu, H. Xu, Y. Xu, Y. S. Yamamoto, B. Zhao and L. M. Liz-Marzan, ACS Nano, 2020, 14, 28–117.
37. X. X. Han, R. S. Rodriguez, C. L. Haynes, Y. Ozaki and B. Zhao, Nat. Rev. Dis. Primers, 2021, 1, 87.
38. Y. Hang, J. Boryczka and N. Wu, Chem. Soc. Rev., 2022, 51, 329–375.
39. H. Liu and P. Lalanne, Nature, 2008, 452, 728–731.
40. L. P. Hackett, A. Ameen, W. Li, F. K. Dar, L. L. Goddard and G. L. Liu, ACS Sens., 2018, 3, 290–298.
41. Z. Chen, P. Li, S. Zhang, Y. Chen, P. Liu and H. Duan, Nanotechnology, 2019, 30, 335201.
42. M. L. Brongersma, N. J. Halas and P. Nordlander, Nat. Nanotechnol., 2015, 10, 25–34.
43. X. Cui, Q. Ruan, X. Zhuo, X. Xia, J. Hu, R. Fu, Y. Li, J. Wang and H. Xu, Chem. Rev., 2023, 123, 6891–6952.
44. M. Kim, J. H. Lee and J. M. Nam, Adv. Sci., 2019, 6, 1900471.
45. Q. Su, C. Jiang, D. Gou and Y. Long, ACS Appl. Bio Mater., 2021, 4, 4684–4705.
46. D. Semeniak, D. F. Cruz, A. Chilkoti and M. H. Mikkelsen, Adv. Mater., 2023, 35, 2107986.
47. J.-F. Li, C.-Y. Li and R. F. Aroca, Chem. Soc. Rev., 2017, 46, 3962–3979.
48. G. L. Liu, Y.-T. Long, Y. Choi, T. Kang and L. P. Lee, Nat. Methods, 2007, 4, 1015–1017.
49. Y. Choi, T. Kang and L. P. Lee, Nano Lett., 2008, 9, 85–90.
50. S. F. Tan, L. Wu, J. K. W. Yang, P. Bai, M. Bosman and C. A. Nijhuis, Science, 2014, 343, 1496–1499.
51. J. J. Baumberg, J. Aizpurua, M. H. Mikkelsen and D. R. Smith, Nat. Mater., 2019, 18, 668–678.
52. O. S. Ojambati, R. Chikkaraddy, W. D. Deacon, M. Horton, D. Kos, V. A. Turek, U. F. Keyser and J. J. Baumberg, Nat. Commun., 2019, 10, 1049.
53. R. Chikkaraddy, B. De Nijs, F. Benz, S. J. Barrow, O. A. Scherman, E. Rosta, A. Demetriadou, P. Fox, O. Hess and J. J. Baumberg, Nature, 2016, 535, 127–130.
54. E. Kokin, H. J. An, D. Koo, S. Han, K. Whang, T. Kang, I. Choi and L. P. Lee, Nano Lett., 2022, 22, 9861–9868.
55. B. A. Pandian, R. Sathishraj, M. Djanaguiraman, P. V. Prasad and M. Jugulam, Antioxidants, 2020, 9, 454.
56. J. Zhao, S. Xue, R. Ji, B. Li and J. Li, Chem. Soc. Rev., 2021, 50, 12070–12097.
57. C. Zhan, X.-J. Chen, J. Yi, J.-F. Li, D.-Y. Wu and Z.-Q. Tian, Nat. Rev. Chem., 2018, 1.
58. I. Choi and L. P. Lee, ACS Nano, 2013, 7, 6268–6277.
59. Y. Jin, S. Wu, Z. Zeng and Z. Fu, Environ. Pollut., 2017, 222, 1–9.
60. O. M. Alharbi, R. A. Khattab and I. Ali, J. Mol. Liq., 2018, 263, 442–453.
61. W. Zhou, X. Gao, D. Liu and X. Chen, Chem. Rev., 2015, 115, 10575–10636.
62. B. Bansod, T. Kumar, R. Thakur, S. Rana and I. Singh, Biosens. Bioelectron., 2017, 94, 443–455.
63. M. Jaishankar, T. Tseten, N. Anbalagan, B. B. Mathew and K. N. Beeregowda, Interdiscip. Toxicol., 2014, 7, 60.
64. M. L. Budlayan, J. Dalagan, J. P. Lagare-Oracion, J. Patricio, S. Arco, F. Latayada, T. Vales, B. Baje, A. Alguno and R. Capangpangan, Environ. Nanotechnol., Monit. Manage., 2022, 18, 100736.
65. G. Ramesh and T. Radhakrishnan, ACS Appl. Mater. Interfaces, 2011, 3, 988–994.
66. H. S. Chandwadkar, S. Patra, N. G. Gaidhani, D. Sen and C. Majumder, Colloids Surf., A, 2020, 602, 125140.
67. Y. Choi, Y. Park, T. Kang and L. P. Lee, Nat. Nanotechnol., 2009, 4, 742–746.
68. J. Du, B. Zhu and X. Chen, Small, 2013, 9, 4104–4111.
69. Z. Li, R. Paul, T. Ba Tis, A. C. Saville, J. C. Hansel, T. Yu, J. B. Ristaino and Q. Wei, Nat. Plants, 2019, 5, 856–866.
70. P. P. Chaudhari, L.-K. Chau, Y.-T. Tseng, C.-J. Huang and Y.-L. Chen, Microchim. Acta, 2020, 187, 1–11.
71. H. Kuang, H. Yin, L. Liu, L. Xu, W. Ma and C. Xu, ACS Appl. Mater. Interfaces, 2014, 6, 364–369.
72. J. Chen, Y. Huang, P. Kannan, L. Zhang, Z. Lin, J. Zhang, T. Chen and L. Guo, Anal. Chem., 2016, 88, 2149–2155.
73. S. A. Khan, J. A. DeGrasse, B. J. Yakes and T. R. Croley, Anal. Chim. Acta, 2015, 892, 167–174.
74. B. Huang, C. Lei, C. Wei and G. Zeng, Environ. Int., 2014, 71, 118–138.
75. R. Borah, J. Smets, R. Ninakanti, M. L. Tietze, R. Ameloot, D. N. Chigrin, S. Bals, S. Lenaerts and S. W. Verbruggen, ACS Appl. Nano Mater., 2022, 5, 11494–11505.
76. K. Yang, C. Zhang, K. Zhu, Z. Qian, Z. Yang, L. Wu, S. Zong, Y. Cui and Z. Wang, ACS Nano, 2022, 16, 19335–19345.
77. M. L. Tietze, M. Obst, G. Arnauts, N. Wauteraerts, S. Rodriguez-Hermida and R. Ameloot, ACS Appl. Nano Mater., 2022, 5, 5006–5016.
78. C. S. L. Koh, H. K. Lee, X. Han, H. Y. F. Sim and X. Y. Ling, Chem. Commun., 2018, 54, 2546–2549.
79. Y. Y. Zhao, B. Q. Dong, K. D. Benkstein, L. Chen, K. L. Steffens and S. Semancik, ACS Appl. Mater. Interfaces, 2022, 14, 54411–54422.
80. W. Wei, J. Nong, G. Zhang, L. Tang, X. Jiang, N. Chen, S. Luo, G. Lan and Y. Zhu, Sensors, 2016, 17, 2.
81. K. H. Kim, S. Jo, S. E. Seo, J. Kim, D. S. Lee, S. Joo, J. Lee, H. S. Song, H. G. Lee and O. S. Kwon, ACS Sens., 2023, 8, 2169–2178.
82. T. Karakouz, A. Vaskevich and I. Rubinstein, J. Phys. Chem. B, 2008, 112, 14530–14538.
83. I. T. Baldwin, R. Halitschke, A. Paschold, C. C. Von Dahl and C. A. Preston, Science, 2006, 311, 812–815.
84. V. Bitas, H.-S. Kim, J. W. Bennett and S. Kang, Mol. Plant-Microbe Interact., 2013, 26, 835–843.
85. L. Shang, C. Liu, B. Chen and K. Hayashi, Sens. Actuators, B, 2018, 260, 617–626.
86. L. Shang, C. Liu, B. Chen and K. Hayashi, ACS Sens., 2018, 3, 1531–1538.
87. G. Lee, O. Hossain, S. Jamalzadegan, Y. Liu, H. Wang, A. C. Saville, T. Shymanovich, R. Paul, D. Rotenberg and A. E. Whitfield, Sci. Adv., 2023, 9, eade2232.
88. Y. Guo, Nat. Food, 2023, 4, 350.
89. P. S. Jorgensen, A. Aktipis, Z. Brown, Y. Carrière, S. Downes, R. R. Dunn, G. Epstein, G. B. Frisvold, D. Hawthorne, Y. T. Gröhn, G. T. Gujar, D. Jasovsky, E. Y. Klein, F. Klein, G. Lhermie, D. Mota-Sanchez, C. Omoto, M. Schlüter, H. M. Scott, D. Wernli, S. P. Carroll and P. Living Resistance, Nat. Sustain., 2018, 1, 632–641.
90. C. J. Murray, K. S. Ikuta, F. Sharara, L. Swetschinski, G. R. Aguilar, A. Gray, C. Han, C. Bisignano, P. Rao and E. Wool, The Lancet, 2022, 399, 629–655.
91. S. A. Kraemer, A. Ramachandran and G. G. Perron, Microorganisms, 2019, 7, 180.
92. F. Baquero, J.-L. Martínez and R. Cantón, Curr. Opin. Biotechnol, 2008, 19, 260–265.
93. T. U. Berendonk, C. M. Manaia, C. Merlin, D. Fatta-Kassinos, E. Cytryn, F. Walsh, H. Bürgmann, H. Sørum, M. Norström and M.-N. Pons, Nat. Rev. Microbiol., 2015, 13, 310–317.
94. D. J. Larsson and C.-F. Flach, Nat. Rev. Microbiol., 2022, 20, 257–269.
95. Q. C. Yang, Y. Y. Hao, Z. F. Chen, N. Lan, X. He, D. M. Hu, Z. Y. Xu, L. L. Liang, D. L. Cao, J. Guo, Y. Ran and B. O. Guan, Sens. Actuators, B, 2023, 393, 134248.
96. R. L. Wang, H. Z. Zhang, Q. Y. Liu, F. Liu, X. L. Han, X. Q. Liu, K. W. Li, G. Z. Xiao, J. Albert, X. H. Lu and T. Guo, Nat. Commun., 2022, 13, 547.
97. C. Y. Shen, Z. L. Huang, X. M. Chen, Z. H. Wang, J. Zhou, Z. K. Wang, D. J. Liu, C. X. Li, T. Q. Zhao, Y. Zhang, S. Q. Xu, W. J. Zhou and W. Peng, Biosens. Bioelectron., 2023, 242, 115719.
98. L. J. Zu, X. C. Wang, P. Liu, J. W. Xie, X. J. Zhang, W. R. Liu, Z. C. Li, S. Q. Zhang, K. W. Li, A. Giannetti, W. Bi, F. Chiavaioli, L. Shi and T. Guo, Adv. Sci., 2024, 2308783.
99. Y. Yang, L. Yang, M. Wang, Q. Yang, X. Liu, J. Shen, G. Liu and M. Zheng, Chemosphere, 2020, 255, 126958.
100. Y. Qian, G. Meng, Q. Huang, C. Zhu, Z. Huang, K. Sun and B. Chen, Nanoscale, 2014, 6, 4781–4788.
101. P. R. S. Kodavanti and R. C. Gupta, Reproductive and Developmental Toxicology, 2011.
102. L. N. Vandenberg, M. V. Maffini, C. Sonnenschein, B. S. Rubin and A. M. Soto, Endocr. Rev., 2009, 30, 75–95.
103. B. S. Rubin, J. Steroid Biochem. Mol. Biol., 2011, 127, 27–34.
104. M. V. Maffini, B. S. Rubin, C. Sonnenschein and A. M. Soto, Mol. Cell. Endocrinol., 2006, 254, 179–186.
105. Z. Xu, L. Xu, Y. Zhu, W. Ma, H. Kuang, L. Wang and C. Xu, Chem. Commun., 2012, 48, 5760–5762.
106. H. Shaikh, G. Sener, N. Memon, M. I. Bhanger, S. M. Nizamani, R. Üzek and A. Denizli, Anal. Methods, 2015, 7, 4661–4670.
107. A. Tariq, M. Waqas, T. Beihai, M. Iqbal, S. Ali, S. U. Khan, N. Abbas, M. N. ul Hassan and Q. Ali, Pacific Int. J., 2019, 2, 98–103.
108. M. R. Mirghafouri, S. Abbasi-Moayed, F. Ghasemi and M. R. Hormozi-Nezhad, Anal. Methods, 2020, 12, 5877–5884.
109. H. Fan, R. Li, Y. Chen, H. Zhang, S. Zeng, W. Ji, W. Hu, S. Yin, Y. Li and G. L. Liu, Biosens. Bioelectron., 2024, 248, 115974.
110. S.-H. Wang, S.-C. Lo, Y.-J. Tung, C.-W. Kuo, Y.-H. Tai, S.-Y. Hsieh, K.-L. Lee, S.-R. Hsiao, J.-F. Sheen and J.-C. Hsu, Biosens. Bioelectron., 2020, 170, 112677.
111. M. Koushkestani, S. Abbasi-Moayed, F. Ghasemi, V. Mahdavi and M. R. Hormozi-Nezhad, Food Chem. Toxicol., 2021, 151, 112109.
112. P. Wang, L. Wu, Z. Lu, Q. Li, W. Yin, F. Ding and H. Han, Anal. Chem., 2017, 89, 2424–2431.
113. S. Liu, S. G. Liu, W. Jiang, Q. Zhao, W. Gao and X. Shi, ACS Appl. Nano Mater., 2022, 5, 16402–16412.
114. X. Fu, S. Wang, S. Mourdikoudis, J. Sun, X. Liu, W. Fu, Y. Zhang, F. Ma, E. Liang and G. Zheng, ACS Sustainable Chem. Eng., 2022, 11, 29–37.
115. M. M. Bordbar, T.-A. Nguyen, A. Q. Tran and H. Bagheri, Sci. Rep., 2020, 10, 17302.
116. M. Koushkestani, F. Ghasemi and M. R. Hormozi-Nezhad, ACS Appl. Nano Mater., 2024, 7, 2764–2774.
117. J. Pizzorno, The Toxin Solution: How Hidden Poisons in the Air, Water, Food, and Products We Use are Destroying Our Health--and what We Can Do to Fix it, HarperCollins, 2017.
118. M. Badar, F. Batool, S. S. Khan, I. Khokhar, M. Qamar and C. Yasir, Buffalo Bull., 2017, 36, 43–56.
119. C.-H. Zhang, L.-W. Liu, P. Liang, L.-J. Tang, R.-Q. Yu and J.-H. Jiang, Anal. Chem., 2016, 88, 7447–7452.
120. D. Bass, K. W. Christison, G. D. Stentiford, L. S. Cook and H. Hartikainen, Trends Parasitol., 2023, 39, 285–304.
121. M. C. Yates, A. M. Derry and M. E. Cristescu, Trends Ecol. Evol., 2021, 36, 601–609.
122. Y. H. Jang, K. Chung, L. N. Quan, B. Špačková, H. Šípová, S. Moon, W. J. Cho, H.-Y. Shin, Y. J. Jang and J.-E. Lee, Nanoscale, 2013, 5, 12261–12271.
123. K. Chung, J. Lee, J.-E. Lee, J. Y. Lee, S. Moon, K. H. A. Lau, D. Kim and D. H. Kim, Sens. Actuators, B, 2013, 176, 1074–1080.
124. D. Zhang, S. Du, S. Su, Y. Wang and H. Zhang, Biosens. Bioelectron., 2019, 123, 19–24.
125. W. Liu, X. Wang, C. Tai, W. Yan, R. Yu, Y. Li, H. Zhao and F. Zhou, Anal. Chem., 2020, 92, 15705–15710.
126. Y. Wang, Z. Qin, D. R. Boulware, B. S. Pritt, L. M. Sloan, I. J. González, D. Bell, R. R. Rees-Channer, P. Chiodini and W. C. Chan, Anal. Chem., 2016, 88, 11774–11782.
127. S. H. Lee, S. Choi, K. Kwon, N.-H. Bae, B. S. Kwak, W. C. Cho, S. J. Lee and H.-I. Jung, Sens. Actuators, B, 2017, 246, 471–476.
128. Y. Wu, Y. Zhou, H. Huang, X. Chen, Y. Leng, W. Lai, X. Huang and Y. Xiong, Sens. Actuators, B, 2020, 316, 128107.
129. R. De La Rica and M. M. Stevens, Nat. Nanotechnol., 2012, 7, 821–824.
130. J. Luan, A. Seth, R. Gupta, Z. Wang, P. Rathi, S. Cao, H. Gholami Derami, R. Tang, B. Xu and S. Achilefu, Nat. Biomed. Eng., 2020, 4, 518–530.
131. R. Gupta, P. Gupta, S. Wang, A. Melnykov, Q. Jiang, A. Seth, Z. Wang, J. J. Morrissey, I. George and S. Gandra, Nat. Biomed. Eng., 2023, 7, 1556–1570.
132. V. Lamberti, M. Dolci and P. Zijlstra, ACS Nano, 2024, 18, 5805–5813.
133. B. Zhang, R. B. Kumar, H. Dai and B. J. Feldman, Nat. Med., 2014, 20, 948–953.
134. V. Flauraud, R. Regmi, P. M. Winkler, D. T. L. Alexander, H. Rigneault, N. F. van Hulst, M. F. Garcia-Parajo, J. Wenger and J. Brugger, Nano Lett., 2017, 17, 1703–1710.
135. B. Liu, Y. Li, H. Wan, L. Wang, W. Xu, S. Zhu, Y. Liang, B. Zhang, J. Lou and H. Dai, Adv. Funct. Mater., 2016, 26, 7994–8002.
136. W. Xu, L. Wang, R. Zhang, X. Sun, L. Huang, H. Su, X. Wei, C.-C. Chen, J. Lou and H. Dai, Nat. Commun., 2020, 11, 1–9.
137. L. K. Chin, J.-Y. Yang, B. Chousterman, S. Jung, D.-G. Kim, D.-H. Kim, S. Lee, C. M. Castro, R. Weissleder and S.-G. Park, ACS Nano, 2023, 17, 3610–3619.
138. Y. Xianyu, Z. Wang and X. Jiang, ACS Nano, 2014, 8, 12741–12747.
139. J. R. Waldeisen, T. Wang, B. M. Ross and L. P. Lee, ACS Nano, 2011, 5, 5383–5389.
140. D. Sevenler, G. G. Daaboul, F. E. Kanik, N. L. Unlu and M. S. Unlu, ACS Nano, 2018, 12, 5880–5887.
141. S. S. Lee and L. P. Lee, Nanoscale, 2014, 6, 3561–3565.
142. C. Wang, X. G. Nie, Y. Shi, Y. Zhou, J. J. Xu, X. H. Xia and H. Y. Chen, ACS Nano, 2017, 11, 5897–5905.
143. Y. Chen, Y. Zhang, F. Pan, J. Liu, K. Wang, C. Zhang, S. Cheng, L. Lu, W. Zhang and Z. Zhang, ACS Nano, 2016, 10, 8169–8179.
144. K. Liu, Y. C. Bai, L. Zhang, Z. B. Yang, Q. K. Fan, H. Q. Zheng, Y. D. Yin and C. B. Gao, Nano Lett., 2016, 16, 3675–3681.
145. S. Cherukulappurath, S. H. Lee, A. Campos, C. L. Haynes and S.-H. Oh, Chem. Mater., 2014, 26, 2445–2452.
146. J.-H. Lee, M.-H. You, G.-H. Kim and J.-M. Nam, Nano Lett., 2014, 14, 6217–6225.
147. J. Song, F. Wang, X. Yang, B. Ning, M. G. Harp, S. H. Culp, S. Hu, P. Huang, L. Nie and J. Chen, J. Am. Chem. Soc., 2016, 138, 7005–7015.
148. S. Simoncelli, E.-M. Roller, P. Urban, R. Schreiber, A. J. Turberfield, T. Liedl and T. Lohmüller, ACS Nano, 2016, 10, 9809–9815.
149. S. Yamazoe, M. Naya, M. Shiota, T. Morikawa, A. Kubo, T. Tani, T. Hishiki, T. Horiuchi, M. Suematsu and M. Kajimura, ACS Nano, 2014, 8, 5622–5632.
150. Y. Lu, G. L. Liu, J. Kim, Y. X. Mejia and L. P. Lee, Nano Lett., 2005, 5, 119–124.
151. G. L. Liu, Y. Lu, J. Kim, J. C. Doll and L. P. Lee, Adv. Mater., 2005, 17, 2683–2688.
152. S. R. Panikkanvalappil, S. M. Hira, M. A. Mahmoud and M. A. El-Sayed, J. Am. Chem. Soc., 2014, 136, 15961–15968.
153. S. Hong, M. Y. Lee, A. O. Jackson and L. P. Lee, Light: Sci. Appl., 2015, 4, e267.
154. J. Xie, Q. Zhang, J. Y. Lee and D. I. Wang, ACS Nano, 2008, 2, 2473–2480.
155. P. Thirumurugan, D. Matosiuk and K. Jozwiak, Chem. Rev., 2013, 113, 4905–4979.
156. N. K. Devaraj and M. Finn, Chem. Rev., 2021, 121, 6697–6698.
157. A. H. El-Sagheer and T. Brown, Acc. Chem. Res., 2012, 45, 1258–1267.
158. N. Z. Fantoni, A. H. El-Sagheer and T. Brown, Chem. Rev., 2021, 121, 7122–7154.
159. D. Kato and M. Oishi, ACS Nano, 2014, 8, 9988–9997.
160. J. Park and J.-S. Yeo, Chem. Commun., 2014, 50, 1366–1368.
161. S. Vial, Y. Berrahal, M. Prado and J. Wenger, ACS Sens., 2017, 2, 251–256.
162. D. Kavungal, P. Magalhães, S. T. Kumar, R. Kolla, H. A. Lashuel and H. Altug, Sci. Adv., 2023, 9, eadg9644.
163. R. Li, H. L. Fan, H. L. Zhou, Y. Q. Chen, Q. C. Yu, W. J. Hu, G. L. Liu and L. P. Huang, Adv. Sci., 2023, 10, 2301658.
164. H. Haick, Y. Y. Broza, P. Mochalski, V. Ruzsanyi and A. Amann, Chem. Soc. Rev., 2014, 43, 1423–1449.
165. Y. Cai, L. Huang, W. Sun, C. Xu, X. Ren, Y. Ye, Y. Zhu, L. Sun, D. Jiang and Q. Zhang, Sens. Actuators, B, 2023, 381, 133411.
166. N. Meinzer, W. L. Barnes and I. R. Hooper, Nat. Photonics, 2014, 8, 889–898.
167. I. Kim, H. Kim, S. Han, J. Kim, Y. Kim, S. Eom, A. Barulin, I. Choi, J. Rho and L. P. Lee, Adv. Mater., 2023, 35, 2300229.
168. C. K. Carberry and J. E. Rager, Environ. Mol. Mutagen., 2023, 64, 50–66.
169. C. M. Eckhardt, A. A. Baccarelli and H. Wu, Curr. Environ. Health Rep., 2022, 9, 465–476.
170. D. Karpman, A.-l Ståhl and I. Arvidsson, Nat. Rev. Nephrol., 2017, 13, 545–562.
171. R. Kalluri and V. S. LeBleu, Science, 2020, 367, eaau6977.
172. M. Patil, J. Henderson, H. Luong, D. Annamalai, G. Sreejit and P. Krishnamurthy, Front. Cell Dev. Biol., 2019, 7, 315.
173. W. Zhao, X.-L. Zheng and S.-P. Zhao, Heart Failure Rev., 2015, 20, 337–348.
174. W. Liu, X. Bai, A. Zhang, J. Huang, S. Xu and J. Zhang, Front. Mol. Neurosci., 2019, 12, 240.
175. V. Thongboonkerd, Front. Pharmacol., 2020, 10, 1655.
176. I. Wortzel, S. Dror, C. M. Kenific and D. Lyden, Dev. Cell, 2019, 49, 347–360.
177. K. M. McAndrews and R. Kalluri, Mol. Cancer, 2019, 18, 1–11.
178. Y. Chen, Q. Zhu, L. Cheng, Y. Wang, M. Li, Q. Yang, L. Hu, D. Lou, J. Li, X. Dong, L. Luke and F. Liu, Nat. Methods, 2021, 18, 212–218.
179. Q. Zhu, L. Cheng, C. Deng, L. Huang, J. Li, Y. Wang, M. Li, Q. Yang, X. Dong, J. Su, L. Lee and F. Liu, Proc. Natl. Acad. Sci. U. S. A., 2021, 118, e2108876118.
180. T. Skotland, K. Sagini, K. Sandvig and A. Llorente, Adv. Drug Delivery Rev., 2020, 159, 308–321.
181. S. Boukouris and S. Mathivanan, Proteomics: Clin. Appl., 2015, 9, 358–367.
182. O. Gusachenko, M. Zenkova and V. Vlassov, Biochemistry, 2013, 78, 1–7.
183. S. J. Biller, F. Schubotz, S. E. Roggensack, A. W. Thompson, R. E. Summons and S. W. Chisholm, Science, 2014, 343, 183–186.
184. M. Cao, N. Diao, X. Cai, X. Chen, Y. Xiao, C. Guo, D. Chen and X. Zhang, Mater. Horiz., 2023, 10, 3879–3894.
185. K. Liang, F. Liu, J. Fan, D. Sun, C. Liu, C. J. Lyon, D. W. Bernard, Y. Li, K. Yokoi and M. H. Katz, Nat. Biomed. Eng., 2017, 1, 1–11.
186. S. Kumar, J. A. Han, I. J. Michael, D. Ki, V. Sunkara, J. Park, S. Gautam, H. K. Ha, L. Zhang and Y. K. Cho, Adv. Funct. Mater., 2019, 29, 1902669.
187. X. Wu, H. Zhao, A. Natalia, C. Z. J. Lim, N. R. Y. Ho, C.-A. J. Ong, M. C. C. Teo, J. B. Y. So and H. Shao, Sci. Adv., 2020, 6, eaba2556.
188. Y. Zhang, D. Wang, S. Yue, Y. Lu, C. Yang, J. Fang and Z. Xu, ACS Sens., 2019, 4, 3210–3218.
189. H. Im, H. Shao, Y. I. Park, V. M. Peterson, C. M. Castro, R. Weissleder and H. Lee, Nat. Biotechnol., 2014, 32, 490–495.
190. D. Raghu, J. A. Christodoulides, M. Christophersen, J. L. Liu, G. P. Anderson, M. Robitaille, J. M. Byers and M. P. Raphael, PLoS One, 2018, 13, e0202773.
191. J. N. Shen, Z. T. Ma, J. Q. Xu, T. H. Xue, X. Q. Lv, G. X. Zhu and B. J. Huang, ACS Appl. Mater. Interfaces, 2024, 16, 22776–22793.
192. W. Liu, S. Pan, H. Zhang, Z. Tang, J. Liang, Y. Wang, M. Zhang, X. Hu, W. Pang and X. Duan, ACS Cent. Sci., 2018, 4, 899–908.
193. A. T. Reiner, N.-G. Ferrer, P. Venugopalan, R. C. Lai, S. K. Lim and J. Dostálek, Analyst, 2017, 142, 3913–3921.
194. K. S. Yang, H. Im, S. Hong, I. Pergolini, A. F. Del Castillo, R. Wang, S. Clardy, C.-H. Huang, C. Pille and S. Ferrone, Sci. Transl. Med., 2017, 9, eaal3226.
195. C. Z. J. Lim, Y. Zhang, Y. Chen, H. Zhao, M. C. Stephenson, N. R. Y. Ho, Y. Chen, J. Chung, A. Reilhac, T. P. Loh, C. L. H. Chen and H. Shao, Nat. Commun., 2019, 10, 1144.
196. X. Zeng, Y. Yang, N. Zhang, D. Ji, X. Gu, J. M. Jornet, Y. Wu and Q. Gan, IEEE J. Sel. Top. Quantum Electron., 2018, 25, 1–7.
197. S. Sharma, P. Sachdeva and J. S. Virdi, Appl. Microbiol. Biotechnol., 2003, 61, 424–428.
198. F. Y. Ramírez-Castillo, A. Loera-Muro, M. Jacques, P. Garneau, F. J. Avelar-González, J. Harel and A. L. Guerrero-Barrera, Pathogens, 2015, 4, 307–334.
199. R. Dhand and J. Li, Am. J. Respir. Crit. Care Med., 2020, 202, 651–659.
200. L. Yao, W. Zhu, J. Shi, T. Xu, G. Qu, W. Zhou, X.-F. Yu, X. Zhang and G. Jiang, Chem. Soc. Rev., 2021, 50, 3656–3676.
201. A. Bogler, A. Packman, A. Furman, A. Gross, A. Kushmaro, A. Ronen, C. Dagot, C. Hill, D. Vaizel-Ohayon and E. Morgenroth, Nat. Sustain., 2020, 3, 981–990.
202. L. Huang, L. Ding, J. Zhou, S. Chen, F. Chen, C. Zhao, J. Xu, W. Hu, J. Ji, H. Xu and G. Logan Liu, Biosens. Bioelectron., 2021, 171, 112685.
203. S. Wang, X. Shan, U. Patel, X. Huang, J. Lu, J. Li and N. Tao, Proc. Natl. Acad. Sci. U. S. A., 2010, 107, 16028–16032.
204. W. Liu and L. P. Lee, Adv. Mater., 2022, 35, 2206525.
205. W. Liu, F. Yue and L. P. Lee, Acc. Chem. Res., 2021, 54, 4107–4119.
206. E. M. Materón, F. R. Gómez, M. B. Almeida, F. M. Shimizu, A. Wong, K. B. Teodoro, F. S. Silva, M. J. Lima, M. K. S. Angelim and M. E. Melendez, ACS Appl. Mater. Interfaces, 2022, 14, 54527–54538.
207. Y. Park, B. Ryu, S. J. Ki, M. Chen, X. Liang and K. Kurabayashi, Nano Lett., 2022, 23, 98–106.
208. B. D. Ventura, M. Cennamo, A. Minopoli, R. Campanile, S. B. Censi, D. Terracciano, G. Portella and R. Velotta, ACS Sens., 2020, 5, 3043–3048.
209. M. Alafeef, P. Moitra, K. Dighe and D. Pan, Nat. Protoc., 2021, 16, 3141–3162.
210. P. Moitra, M. Alafeef, K. Dighe, M. B. Frieman and D. Pan, ACS Nano, 2020, 14, 7617–7627.
211. E. V. Usachev, E. Agranovski, O. V. Usacheva and I. E. Agranovski, J. Aerosol Sci., 2015, 90, 136–143.
212. G. Qiu, Y. Yue, J. Tang, Y.-B. Zhao and J. Wang, Environ. Sci. Technol., 2020, 54, 1353–1362.
213. E. V. Usachev, A. M. Tam, O. V. Usacheva and I. E. Agranovski, J. Aerosol Sci., 2014, 76, 39–47.
214. E. Usachev, O. Usacheva and I. Agranovski, J. Appl. Microbiol., 2013, 115, 766–773.
215. G. Qiu, M. Spillmann, J. Tang, Y. B. Zhao, Y. Tao, X. Zhang, H. Geschwindner, L. Saleh, W. Zingg and J. Wang, Adv. Sci., 2022, 9, 2204774.
216. Z. Jin, Y. Mantri, M. Retout, Y. Cheng, J. Zhou, A. Jorns, P. Fajtova, W. Yim, C. Moore and M. Xu, Angew. Chem., 2022, 134, e202112995.
217. J. H. Son, B. Cho, S. Hong, S. H. Lee, O. Hoxha, A. J. Haack and L. P. Lee, Light: Sci. Appl., 2015, 4, e280.
218. F. Zang, Z. Su, L. Zhou, K. Konduru, G. Kaplan and S. Y. Chou, Adv. Mater., 2019, 31, 1902331.
219. M.-P. N. Bui, S. Ahmed and A. Abbas, Nano Lett., 2015, 15, 6239–6246.
220. D. Lu, G. Zhu, X. Li, J. Xiong, D. Wang, Y. Shi, T. Pan, B. Li, L. P. Lee and H. Xin, Nat. Photonics, 2023, 1–8.
221. N. R. Blumenfeld, M. A. E. Bolene, M. Jaspan, A. G. Ayers, S. Zarrandikoetxea, J. Freudman, N. Shah, A. M. Tolwani, Y. Hu and T. L. Chern, Nat. Nanotechnol., 2022, 1–9.
222. J. H. Lee, Z. Cheglakov, J. Yi, T. M. Cronin, K. J. Gibson, B. Z. Tian and Y. Weizmann, J. Am. Chem. Soc., 2017, 139, 8054–8057.
223. P. J. R. Roche, M. Najih, S. S. Lee, L. K. Beitel, M. L. Carnevale, M. Paliouras, A. G. Kirk and M. A. Trifiro, Analyst, 2017, 142, 1746–1755.
224. J. Wu, K. Jiang, H. Mi, Y. Qiu, J. Son, H. J. Park, J.-M. Nam and J.-H. Lee, Nanoscale, 2021, 7348–7354.
225. J. Cheong, H. Yu, C. Y. Lee, J.-U. Lee, H.-J. Choi, J.-H. Lee, H. Lee and J. Cheon, Nat. Biomed. Eng., 2020, 4, 1159–1167.
226. K. Jiang, J. Wu, Y. Qiu, Y. Y. Go, K. Ban, H. J. Park and J.-H. Lee, Sens. Actuators, B, 2021, 129762.
227. B.-H. Kang, Y. Lee, E.-S. Yu, H. Na, M. Kang, H. J. Huh and K.-H. Jeong, ACS Nano, 2021, 10194–10202.
228. B.-H. Kang, K.-W. Jang, E.-S. Yu, H. Na, Y.-J. Lee, W.-Y. Ko, N. Bae, D. Rho and K.-H. Jeong, ACS Nano, 2023, 17, 6507–6518.
229. T. AbdElFatah, M. Jalali, S. G. Yedire, I. I. Hosseini, C. del Real Mata, H. Khan, S. V. Hamidi, O. Jeanne, R. Siavash Moakhar and M. McLean, Nat. Nanotechnol., 2023, 1–11.
230. Y. Liu, H. Ye, H. Huynh, C. Xie, P. Kang, J. S. Kahn and Z. Qin, Nat. Commun., 2022, 13, 1–11.
231. S. Seo, T.-W. Chang and G. L. Liu, Sci. Rep., 2018, 8, 3002.
232. H.-D. Um, N. Kim, K. Lee, I. Hwang, J. H. Seo, J. Y. Young, P. Duane, M. Wober and K. Seo, Sci. Rep., 2015, 5, 11277.
233. R. A. Lai, T. M. Hymel, V. K. Narasimhan and Y. Cui, ACS Appl. Mater. Interfaces, 2016, 8, 8875–8879.
234. D. D. Ling, Z. L. Wu, S. J. Li, W. Q. Zhao, C. J. Ma, J. Wang, Z. M. Jiang, Z. Y. Zhong, Y. B. Zheng and X. J. Yang, ACS Nano, 2017, 11, 1478–1487.
235. C. Dai, Z. Lin, K. Agarwal, C. Mikhael, A. Aich, K. Gupta and J.-H. Cho, Nano Lett., 2020, 20, 6697–6705.
236. B. Cho, S. H. Lee, J. Song, S. Bhattacharjee, J. Feng, S. Hong, M. Song, W. Kim, J. Lee, D. Bang, B. Wang, L. Riley and L. Lee, ACS Nano, 2019, 13, 13866–13874.
237. B. de Nijs, F. Benz, S. J. Barrow, D. O. Sigle, R. Chikkaraddy, A. Palma, C. Carnegie, M. Kamp, R. Sundararaman, P. Narang, O. A. Scherman and J. J. Baumberg, Nat. Commun., 2017, 8, 994.
238. F. Benz, M. K. Schmidt, A. Dreismann, R. Chikkaraddy, Y. Zhang, A. Demetriadou, C. Carnegie, H. Ohadi, B. de Nijs, R. Esteban, J. Aizpurua and J. J. Baumberg, Science, 2016, 354, 726–729.
239. Z. Gao, Y. Song, T. Y. Hsiao, J. He, C. Wang, J. Shen, A. MacLachlan, S. Dai, B. H. Singer and K. Kurabayashi, ACS Nano, 2021, 15, 18023–18036.
240. M. N. Holme, S. Rana, H. M. Barriga, U. Kauscher, N. J. Brooks and M. M. Stevens, ACS Nano, 2018, 12, 8197–8207.
241. C. R. MacNair, S. T. Rutherford and M.-W. Tan, Nat. Rev. Microbiol., 2024, 22, 262–275.
242. A. van Belkum, C.-A. D. Burnham, J. W. Rossen, F. Mallard, O. Rochas and W. M. Dunne Jr, Nat. Rev. Microbiol., 2020, 18, 299–311.
243. D. D. Galvan and Q. Yu, Adv. Healthcare Mater., 2018, 7, 1701335.
244. S. Singh, A. Numan and S. Cinti, Anal. Chem., 2021, 94, 26–40.
245. R. Huang, X. Cai, J. Du, J. Lian, P. Hui, M. Gu, F. Li, J. Wang and W. Chen, ACS Nano, 2022, 16, 19229–19239.
246. J. Zhang, M. Wang, J. Xiao, M. Wang, Y. Liu and X. Gao, ACS Sens., 2024, 9, 379–387.
247. T. Yu, Y. Fu, J. T. He, J. Zhang and Y. L. Xianyu, ACS Nano, 2023, 17, 4551–4563.
248. K. Syal, R. Iriya, Y. Yang, H. Yu, S. Wang, S. E. Haydel, H.-Y. Chen and N. Tao, ACS Nano, 2016, 10, 845–852.
249. S. Nomura, J. Hum. Genet., 2021, 66, 75–84.
250. B. Zhang, K. Huang, L. Zhu, Y. Luo and W. Xu, Arch. Toxicol., 2017, 91, 2539–2549.
251. O. Rozenblatt-Rosen, M. J. Stubbington, A. Regev and S. A. Teichmann, Nature, 2017, 550, 451–453.
252. M. Liu, Q. Li, L. Liang, J. Li, K. Wang, J. Li, M. Lv, N. Chen, H. Song and J. Lee, Nat. Commun., 2017, 8, 1–10.
253. H. J. An, Y. Kim, S. Chang, H. Kim, J. Song, H. Park and I. Choi, Nat. Commun., 2021, 12, 1–11.
254. R. G. Sobral-Filho, L. DeVorkin, S. Macpherson, A. Jirasek, J. J. Lum and A. G. Brolo, ACS Nano, 2018, 12, 1902–1909.
255. F. Lussier, T. Brulé, M. Vishwakarma, T. Das, J. P. Spatz and J.-F. O. Masson, Nano Lett., 2016, 16, 3866–3871.
256. S. E. Lee, Q. Chen, R. Bhat, S. Petkiewicz, J. M. Smith, V. E. Ferry, A. L. Correia, A. P. Alivisatos and M. J. Bissell, Nano Lett., 2015, 15, 4564–4570.
257. K. Lee, Y. Cui, L. P. Lee and J. Irudayaraj, Nat. Nanotechnol., 2014, 9, 474.
258. W. E. Briley, M. H. Bondy, P. S. Randeria, T. J. Dupper and C. A. Mirkin, Proc. Natl. Acad. Sci. U. S. A., 2015, 112, 9591–9595.
259. H. Xin, W. J. Sim, B. Namgung, Y. Choi, B. Li and L. P. Lee, Nat. Commun., 2019, 10, 1–11.
260. S. Ansaryan, Y.-C. Liu, X. Li, A. M. Economou, C. S. Eberhardt, C. Jandus and H. Altug, Nat. Biomed. Eng., 2023, 1–16.
261. Y.-C. Liu, S. Ansaryan, X. Li, E. R. Arvelo and H. Altug, Biosens. Bioelectron., 2022, 202, 113955.
262. F. Yesilkoy, R. A. Terborg, J. Pello, A. A. Belushkin, Y. Jahani, V. Pruneri and H. Altug, Light: Sci. Appl., 2018, 7, 17152.
263. R. Batool, M. Soler, F. Colavita, L. Fabeni, G. Matusali and L. M. Lechuga, Biosens. Bioelectron., 2023, 226, 115137.
264. J.-H. Song, S. Raza, J. van de Groep, J.-H. Kang, Q. Li, P. G. Kik and M. L. Brongersma, Nat. Commun., 2021, 12, 48.
265. E. K. Herkert, D. R. B. Alvaro, M. Recchia, W. Langbein, P. Borri and M. F. Garcia-Parajo, ACS Nano, 2023, 17, 8453–8464.
266. J. H. Han, Y. C. Lim, R. M. Kim, J. Lv, N. H. Cho, H. Kim, S. D. Namgung, S. W. Im and K. T. Nam, ACS Nano, 2023, 2306–2317.
267. Z. C. Yu, L. Pan, X. W. Ma, T. M. Li, F. K. Wang, D. L. Yang, M. Li and P. F. Wang, Biosens. Bioelectron., 2023, 237, 115526.
268. D. Punj, R. Regmi, A. Devilez, R. Plauchu, S. B. Moparthi, B. Stout, N. Bonod, H. Rigneault and J. R. M. Wenger, ACS Photonics, 2015, 2, 1099–1107.
269. L. Lermusiaux, A. Sereda, B. Portier, E. Larquet and S. Bidault, ACS Nano, 2012, 6, 10992–10998.
270. C. P. Byers, B. S. Hoener, W.-S. Chang, S. Link and C. F. Landes, Nano Lett., 2016, 16, 2314–2321.
271. M. Zhang, N. Large, A. L. Koh, Y. Cao, A. Manjavacas, R. Sinclair, P. Nordlander and S. X. Wang, ACS Nano, 2015, 9, 9331–9339.
272. A. Puchkova, C. Vietz, E. Pibiri, B. Wunsch, M. S. Paz, G. P. Acuna and P. Tinnefeld, Nano Lett., 2015, 15, 8354–8359.
273. L. Lermusiaux, V. Maillard and S. Bidault, ACS Nano, 2015, 9, 978–990.
274. Y. Y. Huang, P. W. Chen, L. Y. Zhou, J. Y. Zheng, H. T. Wu, J. X. Liang, A. X. Xiao, J. Li and B. O. Guan, Adv. Mater., 2023, 35, 2304116.
275. D. S. Seferos, D. A. Giljohann, H. D. Hill, A. E. Prigodich and C. A. Mirkin, J. Am. Chem. Soc., 2007, 129, 15477–15479.
276. C. H. J. Choi, L. Hao, S. P. Narayan, E. Auyeung and C. A. Mirkin, Proc. Natl. Acad. Sci. U. S. A., 2013, 110, 7625–7630.
277. T. L. Halo, K. M. McMahon, N. L. Angeloni, Y. Xu, W. Wang, A. B. Chinen, D. Malin, E. Strekalova, V. L. Cryns and C. Cheng, Proc. Natl. Acad. Sci. U. S. A., 2014, 111, 17104–17109.
278. G. E. Alexander, Dialogues Clin. Neurosci., 2022, 259–280.
279. A. Bose and M. F. Beal, J. Neurochem., 2016, 139, 216–231.
280. W. D. Parker Jr, S. J. Boyson and J. K. Parks, Ann. Neurol., 1989, 26, 719–723.
281. J. Y. Lee, Y. Park, S. Pun, S. S. Lee, J. F. Lo and L. P. Lee, Nanoscale, 2015, 7, 10340–10343.
282. X. Zhang, Z. Zhu, W. Liu, F. Gao, J. Guo, B. Song, L. P. Lee and F. Zhang, J. Phys. Chem. Lett., 2022, 13, 7779–7787.
283. A. Jain, J. Gosling, S. Liu, H. Wang, E. M. Stone, S. Chakraborty, P.-S. Jayaraman, S. Smith, D. B. Amabilino and M. Fromhold, Nat. Nanotechnol., 2024, 19, 106–114.
284. S. S. Evans, E. A. Repasky and D. T. Fisher, Nat. Rev. Immunol., 2015, 15, 335–349.
285. A. Choe, J. Yeom, R. Shanker, M. P. Kim, S. Kang and H. Ko, NPG Asia Mater., 2018, 10, 912–922.
286. W. N. Zhang, X. W. Huang, W. J. Liu, Z. S. Gao, L. Y. Zhong, Y. W. Qin, B. J. Li and J. Li, ACS Appl. Mater. Interfaces, 2023, 15, 4469–4476.
287. J. Guo, C. Shang, S. Gao, Y. Zhang, B. Fu and L. Xu, Adv. Mater. Technol., 2023, 2201506.
288. J. Guo, B. Zhou, R. Zong, L. Pan, X. Li, X. Yu, C. Yang, L. Kong and Q. Dai, ACS Appl. Mater. Interfaces, 2019, 11, 33589–33598.
289. U. Mogera, H. Guo, M. Namkoong, M. S. Rahman, T. Nguyen and L. Tian, Sci. Adv., 2022, 8, eabn1736.
290. M. A. Cretikos, R. Bellomo, K. Hillman, J. Chen, S. Finfer and A. Flabouris, Med. J. Aust., 2008, 188, 657–659.
291. S. A. Taplidou and L. J. Hadjileontiadis, Comput. Biol. Med., 2007, 37, 1073–1083.
292. J. F. Thayer, S. S. Yamamoto and J. F. Brosschot, Int. J. Cardiol., 2010, 141, 122–131.
293. J. Wang, Y. Zhu, Z. Wu, Y. Zhang, J. Lin, T. Chen, H. Liu, F. Wang and L. Sun, Microsyst. Nanoeng., 2022, 8, 16.
294. T. Liu, G.-y Gou, F. Gao, P. Yao, H. Wu, Y. Guo, M. Yin, J. Yang, T. Wen and M. Zhao, ACS Nano, 2023, 17, 5673–5685.
295. G. DeMaagd and A. Philip, P&T, 2015, 40, 504.
296. A. J. Bandodkar, W. J. Jeang, R. Ghaffari and J. A. Rogers, Annu. Rev. Anal. Chem., 2019, 12, 1–22.
297. M. Bariya, H. Y. Y. Nyein and A. Javey, Nat. Electron., 2018, 1, 160–171.
298. S. Emaminejad, W. Gao, E. Wu, Z. A. Davies, H. Yin Yin Nyein, S. Challa, S. P. Ryan, H. M. Fahad, K. Chen and Z. Shahpar, Proc. Natl. Acad. Sci. U. S. A., 2017, 114, 4625–4630.
299. S. Y. Oh, S. Y. Hong, Y. R. Jeong, J. Yun, H. Park, S. W. Jin, G. Lee, J. H. Oh, H. Lee and S.-S. Lee, ACS Appl. Mater. Interfaces, 2018, 10, 13729–13740.
300. Y. Yang, Y. Song, X. Bo, J. Min, O. S. Pak, L. Zhu, M. Wang, J. Tu, A. Kogan and H. Zhang, Nat. Biotechnol., 2020, 38, 217–224.
301. S. Kodama, K. Saito, Y. Yachi, M. Asumi, A. Sugawara, K. Totsuka, A. Saito and H. Sone, Diabetes Care, 2009, 32, 1737–1742.
302. K. Kohagura, M. Kochi, T. Miyagi, T. Kinjyo, Y. Maehara, K. Nagahama, A. Sakima, K. Iseki and Y. Ohya, Hypertens. Res., 2013, 36, 43–49.
303. Y. Li, Y. X. Guo, H. L. Chen, X. Xiao, F. D. Long, H. Q. Zhong, K. Wang, Z. Y. Guo, Z. F. Zhuang and Z. M. Liu, ACS Photonics, 2024, 11, 613–625.
304. Y. Han, X. H. Fang, H. L. Li, L. Zha, J. X. Guo and X. P. Zhang, ACS Sens., 2023, 8, 4774–4781.
305. X. Liu, T. H. Li, T. C. Lee, Y. T. Sun, Y. H. Liu, L. Shang, Y. Y. Han, W. B. Deng, Z. Q. Yuan and A. L. Dang, ACS Sens., 2024, 9, 483–493.
306. X. He, C. Fan, Y. Luo, T. Xu and X. Zhang, npj Flex. Electron., 2022, 6, 60.
307. J. Xiao, J. Wang, Y. Luo, T. Xu and X. Zhang, ACS Sens., 2023, 8, 1766–1773.
308. Y. Wang, C. Zhao, J. Wang, X. Luo, L. Xie, S. Zhan, J. Kim, X. Wang, X. Liu and Y. Ying, Sci. Adv., 2021, 7, eabe4553.
309. K. Zhu, K. Yang, Y. Z. Zhang, Z. Y. Yang, Z. T. Qian, N. Li, L. Li, G. H. Jiang, T. Y. Wang, S. F. Zong, L. Wu, Z. Y. Wang and Y. P. Cui, Small, 2022, 18, 2201508.
310. M. Friedel, I. A. Thompson, G. Kasting, R. Polsky, D. Cunningham, H. T. Soh and J. Heikenfeld, Nat. Biomed. Eng., 2023, 7, 1541–1555.
311. Z. Wu, Z. Qiao, S. Chen, S. Fan, Y. Liu, J. Qi and C. T. Lim, Commun. Mater., 2024, 5, 33.
312. D. Rybak, Y.-C. Su, Y. Li, B. Ding, X. Lv, Z. Li, Y.-C. Yeh, P. Nakielski, C. Rinoldi and F. Pierini, Nanoscale, 2023, 15, 8044–8083.
313. J. Wang, Z. Lu, R. Cai, H. Zheng, J. Yu, Y. Zhang and Z. Gu, Lab Chip, 2023, 23, 869–887.
314. Z. Wang, J. Luan, A. Seth, L. Liu, M. You, P. Gupta, P. Rathi, Y. Wang, S. Cao and Q. Jiang, Nat. Biomed. Eng., 2021, 5, 64–76.
315. J. Ju, C.-M. Hsieh, Y. Tian, J. Kang, R. Chia, H. Chang, Y. Bai, C. Xu, X. Wang and Q. Liu, ACS Sens., 2020, 5, 1777–1785.
316. V. T. N. Linh, S.-G. Yim, C. Mun, J.-Y. Yang, S. Lee, Y. W. Yoo, D. K. Sung, Y.-I. Lee, D.-H. Kim and S.-G. Park, Appl. Surf. Sci., 2021, 551, 149411.
317. J. E. Park, N. Yonet-Tanyeri, E. Vander Ende, A.-I. Henry, B. E. Perez White, M. Mrksich and R. P. Van Duyne, Nano Lett., 2019, 19, 6862–6868.
318. B. Miranda, M. Battisti, S. De Martino, V. Nocerino, P. Dardano, L. De Stefano and G. Cangiano, Adv. Mater. Technol., 2023, 8, 2300037.
319. J. S. Kee, S. Y. Lim, A. P. Perera, Y. Zhang and M. K. Park, Sens. Actuators, B, 2013, 182, 576–583.
320. J. Y. Xiao, S. X. Zhang, Q. Z. Liu, T. L. Xu and X. J. Zhang, Sens. Actuators, B, 2024, 398, 134685.
321. J. B. Edel, A. A. Kornyshev and M. Urbakh, ACS Nano, 2013, 7, 9526–9532.
322. V. Flauraud, M. Mastrangeli, G. D. Bernasconi, J. Butet, D. T. Alexander, E. Shahrabi, O. J. Martin and J. Brugger, Nat. Nanotechnol., 2017, 12, 73–80.
323. L. Y. Wu, B. M. Ross and L. P. Lee, Nano Lett., 2009, 9, 1956–1961.
324. S. Gupta, C. H. Huang, G. P. Singh, B. S. Park, N.-H. Chua and R. J. Ram, Sci. Rep., 2020, 10, 20206.
325. C. Duan, J. Li, Y. Zhang, K. Ding, X. Geng and Y. Guan, TrAC, Trends Anal. Chem., 2022, 154, 116653.
326. Z. Ballard, C. Brown, A. M. Madni and A. Ozcan, Nat. Mach. Intell., 2021, 3, 556–565.
327. M. Oliverio, S. Perotto, G. C. Messina, L. Lovato and F. De Angelis, ACS Appl. Mater. Interfaces, 2017, 9, 29394–29411.
328. R. D’Agata, N. Bellassai, V. Jungbluth and G. Spoto, Polymers, 2021, 13, 1929.
329. A. Frutiger, A. Tanno, S. Hwu, R. F. Tiefenauer, J. Voros and N. Nakatsuka, Chem. Rev., 2021, 121, 8095–8160.
330. J. A. Jackman, A. R. Ferhan and N.-J. Cho, Chem. Soc. Rev., 2017, 46, 3615–3660.
331. T. Pham, J. B. Jackson, N. J. Halas and T. R. Lee, Langmuir, 2002, 18, 4915–4920.
332. D. Zopes, B. Stein, S. Mathur and C. Graf, Langmuir, 2013, 29, 11217–11226.
333. S. Engel, E.-C. Fritz and B. J. Ravoo, Chem. Soc. Rev., 2017, 46, 2057–2075.
334. C. Wang, Z. Ma, T. Wang and Z. Su, Adv. Funct. Mater., 2006, 16, 1673–1678.
335. S. Kim, J. Seo, H. H. Park, N. Kim, J.-W. Oh and J.-M. Nam, Acc. Chem. Res., 2019, 52, 2793–2805.
336. O. Limaj, D. Etezadi, N. J. Wittenberg, D. Rodrigo, D. Yoo, S.-H. Oh and H. Altug, Nano Lett., 2016, 16, 1502–1508.
337. M. Soler, C. S. Huertas and L. M. Lechuga, Expert Rev. Mol. Diagn., 2019, 19, 71–81.
338. F. B. Myers and L. P. Lee, Lab Chip, 2008, 8, 2015–2031.
339. M. Yafia, O. Ymbern, A. O. Olanrewaju, A. Parandakh, A. Sohrabi Kashani, J. Renault, Z. Jin, G. Kim, A. Ng and D. Juncker, Nature, 2022, 605, 464–469.
340. E.-C. Yeh, C.-C. Fu, L. Hu, R. Thakur, J. Feng and L. P. Lee, Sci. Adv., 2017, 3, e1501645.
341. R. L. Rich and D. G. Myszka, Curr. Opin. Biotechnol, 2000, 11, 54–61.
342. B. Špačková, N. S. Lynn Jr, J. I. Slaby, H. Šípová and J. I. Homola, ACS Photonics, 2018, 5, 1019–1025.
343. N. J. de Mol and M. J. Fischer, Methods Mol. Biol., 2010, 627, 55–73.
344. N. Polley, S. Sardar, P. Werner, I. Gersonde, Y. Kanehira, I. Bald, D. Repp, T. Pertsch and C. Pacholski, ACS Nano, 2023, 17, 1403–1413.
345. Y. J. Song, P. Y. Chen, M. T. Chung, R. Nidetz, Y. Park, Z. H. Liu, W. McHugh, T. T. Cornell, J. P. Fu and K. Kurabayashi, Nano Lett., 2017, 17, 2374–2380.
346. J. Fritzsche, D. Albinsson, M. Fritzsche, T. J. Antosiewicz, F. Westerlund and C. Langhammer, Nano Lett., 2016, 16, 7857–7864.
347. N. Schuwirth, F. Borgwardt, S. Domisch, M. Friedrichs, M. Kattwinkel, D. Kneis, M. Kuemmerlen, S. D. Langhans, J. Martínez-López and P. Vermeiren, Ecol. Modell., 2019, 411, 108784.
348. G. Hanrahan, S. Ussher, M. Gledhill, E. P. Achterberg and P. J. Worsfold, TrAC, Trends Anal. Chem., 2002, 21, 233–239.
349. N. Schwalbe and B. Wahl, The Lancet, 2020, 395, 1579–1586.
350. S. Subramaniam, N. Raju, A. Ganesan, N. Rajavel, M. Chenniappan, C. Prakash, A. Pramanik, A. K. Basak and S. Dixit, Sustainability, 2022, 14, 9951.
351. J. F. Masson, J. S. Biggins and E. Ringe, Nat. Nanotechnol., 2023, 18, 111–123.
352. Y. Sugimoto, K. Kajikawa, D. Mayer, A. Offenhäusser and M. Toma, ACS Appl. Nano Mater., 2024 DOI:10.1021/acsanm.4c00274.
353. X. M. Kong, Y. T. Xi, P. Le Duff, X. Y. Chong, E. W. Li, F. H. Ren, G. L. Rorrer and A. X. Wang, Biosens. Bioelectron., 2017, 88, 63–70.
354. K. Sivashanmugan, K. Squire, J. A. Kraai, A. Tan, Y. Zhao, G. L. Rorrer and A. X. Wang, Adv. Opt. Mater., 2019, 7, 1900415.
355. J. Guo, Z. Liang, Y. Huang, K. Kim, P. Vandeventer and D. Fan, ACS Nano, 2020, 14, 15204–15215.
356. A. Li, X. Zhao, G. Duan, S. Anderson and X. Zhang, Adv. Funct. Mater., 2019, 29, 1809029.
357. M. Kolle, P. M. Salgard-Cunha, M. R. Scherer, F. Huang, P. Vukusic, S. Mahajan, J. J. Baumberg and U. Steiner, Nat. Nanotechnol., 2010, 5, 511–515.
358. N. L. Garrett, R. Sekine, M. W. Dixon, L. Tilley, K. R. Bambery and B. R. Wood, Phys. Chem. Chem. Phys., 2015, 17, 21164–21168.
359. G. Shi, M. Wang, Y. Zhu, Y. Wang, X. Yan, X. Sun, H. Xu and W. Ma, Beilstein J. Nanotechnol., 2019, 10, 578–588.
360. J. He, N. S. Villa, Z. Luo, S. An, Q. Shen, P. Tao, C. Song, J. Wu, T. Deng and W. Shang, RSC Adv., 2018, 8, 32395–32400.

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