SERS-based microdevices for use as in vitro diagnostic biosensors

Abstract

Advances in surface-enhanced Raman scattering (SERS) detection have helped to overcome the limitations of traditional in vitro diagnostic methods, such as fluorescence and chemiluminescence, owing to its high sensitivity and multiplex detection capability. However, for the implementation of SERS detection technology in disease diagnosis, a SERS-based assay platform capable of analyzing clinical samples is essential. Moreover, infectious diseases like COVID-19 require the development of point-of-care (POC) diagnostic technologies that can rapidly and accurately determine infection status. As an effective assay platform, SERS-based bioassays utilize SERS nanotags labeled with protein or DNA receptors on Au or Ag nanoparticles, serving as highly sensitive optical probes. Additionally, a microdevice is necessary as an interface between the target biomolecules and SERS nanotags. This review aims to introduce various microdevices developed for SERS detection, available for POC diagnostics, including LFA strips, microfluidic chips, and microarray chips. Furthermore, the article presents research findings reported in the last 20 years for the SERS-based bioassay of various diseases, such as cancer, cardiovascular diseases, and infectious diseases. Finally, the prospects of SERS bioassays are discussed concerning the integration of SERS-based microdevices and portable Raman readers into POC systems, along with the utilization of artificial intelligence technology.

---

Sungwoon Lee

Sungwoon Lee earned his PhD in Chemistry from Chung-Ang University in 2021. He is currently working as a post-doctoral research associate at the same university. His ongoing research revolves around advancing a highly sensitive plasmonic-based platform for bio-diagnosis. He is keenly interested in crafting manageable plasmonic substrates without the need for machining processes and fine-tuning Raman spectroscopy to optimize spectroscopic capabilities.

Lingxin Chen

Lingxin Chen is a professor at the Yantai Institute of Coastal Zone Research, Chinese Academy of Sciences. His ongoing research interests encompass pollutant identification, analysis/monitoring technologies, and the advancement of nanoscale analytical methods utilizing innovative materials like molecularly imprinted polymers. As a corresponding author, he has authored over 400 peer-reviewed articles in esteemed journals, including Nature Sustainability, Nature Communications, Analytical Chemistry, and Environmental Science and Technology. Additionally, he achieved recognition as a Clarivate “Highly Cited Researcher” in the Cross-Field category for the years 2020, 2021, and 2022.

Sang-Woo Joo

Sang-Woo Joo is a Professor in the Department of Chemistry at Soongsil University. Presently, he serves as a Vice Dean for Graduate Studies. He majored in laser molecular spectroscopy and obtained his PhD in 1996 from the University of Chicago. His current research works are concentrated on the development of novel Raman spectroscopic platforms for medical diagnostics and environmental monitoring using functional plasmonic nanomaterials. He has authored over 250 research papers in peer-refereed journals.

Jaebum Choo

Jaebum Choo is a Distinguished Professor in the Department of Chemistry at Chung-Ang University. Currently, he is Vice President for Research and is the Director of the Center for Nanophotonics-based Biomedical Diagnostics Research Center (ERC). Since 2018, he has been an associate editor of Analyst at the Royal Society of Chemistry. He obtained his PhD in 1994 from Texas A&M University. His ongoing research programs are dedicated to developing highly sensitive nanoplasmonic sensor systems for rapid and precise in vitro diagnostics of infectious diseases. Throughout his career, he has authored over 305 research papers in peer-reviewed journals and contributed to nine book chapters.

---

1. Introduction

Biomedical diagnosis traditionally involves colorimetric, fluorescence, and electrochemi-luminescence (ECL) detection methods to analyze and quantify protein or gene biomarkers. For example, ECL methods are used in immunoassays performed in clinical laboratories for in vitro diagnostics.^1,2 Biological laboratories perform protein analysis in 96-well plates using enzyme-linked immunosorbent assay (ELISA) methods that use colorimetric or fluorescence detection.³ Since the start of the COVID-19 pandemic, fluorescence-based real-time polymerase chain reaction (RT-PCR) has been adopted as the gold standard diagnostic process.^4,5 However, the commonly used optical detection methods such as fluorescence or chemiluminescence in existing bioassays have limitations in terms of sensitivity and multiplex detection capabilities. Consequently, new detection methods with enhanced capabilities are in demand. In this regard, surface-enhanced Raman scattering (SERS) detection could be used to overcome the limitations of the conventional detection methods used in bioassays.^6,7

Electromagnetic enhancement is a well-known mechanism that relies on the interaction between incident light, the molecule, and the nanostructured surface.^8–15 It comprises two key components: localized surface plasmon resonance (LSPR) and hot spots. LSPR involves the interaction of light with metallic nanostructures such as gold (Au) or silver (Ag) to induce the collective oscillation of the surface plasmons, which are the free electrons within the metal.¹⁶ Surface plasmons oscillate at specific frequencies depending on the size, shape, and composition of the Au or Ag nanostructures. LSPR concentrates the electric field near the nanostructures, which produces an enhanced electromagnetic field around the molecule of interest.¹⁷ Consequently, this significantly amplifies the Raman scattering signal emitted from the molecule (Fig. 1). Additionally, SERS also benefits from hot spots, regions on the metallic surface exhibiting strong electromagnetic field enhancement. On the other hand, chemical enhancement is a phenomenon where charge transfer occurs from the metal surface to the analyte molecule upon forming a chemical bond between an analyte and a metal nanoparticle. This increases the polarizability of the molecule, leading to an amplification of signal intensity. However, it is generally known that electromagnetic enhancement contributes more significantly to signal amplification than chemical enhancement.^16,17 Thus, SERS-based bioassay could address the sensitivity issues in conventional optical measurement methods.¹⁸ In addition, SERS peaks are much narrower than those of fluorescence or luminescence emission bands and offer an advantage for simultaneously detecting multiple biomarkers.^19,20 Numerous studies published over the past 20 years have demonstrated this potential. This review consolidates the results of studies on SERS-based bioassays for diagnosing diseases such as cancer, cardiovascular diseases, and infectious diseases reported over the last two decades.

Fig. 1 Schematics of localized surface plasmon resonance (LSPR) contribution to the electric field and the chemical mechanism underlying surface-enhanced Raman scattering (SERS).

SERS bioassays employ highly sensitive optical probes such as Au or Ag nanoparticles coupled with receptors, serving as SERS nanotags,^21,22 a critical facet for advancing SERS detection in disease diagnostics. Therefore, assay platforms are required to facilitate efficient reactions between target molecules and SERS nanotags. Particularly, infectious diseases such as COVID-19 warrant the development of point-of-care (POC) diagnostic technologies with the ability to determine the presence of the disease on-site rapidly and accurately.^23,24 Various microdevices have been recently developed to function as SERS-based POC detection platforms, including lateral flow assay (LFA) strips,²⁵ microfluidic devices,²⁶ and microarray chips.²⁷ Additionally, researchers have created integrated systems for POC diagnosis by combining these microdevices with portable Raman readers.²⁸ This review discusses SERS-based microdevices and integrated systems that are effective for POC diagnosis of infectious diseases. It discusses how the formation of uniform nanogaps plays a crucial role in ensuring reproducibility because signal enhancement is achieved through LSPR effects in SERS detection.²⁹ This review also introduces fabrication techniques for creating uniform nanostructured substrates and methods for improving reproducibility, such as average ensemble generation and Raman mapping techniques. Finally, we provide insights into the conditions conducive to transferring SERS-based bioassay technology developed in the laboratory to clinical settings and discuss the prospects for the clinical application of SERS-based bioassays aided by artificial intelligence (AI) technology.

2. Development of platform technologies for SERS-based bioassays

The two primary approaches for the SERS detection method in quantitatively analyzing proteins or DNA/RNA in immunoassays or molecular diagnostics are label-based^30,31 and label-free.^32,33 The label-based detection approach uses optical labels such as dyes or luminol (used in fluorescence or chemiluminescence). The labels are bound to the target, and the optical signal of the labels is measured to quantify the target. In SERS-based bioassays, signal amplification is achieved using Raman reporter-labeled SERS nanotags as optical labels, which can generate strong plasmonic signals.³⁴ In contrast, the label-free detection approach directly analyzes the SERS signals emitted by the target molecules without using SERS nanotags. Then, a statistical method, such as chemometrics or machine learning, is used to analyze the data. However, the critical reason for applying SERS technology in on-site clinics is to overcome the limitations of conventional in vitro diagnostic procedures (such as fluorescence or luminescence).^35–38 Hence, the clinical data measured using existing clinical diagnostic methods must be analyzed and compared with those measured using SERS detection methods to validate this hypothesis. Therefore, this review focuses on recent research trends in label-based detection. In this section, we first explore the current developments in fabricating SERS nanotags for SERS-based bioassays and recent research trends in SERS-based assays.

2.1. SERS nanotags for bioassays

As SERS nanotags, both gold (Au) and silver (Ag) nanomaterials with unique and fascinating properties have been extensively used in bioassays.^39–51 Plasmonics is a subfield of photonics that addresses the interaction between light and metallic nanoparticles with particular emphasis on the excitation of oscillating electrons at the surface of nanoparticles. Au and Ag exhibit a resonance phenomenon known as LSPR. During LSPR, the energy from the incoming light causes the surface-free electrons to resonate and enhance the optical properties. The LSPR phenomenon has wide-ranging applications, including sensing, detection, medical imaging, and therapy. Moreover, LSPR is tunable in Au and Ag by controlling their size, shape, and environment, facilitating precise control over their optical properties. This renders them versatile tools for developing new optical and biological applications. For instance, research is underway to develop SERS-based viral infection diagnostics using Ag nanoparticles to detect viruses rapidly. Some studies have shown promising results for SERS spectroscopy involving Ag nanomaterials for detecting various biological samples. However, further research is required to assess SERS spectroscopy sensitivity and specificity for bioassays and optimize the technique for practical use in clinical settings. Additionally, 2D materials^52–58 and magnetic materials^59–74 have been introduced as efficient SERS nanotags (Fig. 2). Table 1 compares the diagnostic performances of different SERS nanotags in bioassay.

Fig. 2 Surface-enhanced Raman scattering (SERS) nanotags for bioassays. Shape controls of plasmonic nanoparticles such as disk, ellipse, sphere, cylinder, cube, star forms are depicted as well as the bioconjugation category of antibodies and aptamers.

Table 1 Comparison of diagnostic performances of different SERS in bioassays

Platform	Materials	Tag	Target/sample	LoD/sensitivity	Ref.
Substrates	CdSe	Methylene blue	Uric acid	10^−10 M	75
Substrates	Anodized aluminum oxide gold polyamide nanopillar4	Rhodamine 6G	Carbendazim	10^−14 M	76
Substrates	Gold nanopillar	Cy3-Tau protein antibody	Blood	3.21 fM	77
Substrates	Nanohole micropillar array	Rhodamine 6G BZT	E. coli biofilm	(6.8 × 10^14 molecules cm^−2)	78
Substrates	Nanocone array	Rhodamine 6G	Thiram	10 pM	79
Substrates	Metamaterial nanochannels	ROX-label aptamer	Nucleolin	71 pM	80
Substrates	Si chip with Au nanohole arrays	4-Nitrothiophenol	Click reaction SAMs	Monolayer level	81
Substrates	Au/Ag porous GaN	FAM-DNA	Breast miRNA	8.84 × 10^−10 M	82
Nanotags	MoS2@AuNSs nanoflakes	Mercaptobenzoic acid, DTNB	S. aureus	Single cell	38
Substrates	Ag coat CMOS sensor	Rhodamine 6G	Extracellular vesicle	10^−4 M	83
Substrates	AuNS-nanoporous polycarbonate membrane	Methylene blue	Lactic, uric acid in sweat	10^−13 M	84
Substrates	ZnO–Ag film	Crystal violet	Hg^2+	10^−7 M	85
Substrates	AuNS-Langmuir–Blodgett film	Crystal violet, rhodamine	Tetracycline	0.05 μg mL^−1	86
Substrates	Ag/3D-TiO2/Si	Rhodamine 6G	Rhodamine 6G	10^−10 M	87
Substrates	Ag nanograss	Chlorpyrifos	Chlorpyrifos	500 nM	88
Substrates	AgNPs anodized aluminum oxide	4-NTP	4-NTP	10^−5 M	89
Substrates	Nanopillar array	NT, SA-Cy5	Small organic molecules and proteins	5 nM	90
Substrates	Au nanocone array	Rhodamine 6G	Serum, gastric cancer	10^−10 M	91
Substrates	AuNPs Si nanorod array	Rhodamine 6G	Rhodamine 6G	10^−14 M	92
Substrates	Au nanodimple array	MGITC	PCR samples	10^5 copies per μL	93
Substrates	Au–SiO2 microelectrode array	4-Aminothiophenol	4-Aminothiophenol	0.5 nM	94
Substrates	Au nanorod array	Rhodamine 6G	PCB-77	2.4 nM	95
Substrates	Nanopillar array	Mercaptobenzoic acids, DTNB	Cytokine	2.6 aM	96
Substrates	AuNR-Si microchannel	Rhodamine 6G	Rhodamine 6G	10^−18 M	97
Substrates	PDMS-template nanoarray	4-Aminothiophenol	4-Aminothiophenol	10^−9 M	98
Substrate	Ag, Au	4-Aminothiophenol	Aflatoxin	0.48 pg mL^−1	57
Substrate	Ag- and TEA-modified core–shell nanoparticles	4-MPh, 4-MBA	E. coli	10^−10 mol L^−1	100
Nanotags	AuNPs	MPY, MBA, MMC, TFMBA	Blood, breast cancer cells	1–17 cells per mL	50
Nanotags	Ag–Au hollow nanosphere	RBITC, MGITC, DTDC	Breast cancer cell phenotype	Single cell analysis	47
Nanotags	Au@Ag	Mercaptobenzoic acid	microRNA-21	84 fM	40
Nanotags	AuNRs@Ag@4-MBA@BSA	4-Mercaptobenzoic acid	S. aureus, E. coli	Bacterial suspension samples (OD = 0.1)	99
Nanotags	GO sheet plasmonic core shell	ss-DNA	MC-LR toxin	0.635 pM	52
Nanotags	AgNPs	4-Aminothiophenol	Tyrosine phosphorylation	Single tissue cell	101
Nanotags	AuNPs	4-Mercaptobenzonitrile, ethynylbenzene	Interleukin-6, procalcitonin	0.584, 2.99 pg mL^−1	102

---

2.2. Magnetic bead-based SERS assay platforms

Currently, most clinical laboratories perform immunological analysis of the blood using chemiluminescence technology-based automated equipment manufactured by specialized companies such as Roche, Abbott, and Siemens. Three diagnostic reagents are used for the chemiluminescent analysis of specific biomarkers: an antibody set that selectively binds to specific protein biomarkers in the blood. These magnetic beads immobilize bound sandwich immunocomplexes and a luminescent agent such as luminol that activates immobilized immunocomplexes without radiation. Notably, the same magnetic bead-based method used in the chemiluminescence platform in clinical laboratories is also used in magnetic bead-based SERS immunoassay. These magnetic beads are used as capture templates, and SERS nanotags are used as detection labels for the assay.^103–117 To date, numerous research results using the magnetic bead-based SERS assay method have been reported. However, since the significant focus of this paper is on SERS-based microdevices, we will introduce just one representative example of the magnetic bead-based SERS assay.

Villa et al. used the SERS-based magnetic bead assay method to develop a highly sensitive immunoassay platform for detecting the stress hormone cortisol (Fig. 3).¹⁰³ They used Au nanostars as SERS nanotags and observed higher sensitivity during detection than when Au nanospheres or Au nanorods were used. For a comparison of assay efficiency, they simultaneously performed a magnetically assisted SERS immunoassay and SERS assay using Au-coated glass slides, as depicted in Fig. 3. They assayed urine or serum samples and found that the magnetic bead-based assay method demonstrated better performance with a limit of detection (LoD) of 7 ng mL⁻¹, which is lower than the clinical cut-off value for cortisol (10–100 ng mL⁻¹); this indicates its potential for clinical applications. Magnetic bead-based assay methods are versatile and widely used in immunoassays performed in clinical laboratories using chemiluminescence detection and SERS-based immunoassay platforms. However, to apply SERS detection to POC testing, micro devices such as LFA strips or microfluidic chips that can miniaturize the assay system must be developed with a compact Raman spectrophotometer.

Fig. 3 Description of SERSI modalities. (A) MA-SERSI using a suspension of magnetic beads coated with cortisol antibodies as the capture substrate; (B) RS-SERSI using gold-coated glass decorated with cortisol antibodies as the capture substrate. SERS nanotags comprising gold nanostars functionalized with Raman reporter molecules and cortisol-BSA conjugates that selectively bind to cortisol-specific antibodies. Reprinted with permission from ref. 103, Copyright 2020 Elsevier.

3. Development of microdevices for point-of-care SERS-based bioassays

3.1. SERS-based LFA

The paper-based LFA strip is a simple tool that quickly diagnoses the presence or absence of a specific biomarker in blood, urine, nasal mucus, and other bodily fluids at the POC.^118–120 The most commonly used colorimetric LFA strips are antibody-conjugated Au nanoparticles (AuNPs). They work on the phenomenon of AuNPs changing color from colorless to red owing to localized surface plasmon effects when AuNPs accumulate on the test line of the strip due to antibody–antigen interactions.^121–123

A commercialized LFA strip consists of four components: nitrocellulose (NC) membrane, conjugation pad, sample pad, and absorption pad. The NC membrane serves as the substrate that uses its nitrate groups to bind to the peptide bond of antibodies through electrostatic interactions. Specifically, the capture and control antibodies are immobilized to the test and control lines using an antibody dispenser. As shown in Fig. 4, when a sample solution containing target antigens is loaded into the inlet of the LFA strip, it flows towards the absorption pad owing to capillary force. The detection antibody-conjugated AuNPs in the conjugation pad bind to the target antigen to form an AuNP–antigen complex, which continues to flow up to the test line and attaches to the pre-immobilized capture antibody. The unattached antibody-conjugated AuNPs continue to flow through the NC membrane and bind to the antibodies immobilized on the control line through antibody–antibody interactions. Thus, the AuNPs are accumulated on the capture and control lines. Those that remain unattached exit the absorption pad. This accumulation produces a color change from colorless to red owing to nanoparticles' localized surface plasmon effects. Thus, in the absence of target antigens in the sample, only the control line turns red, displaying a single red line. However, in the presence of target antigens, two red lines are displayed, thus differentiating positive and negative results.^124–127

Fig. 4 Basic principles of conventional LFA (left) and SERS-LFA (right) strips.

The first commercially successful LFA strips was produced in 1976 for pregnancy test kits to detect the human chorionic gonadotropin (hCG) hormone, which is excreted in urine when pregnant.^128,129 More recently, COVID-19 LFA strips have been developed to rapidly detect the nucleocapsid protein expressed within the coronavirus, enabling its widespread use for on-site diagnosis of SARS-CoV-2 infection worldwide.^130–132 Despite the successful use of colorimetric LFA strips for on-site diagnosis of COVID-19, the increase in the rate of false-negative diagnosis at low viral concentrations, such as during initial or asymptomatic infections, became an issue as sensitivity dropped by <50%. According to a US FDA analysis, the false-negative rate increases by 13% for every 10-fold increase in the LoD. Therefore, significantly enhancing immunodiagnostic sensitivity is imperative to curtail the spread of SARS-CoV-2.^133–135 Thus, many researchers have directed their efforts toward augmenting the sensitivity of colorimetric LFA strips.

Several technologies have been developed to bolster the detection sensitivity of analytes well beyond the mere visualization of AuNP accumulation-induced color change in the test line of colorimetric LFA strips. The most widely used method is using portable fluorescence readers to measure the optical signal of LFA strips using fluorescence dyes, microspheres, or quantum dots.^136–138 However, this fluorescence measurement method has limitations in substantially improving the detection sensitivity of colorimetric LFA. In contrast, SERS detection technology has garnered attention as a highly sensitive method that can overcome biomarkers' low detection sensitivity limit in immunoassays. Exposing SERS nanotags to a laser beam significantly enhances the incident electromagnetic field at active sites owing to localized surface plasmon effects. These active sites are referred to as “hot spots.” This electromagnetic enhancement addresses the low-sensitivity issue inherent in colorimetric or fluorescence detection.^139,140 Consequently, the SERS-LFA platform has recently found widespread application in infectious disease diagnosis. Furthermore, similar to fluorescence LFAs, SERS-LFA strips use the same AuNPs, but unlike fluorescence LFAs, they are discernible with the naked eye. Thus, samples with high viral load can be visually identified, and those with low viral load can be detected by monitoring the characteristic Raman peak intensity of the highly sensitive SERS tags. Moreover, various portable Raman systems have been developed recently,^141,142 which when combined with SERS-LFA strips, enable the implementation of SERS-based LFA technology for on-site diagnosis of infectious viruses or bacteria with high sensitivity.¹⁴³

Fig. 4 compares the basic principles of conventional LFA and SERS-LFA strips. As seen in Fig. 4, using Raman reporter-labeled SERS nanotags enables one to observe color changes with the naked eye, like that of conventional colorimetric LFA strips. Additionally, measuring the SERS signal at the test and control lines enables the quantitative analysis of the enhanced SERS signal in the hot spots, resulting in high-sensitivity quantification. This electromagnetic enhancement mitigates the sensitivity problems inherent in fluorescence detection. Thus, combining the sensitive detection capability of SERS with the rapid and user-friendly sensing platform of LFA strips enables the detection of target biomolecules with high sensitivity in the field. Recently published study results confirmed the high-sensitivity of detection observed while using these SERS-LFA strips for various infectious viruses,¹⁴⁴ bacteria,^145,146 and pathogens.^147,148

Lee et al. developed a SERS-LFA strip to diagnose scrub typhus caused by the intracellular bacterium Orientia tsutsugamushi (O. tsutsugamushi)as shown in Fig. 5.¹⁴⁹ Currently, the standard diagnostic method for scrub typhus is immunofluorescence assay (IFA), in which infection is determined based on comparing fluorescence images of O. tsutsugamushi-generated target IgG and IgM antibody titers that are obtained by diluting the standard titer by half and observing the images with a fluorescence microscope at each concentration. Given some of the major drawbacks of performing an IFA test, an expensive space-occupying fluorescence microscope and subjective endpoint determination are needed, as accurate quantification is difficult to obtain. In contrast, the SERS-LFA strip enables objective quantification; the portable Raman reader accurately and rapidly determines the IgG and IgM titer values in the field. A comparison of the IFA assay results of 40 clinical samples with those obtained from SERS-LFA showed good concordance between the two. However, the quantitative analysis of IgG and IgM titers was possible only when the SERS-LFA method was used.

Fig. 5 Schematic of the SERS-LFA process for quantitative analysis. (A) Detection of O. tsutsugamushi-specific immunoglobulin G antibodies lines in human serum using sandwich immunocomplex. (B) Fabrication of SERS-LFA strips. ((A)–(E)). (C) Optical images of LFA strips after loading O. tsutsugamushi-specific IgG antibodies at different titer concentrations. (D) Raman mapping results for the test and control lines were in 2800 × 300 μm and 100 × 100 μm, respectively. In total, 84 pixels were imaged at 100 μm intervals in 42 s. The scale bar on the right shows the Raman intensity color code. (E) Average SERS spectra of 84 pixel points for different titer concentrations of O. tsutsugamushi-specific IgG antibodies on the test and control lines. (F) Corresponding standard calibration curve for different antibody titer concentrations. The y-axis represents the Raman intensity ratio between the test and control lines at 1616 cm⁻¹. The error bars indicate the standard deviations for three measurements. The cutoff titer value (256) and LoD (49.5) are marked on the calibration curve. Reprinted with permission from ref. 149, Copyright 2019 American Chemical Society.

Additionally, the SERS-LFA strip is used for the high-sensitivity diagnosis of genetic markers such as DNA and RNA.^25,150 RT-PCR is currently used as the standard diagnostic method for detecting viral or bacterial target genes. However, it requires essential preprocessing steps and an amplification process using primers, which is time consuming (3–4 h) and challenging for on-site diagnosis. Moreover, RT-PCR has demonstrated limited sensitivity in terms of fluorescence detection, which necessitates the amplification of the target DNA using primer DNAs with thermocyclic steps before detection. Therefore, a highly sensitive detection method based on SERS detection can mitigate these issues.

The immunodeficiency virus type 1 (HIV-1) causes disease by infecting and destroying the human immune system. Fu et al. developed a SERS-LFA strip with high sensitivity towards HIV-1 DNA.¹⁵¹ Instead of a capture antibody, the test line on this strip contains immobilized capture DNA capable of binding to the target HIV-1 DNA through hybridization. The control line contains immobilized control DNA capable of directly binding to the detection DNA bound to AuNP surfaces. DNA hybridization assays performed using this SERS-LFA strip showed a strong linear relationship between SERS nanotag Raman peak intensity and target DNA concentration, enabling the measurement at LoD as low as 0.24 pg mL⁻¹. Wang et al. have developed a SERS-LFA strip capable of simultaneously detecting two target DNAs associated with Kaposi's sarcoma herpes virus (KSHV) and bacillary angiomatosis (BA) as shown in Fig. 6.¹⁵² They immobilized KSHV and BA capture DNAs on two separate test lines and used SERS nanotags labeled with two different Raman reporters to detect both target DNAs with high sensitivity. The detectable concentrations for KSHV and BA target DNAs were 0.043 and 0.074 pM, respectively, demonstrating an approximate 10 000-fold increase in sensitivity compared with colorimetric LFA.

Fig. 6 (A) Schematic illustration of LFA biosensor for simultaneous detection of two nucleic acids. The strip comprises two test lines and one control line. (B) (i) Kaposi's sarcoma herpes virus (KSHV) DNA–AuNP complexes were captured by the probe KSHV DNAs on the first test line; (ii) bacillary angiomatosis (BA) DNA–AuNP complexes were captured by the probe BA DNAs on the second test line; and (iii) control DNAs captured excess KSHV and BA detection DNAs attached to AuNPs through T20–A20 hybridization on the (third) control line. (C) DNA hybridizations corresponding to two test lines (i) and (ii) and one control line (iii). (D) Digital photographic images. (E) SERS spectra of the SERS-based LFA biosensor in the presence of (i) KSHV, 0 pM; BA, 0 pM; (ii) KSHV, 100 pM; BA, 0 pM; (iii) KSHV, 0 pM; BA, 100 pM; and (iv) KSHV, 100 pM; BA, 100 pM. Assay time: 20 min. Reprinted with permission from ref. 152, Copyright 2017 American Chemical Society.

Kim et al. developed a dual-flow SERS-LFA for detecting thyroid-stimulating hormone (TSH) with >10 times enhanced sensitivity compared to single-flow SERS-LFA.¹⁵³Fig. 7 illustrates the proposed dual-flow SERS-LFA operating principle and assay results. On sample loading, 25 nm SERS nanotags flow through a short pathway and are immobilized on the test line, followed by the sequential flow of 45 nm AuNPs through another long path and accumulation on the test line. The sequential flow of the two AuNP colloidal solutions creates additional hotspots between the 25 and 45 nm AuNPs at the test line. This dual-flow SERS-LFA strip measured TSH concentration with high sensitivity at a LoD of 0.15 μIU mL⁻¹, significantly surpassing the detection limits of single-flow LFA strips and ELISA.

Fig. 7 (A) Working principle of a SERS-based dual-flow sensor using (i) 25 and (ii) 45 nm SERS nanotags. (B) thyroid-stimulating hormone (TSH) assays using the dual-flow sensor. When the sliding top panel slides aside from the bottom panel, two fluids #1 and #2, start flowing through the inlets owing to capillary force toward the waste reservoir. (C) Sandwich immunocomplexes are formed using (i) 25 nm SERS nanotags only, and (ii) both 25 and 45 nm SERS nanotags are immobilized on the test zone. (D)–(G) Average Raman spectra corresponding to the 72 pixel points at different TSH concentrations over a 0–40 μIU mL⁻¹ range in the (D) single-flow and (E) dual-flow sensors. (F) Comparison of calibration curves corresponding to the single-flow (black) and dual-flow (red) sensors. The error bars designate the standard deviations of 72 pixels in the Raman mapping zone. (G) Comparison of the TSH assay results measured with the single-flow (black) and dual-flow (red) sensors in the low concentration range (0–4.0 μIU mL⁻¹). The error bars designate standard deviations from three different measurements. Reprinted with permission from ref. 153, Copyright 2021 American Chemical Society.

New SERS nanotags have been designed to enhance SERS-LFA sensitivity further. Liu et al. developed a core–shell multifunctional nano-tracer that enables precise particle size control for the new SERS nanotags (Fig. 8).¹⁵⁴ They synthesized cyanide (CN)-bridged coordination polymer that exhibits characteristic Raman peaks in the silent region (1800–2800 cm⁻¹) to prevent spectral overlap between the Raman reporter molecule and target protein. They also used 100 nm-sized AuNPs to enhance plasmonic coupling efficiency and antibody binding affinity.

Fig. 8 (A) Schematic illustration of engineered multi-functional core–shell APNPs. (B) Affinity analysis of AuNPs, L-AuNPs, and APNPs. (C) Dual-response mediated ECCRD assay using APNPs-LFIA. Reprinted with permission from ref. 154, Copyright 2022 John Wiley and Sons.

To enhance the sensitivity of the SERS-LFA strip, Huang et al. used nanomaterials composed of three metals (Fig. 9)¹⁵⁵ to synthesize urchin-shaped Au–Ag@Pt nanoparticles (UAA@Pt NPs), which were used as SERS nanotags. Using these nanotags, they successfully and simultaneously detected three types of bacteria (E. coli, S. aureus, and P. aeruginosa) in blood. The synthesized UAA@Pt NPs demonstrated their utility both for SERS-LFA as optical probes and for colorimetric (CM), photothermal (PT), and catalytic (CL) LFA strips. The measured LoDs were 3 CFU mL⁻¹ for SERS-LFA, 27 CFU mL⁻¹ for PT-LFA, and 18 CFU mL⁻¹ for CL-LFA during detecting S. aureus as the target bacteria. These values are approximately 330-fold, 37-fold, and 55-fold, respectively, more sensitive than those obtained with CL-LFA strip usage.

Fig. 9 Schematic Illustration of (A) synthesis of multifunctional UAA@P/M; (B) principle of the UAA@P/M-Integrated LFIA for multiple bacterial discrimination; (C) procedures for UAA@P/M-Integrated LFIA assay for Multimodal Bacterial Detection. Reprinted with permission from ref. 155, Copyright 2023 American Chemical Society.

3.2. SERS-based microfluidics

The recent COVID-19 pandemic has created an urgent need for technology that can accurately and rapidly detect pathogenic viruses or bacteria in the field. SERS-based microfluidics, which is the fusion of microfluidics with SERS detection, enables precise on-site determination of viral or bacterial infections. It has garnered significant interest from researchers primarily owing to its high potential for its application in POC diagnosis.^156–158 Despite continued efforts of researchers to implement high-sensitivity SERS detection techniques for the quantitative analysis of biomolecules such as proteins and genes, limitations in SERS signal reproducibility have made accurate quantification challenging. This can be attributed to the reliance on localized surface plasmon effects in nanogaps on the aggregation of plasmonic nanoparticles or nanostructure-patterned substrates, raising the issue of non-uniform nanogaps.^159,160 To mitigate this reproducibility issue, a microfluidic device for SERS-based assays may be used. That is, reproducible and sensitive detection is achieved by harnessing the average ensemble effects of SERS nanotags that rapidly move within microfluidic channels.^161,162 Thus, SERS detection techniques enable high-sensitivity detection, whereas microfluidic devices enable reproducible biomolecule detection.^163–165 Another advance of using microfluidic devices is their ability to analyze small sample volumes, which provides the additional benefit of facilitating automated assay processes at rapid detection times.

Immunoassay is a widely used biochemical analysis technique for quantitatively analyzing substances such as proteins, cells, viruses, and bacteria. Laboratories commonly use absorption or fluorescence-based enzyme-linked immunosorbent assay (ELISA). This method involves serial dilution of a stock solution into each well of a 96-well plate, followed by the formation of immunocomplexes driven by antibody–antigen interactions, which are quantified as a change in absorbance through enzyme catalysis or fluorescence signal using dyes. Conventional ELISA requires tedious manual dilution with repetitive pipetting and continuous washing steps. It also involves longer assay times and necessitates a significant sample volume for analysis.^166,167 Various SERS-based gradient microfluidic devices have been developed to address these issues by integrating gradient microfluidic channels that enable automatic and accurate concentration gradients with highly sensitive SERS detection technology.

Chon et al. have developed an on-chip assay device that enables automatic SERS immunoassays. They used magnetic bead-Au hollow SERS nanotags created by embedding a mini solenoid apparatus into a gradient microfluidic channel.¹⁶⁸ Lee et al. developed a programmable and fully automatic Au array-embedded gradient microfluidic channel.¹⁶⁹ This microfluidic chip allows precise control of the serial dilution of the target antigens using a two-fold logarithmic gradient in the volumetric mixing ratio of the two merging solutions. It demonstrated high-sensitivity by measuring the SERS signal of fixed sandwich immunocomplexes within Au-patterned microarray wells embedded in the channel. Notably, all experimental conditions about sample serial dilution, immunoreaction, and SERS detection can be automatically controlled within the microfluidic channel, which reduces the assay time to ≤60 min. This programmable gradient microfluidic device is applicable for multiplex DNA detection. Choi et al. developed a SERS-based gradient microfluidic channel to measure various duplex DNA oligomer mixture concentrations simultaneously (Fig. 10).¹⁷⁰ They created a mathematical model based on electric circuit theory to generate multiple concentration gradients to obtain an accurate gradient concentration. Using this model, they fabricated gradient microfluidic channels for DNA detection. In this study, spermine tetrahydrochloride-coated AgNPs were used as SERS nanotags, and two breast cancer-related target genes were quantitatively analyzed using on-chip SERS detection. During analysis, hot spots were formed by electrostatic interactions between AgNPs and the DNA oligonucleotide mixtures flowing through the channel.

Fig. 10 Arrangement of the micro-network gradient channel based on SERS for concurrent quantification of two mixtures of DNA oligomers. (A) Upper layer: A PDMS panel for producing different concentrations of DNA 1 and for the even distribution of Ag nanoparticles; middle layer: a PDMS panel for creating various concentrations of DNA 2, along with grooved channels for effective mixing; lower layer: slide glass used for PDMS adhesion. (B) Three-layer integrated micro-network gradient channel, and (C) Image of the channel filled with various ink colors (blue, red, and yellow) injected via the three entry points. Reprinted with permission from ref. 170, Copyright 2012 Royal Society of Chemistry.

Microfluidic chips are classified into continuous flow-based and droplet-based microfluidic devices. The introduction of SERS detection into continuous flow-based microfluidic devices induces the “memory effect,” which causes the aggregation of the Au or Ag nanoparticles on the channel surface, significantly reducing sensitivity and reproducibility.^171,172 Droplet-based microfluidic devices use a two-phase liquid/liquid segmented flow system to overcome these issues. In this droplet-based microfluidic system, nanoliter-sized liquid droplets move rapidly within an immiscible carrier fluid, which eliminates the memory effect observed in continuous flow-based microfluidic devices.^173,174 Furthermore, the droplets can be fused, divided, isolated, sorted, and incubated, which enables complex biological processes to be controlled in a high-throughput manner.^175–177 Additionally, measuring the SERS signal of multiple rapidly moving droplets significantly enhances reproducibility owing to “average ensemble effects.” Choi et al. developed a fully automated droplet-based microfluidic platform that performs wash-free immunoassays (Fig. 11A).¹⁷⁸ This system comprises droplet generation, transport, mixing, merging, and splitting modules and has been designed to automate the immunoassay reaction between magnetic beads and Au SERS nanotags through a sequential process (Fig. 11B). Additionally, it enables wash-free immunoassays in the final step (Fig. 11C). They used this multifunctional microdroplet system to detect F1 antigen in Yersinia pestis at a low LoD of 59.6 pg mL⁻¹, sample consumption of <100 μL, and assay time of <10 min. Jeon et al. integrated the concepts of the gradient channel and droplet microfluidics to develop a SERS-based microdroplet device for high-throughput gradient analysis (Fig. 11D).¹⁷⁹ They used a microfluidic gradient generator to serially dilute the target reagent before injecting an oil mixture into the channel to generate multiple droplets in each gradient channel (Fig. 11E). Subsequently, the target reagent and receptor-labeled AuNPs were allowed to react within the droplets, and the SERS signal was measured with high sensitivity. The proposed droplet gradient chip consists of two layers: the first panel for gradient dilution of the reagent and the second for SERS nanotag distribution and droplet generation. Thus, this SERS-based droplet gradient microfluidic device enables high analytical throughput and full assay automation while reducing the sample volume required for analysis to a few hundred μL.

Fig. 11 (A) Photographic images of the integrated microdroplet channel filled with red ink. Photographic images of the (B) droplet merging and (C) droplet splitting compartments. These images were captured using a high-speed camera installed in the microscope. Reprinted with permission from ref. 178, Copyright 2017 American Chemical Society. (D) The system consists of two parallel layers: (i) target loading and serial dilution occur in the top panel, (ii) nanoparticle distribution, droplet generation, and SERS detection occur in the middle panel. (E) Image (left) and photo (right) of the middle layer showing the droplet generation and SERS detection. Reprinted with permission from ref. 179, Copyright 2019 Royal Society of Chemistry.

Morelli et al. developed an automated centrifugal lab-on-disk platform capable of simultaneously performing sample treatment and SERS detection (Fig. 12).¹⁸⁰ The lab-on-disk chip harnesses centrifugal force to distribute each reagent through microfluidic channels, eliminating the need for a micro syringe pump. Its circular design enables the creation of multiple tracks over a wide area, which is advantageous for multiplex bioassays.^181,182 The microfluidic channels were fabricated through injection molding using polypropylene as the device material instead of PDMS to protect the channels from deformation by harsh chemicals. Additionally, they used ultrasonic welding to bond the individual plates. They used the SERS-based lab-on-disk chip to perform sequential steps, including filtration, liquid–liquid extraction, and SERS detection to analyze secondary bacterial metabolites secreted by E. coli in the supernatant solution.

Fig. 12 (A) Lab-on-disc for LLE extraction and detection of bacterial metabolites. The LoD device comprises 12 modules on a PMMA disc. (B) Expanded view of the assay module with the filtration part (1–6) containing a cellulose acetate membrane (5) and the assay part (7–9) embedded with SERS chip (9). (C) Expanded view of the calibration module with SERS chip. (D) Fluidic design of the calibration module comprising DCM loading (1), intermediate (2), and sensing chamber (3) with (E) a SERS chip immobilized with PSA tape. (F) Microfluidic design of the assay module comprising loading (1), filtration (2), metering chambers (3), and a hydrophilic siphon (4). The HCl loading chamber (5) is connected to the mixing chamber (6), which is attached to a serpentine siphon (7) that enables efficient mixing. The DCM loading chamber (8) is connected to the serpentine siphon through the extraction chamber (9), which directly communicates with the detection chamber (10). (G) Schematics of the filtration chamber, showing the integration of the cellulose acetate membrane with the PSA layers and the flow direction. (H) Representation of energy directors at the edge of a microfluidic chamber. (I) Schematic representation of the working principle of ultrasonic welding along with the sketch of energy directors before (i) and after (ii) welding. Reprinted with permission from ref. 180, Copyright 2018 Royal Society of Chemistry.

Some researchers have developed SERS-based devices that integrate digital microfluidic (DMF) technology. Microdroplets are controlled using an electric field on Au electrodes instead of polymer microfluidic channels and syringe pumps.^183,184 Wang et al. developed a SERS-based DMF device to quantitatively analyze avian influenza virus H5N1.¹⁸⁵ They exposed the microdroplets that formed on a hydrophobic electrode an surface to an electric field to transport, merge, and disperse these droplets during the bioassay. This approach used capture antibody-immobilized magnetic beads and Raman reporter-labeled SERS nanotags as solid support and detection nanoprobes, respectively. The target H5N1 virus was quantified in <1 h after immunoreactions and subsequent SERS detection. Das et al. have developed a novel SERS-based device that captures a small portion of the analyte droplets onto a stationary SERS substrate using microspray holes, followed by SERS detection (Fig. 13).¹⁸⁶ This proposed DMF micro-spray hole chip system offers the advantage of performing SERS detection without additional colloidal SERS nanotags. Additionally, precise control is exerted on sample droplets through electrostatic spray generation.

Fig. 13 Scheme for the ESTAS-SERS procedure in a digital microfluidic chip with a built-in microspray hole. The initial materials are combined on the chip and moved beneath the μSH. Initially, the reaction solution is prepared at varying time intervals using an electrostatic spray (ESTAS) to deposit the analyte on the SERS substrate. Subsequently, the SERS substrate with the dried analytes positioned in the beam path of a Raman microscope for SERS detection. Simultaneously, a fresh sample actuated beneath the μSH. Reprinted with permission from ref. 186, Copyright 2023 American Chemical Society.

One widely used method for quantitatively analyzing the target antigen in SERS-based assays on a microfluidic chip involves using magnetic beads as capturing agents and Au SERS nanotags as detection probes. This approach forms a sandwich immunocomplex containing the target antigen, followed by magnetic separation to remove unreacted SERS nanotags and measure the SERS signal.^34,187 Efficient liquid mixing and bio-separation techniques are essential for rapid and sensitive bioanalysis. Xiong et al. have developed a SERS-based microfluidic channel that uses magnetic nano-chains to promote reactions and perform effective bio separation (Fig. 14).¹⁸⁸ This chip does not require a complex channel structure to expedite immunoreactions because the magnetic nano-chains efficiently handle the mixing performance of reagents, promoting rapid progress in a short time duration. Thus, the magnetic nanochain-integrated microfluidic chip enables the detection of cancer protein biomarkers or bacterial species in as little as 8 min using as low as 1 μL of body fluid.

Fig. 14 Development of the magnetic nanochain-integrated microfluidic chip (MiChip). (A) Schematic representation illustrating the MiChip assay platform. (B) and (C) Images displaying the MiChip: a single channel unit (B) and arrays of multichannels (C). The microchannel filled with a red dye to enhance visibility. The scale bar represents 0.5 cm. (D) The MiChip assay design for biomarker detection depicted. The sample, magnetic nanochains conjugated with antibodies (Magchain), and SERS-encoded probes (SERS probe) combined in the mixing chamber. The antibodies on magchains and the SERS probes recognize the targets in the sample, forming sandwich immune complexes. These complexes isolated into the detection chamber for Raman spectroscopic detection. (E) An SEM image of the magnetic nanochains shown. The scale bar represents 20 μm. The inset shows a TEM image of a magnetic nanochain with a scale bar of 200 nm. (F) The SERS spectra of six representative SERS-encoded AuNRs presented, from bottom to top: 4-nitrothiophenol (NTP), 4-bromothiophenol (BTP), 2,3,5,6-tetrafluorothiophenol (TFTP), 3,5-difluorothiophenol (DFTP), 2,4-dichlorothiophenol (DCTP), and 4-methoxy-α-toluenethiol (MATT). (G) The UV-vis spectra of the original AuNRs, SERS-encoded AuNR, and antibody-conjugated SERS probes displayed. The inset shows a TEM image of the AuNRs. Scale bar: 100 nm. Reprinted with permission from ref. 188, Copyright 2018 Macmillan Publishing Ltd.

3.3. SERS-based microarrays

SERS-based microarray chips are an active research topic as they combine microarray technology used in conventional DNA or protein chips with highly sensitive SERS detection. These SERS-based microarrays enable high throughput screening of biotargets that are difficult to implement in LFAs or microfluidic chips.^189–191 Fabrication techniques for nanostructured substrates must be capable of forming reproducible nanogaps of <10 nm to create SERS-based microarray chips. Two widely used approaches for creating nanostructured microarray wells are the top-down approach, which uses lithographic techniques commonly used in semiconductor processes,^192–194 and the bottom-up approach, which harnesses the self-assembly of nanoparticles.^195–197 Three-dimensional nanoplasmonic substrates produced using methods like E-beam or photolithography offer the advantage of rapid large-area fabrication and precise control over nanogap distances. However, fabricating substrates with <20 nm nanogaps is complex and costly. In contrast, the bottom-up method using the self-assembly of Au or Ag nanoparticles enables easier fabrication of <10 nm nanogaps at a lower cost but presents challenges in controlling the uniformity of nanogap sizes, which impacts the reproducibility of the bioassays. To overcome these limitations, hybrid substrates have recently been developed; these incorporate nanoparticles and nanostructured substrates and are applicable in bioassays. In this section, we elaborate on representative top-down solid substrates, bottom-up self-assembly substrates, and recently developed hybrid substrate fabrication methods that combine both approaches.

Fig. 15 shows top-down photolithographic substrates that use different SERS platforms, such as plasmonic nanorods, nanocone, and nanoarray hybrid structures that have been introduced for signal enhancement.^198–230 3D bottom-up self-assembly nanostructures are described owing to facile preparation and cost-effectiveness.^231–251 Combinatory detection is used for synergetic enhancements.^252–266

Fig. 15 SERS substrates for bioassays categorized by top-down, bottom-up, and combinatory methods. Human-made lithographic methods are illustrated at the top. Representative bioassay substrates are depicted in the middle. Self-assembly approaches inspired by nature, utilizing hydrophobic and electrostatic interactions, are presented for the biological target samples. A combinatory approach involving lithography and self-assembly is currently introduced to achieve synergistic effects.

3.3.1. Bioassays using solid nanoplasmonic substrates using top-down approaches. Conventional electron beam lithography faces the limitation in fabricating nanopatterns with <10 nm gaps owing to the diffraction limit caused by the laser wavelength (Fig. 16). Hence, Liu et al. used holographic lithographic technology to fabricate SERS substrates with nanohole arrays and optical cavities aligned in three dimensions.²⁶⁷ This hybrid plasmonic and optical cavity (HPOC) nanostructure can considerably amplify the electromagnetic field and SERS signal intensity through effective coupling between the optical cavity and SPR of the plasmonic nanohole array. Fig. 17 shows the HPOC structure, which forms nanogaps between the photoresist pillar (blue) apex, Au caps, Au film, and Au nanohole arrays. SERS substrates based on HPOC could be used effectively for various biological assays.

Fig. 16 Scales for photolithography and dimensions of biological samples. UV, electron-beam, nano-imprint, and scanning probe lithographic techniques are introduced in the top figures, in comparison with techniques spanning the electromagnetic spectral regions from infrared to hard X-ray, as shown in the middle. Dimensions of biological samples, including genes, viruses, ribosomes, protein antibodies, molecules, and atoms, are also illustrated at the bottom.

Fig. 17 Fabrication of the 3D hybrid plasmonic and optical cavity (HPOC) structures. (A) A schematic of the geometry and (B) the fabrication process of the HPOC structures. (C) and (D) The top-view SEM images of the photoresist template and the HPOC structure, respectively. (E) and (F) The side-view SEM images of the HPOC with cavity depths of 270 and 480 nm, respectively. Reprinted with permission from ref. 267, Copyright 2018 Wiley-VCH Verlag GmbH & Co.

Chen et al. fabricated a 3D nano popcorn plasmonic substrate for highly sensitive influenza virus and COVID-19 assays.²⁶⁸ As shown in Fig. 18, they created a polymer nanodimple substrate by treating polyethylene naphthalate (PEN) polymer film with O₂ ion beams, on which a 100 nm thick Au layer was deposited using thermal evaporation. Next, they reduced the surface energy of the Au/PEN substrate by treating it with 97% perfluorodecanethiol (PFDT), which formed a PFDT self-assembled monolayer on the surface.^269–271 Subsequently, the continued evaporation of Au caused Au nanoparticles (AuNPs) to deposit uniformly on the heads of the dimples owing to the difference in surface energy between Au and PFDT, resulting in a popcorn-like arrangement of AuNPs with uniform hot spots. Using this Au nano popcorn substrate, Chen et al. developed a highly sensitive SERS-based immunoassay platform (Fig. 19) for detecting influenza virus A²⁶⁸ and SARS-CoV-2.²⁷⁰

Fig. 18 Evaluation of nano-dimple and nano-popcorn structures. (A) Sequential process for 3D nano-popcorn plasmonic substrate fabrication including two gold coating steps: the 100-nm-thick gold single-layer nano-dimple structures (i) and AuNP-deposited nano-popcorn structures (ii). Bottom: SEM images of nano-dimple (i) and nano-popcorn (ii) structures. (B) Raman mapping images of 10⁻⁶ M MGITC at 1615 cm⁻¹ for the nano-popcorn (yellow, top) and nano-dimple (dark red, bottom) substrates. Scanned 2 × 2 μm steps over a 50 (x-axis) by 50 μm (y-axis) range for a total of 625 pixels. The scale bar on the right shows the color coding used to depict Raman intensity. (C) Average Raman spectra of 625 pixels for nano-popcorn and nano-dimple substrates. Reprinted with permission from ref. 268, Copyright 2020 Elsevier.

Fig. 19 (A) Schematic illustration of the SERS imaging-based assay using a 3D nano-popcorn plasmonic aptasensor for the quantitative evaluation of A/H1N1 virus. (B) Cy3-labeled aptamer probes are hybridized with capture DNAs on the nano-popcorn substrate (i) creating a strong Raman signal (ii). (C) The recognition of A/H1N1 virus induces a conformational aptamer change (i), leading to decreased Raman signal intensities (ii). Reprinted with permission from ref. 268, Copyright 2020 Elsevier.

Similarly, Park et al. used the difference in surface energy between PFDT and Au to fabricate a high aspect ratio Au nanopillar substrate²⁶⁹ using thermal evaporation (Fig. 20). The Au nanopillar substrate displayed a soft property unlike the nanopillars produced using the sputtering method. Furthermore, the evaporation of water molecules on the surface causes two or three nanopillars to lean toward each other and form extremely small nanogaps owing to surface tension. This phenomenon enhances the coupling of local plasmonic fields and increases the SERS signal. Wang et al. used this nanopillar substrate to develop a Raman imaging-based microarray chip capable of simultaneously detecting three different mycotoxins (ochratoxin A, fumonisin B, and aflatoxin B1) as shown in Fig. 21.²⁷²

Fig. 20 (A) Schematic illustration of Au/PET nanopillar array fabrication and the capillary force-induced self-clustering of the plasmonic nanopillar array. The red area indicates a SERS hot spot where large local field enhancement has occurred. SEM images of (B) the upright as-prepared plasmonic nanopillar array, and (C) the self-clustered plasmonic nanopillar array after water evaporation. The dotted circle highlights a cluster of size (NC) 8. Reprinted with permission from ref. 269, Copyright 2019 Wiley-VCH Verlag GmbH & Co.

Fig. 21 (A) Digital photograph of 7 × 7 microarray wells, and schematic illustration of the competitive immunoassays of OTA, FUMB, and AFB1 on 3D plasmonic substrates. Three lines were used for the multiplex immunoassay of three mycotoxins. SERS mapping and SEM images of SERS nanotags captured using the 3D plasmonic nanopillars on the 3D plasmonic substrates (B) without OTA and (C) with 10⁶ pg mL⁻¹ OTA. Red circles indicate the bounding areas of SERS nanotags with 3D plasmonic nanopillars. Numerical simulation of the electric field distribution over the SERS nanotag-anchoring antibody captured on the leaning Au nanopillars with a gap size of (D) 6.1 nm and (E) 20.0 nm. A linearly polarized 632.8 nm plane wave illumination beam was directed onto the plasmonic nanostructures with polarization along the dimer axis. The distance between Au nanopillar and antibody-conjugated SERS nanotags was set to 5.2 nm, which was determined based on the SEM images and DLS distributions. Reprinted with permission from ref. 272, Copyright 2018 Royal Society of Chemistry.

Dang et al.²⁷³ and Nam et al.²⁷⁴ developed a 3D nanoplasmonic assay platform to perform highly sensitive and reproducible DNA assays (Fig. 22). They used Au nanopore substrates to arrange AuNPs uniformly. This platform uses DNA hybridization to distribute 80 nm-sized AuNPs evenly into the cavities of the Au dimple substrate. In this process, high-density volumetric hotspots are formed between the curved holes of the Au dimple substrate and the AuNPs, which enables the implementation of highly sensitive bioassays (Fig. 23).²⁷³ Yu et al. used a combination of AuNPs synthesized through a bottom-up method and Au nanodimple substrates created through a top-down approach to develop a molecular diagnostic technology in a SERS-based PCR platform to detect the SARS-CoV-2 target gene rapidly (Fig. 24).⁹³ When tested with the same concentration of SARS-CoV-2 target genes (E and RdRp at 1.00 × 10⁵ copies per μL), the results showed that the current fluorescence-based RT-PCR method required 25 thermocycles to amplify and detect the target gene at a detection concentration of 3.36 × 10¹² copies per μL. In contrast, SERS-PCR detected the target gene after only 8 cycles at a concentration of 2.56 × 10⁷ copies per μL, indicating its potential for significantly reducing diagnosis time (Fig. 24).

Fig. 22 Simplified illustration of the internalization of AuNPs and AgNPs into AAO nanopores using ultrasonication process. Scale bars are 100 nm. Reprinted with permission from ref. 274, Copyright 2019 Wiley-VCH Verlag GmbH & Co.

Fig. 23 SEM images and schematics of AuNP-internalized nanodimpled-Au substrates fabricated using (A) electrostatic interactions and (B) DNA hybridization. (i) Top-view and (ii) cross-sectional SEM images (scale bars: 200 nm). (iii) Schematics of the binding interactions between AuNP- and nanodimpled Au cavity. Reprinted with permission from ref. 273, Copyright 2021 Wiley-VCH Verlag GmbH & Co.

Fig. 24 Schematic illustration of acquisition of Raman mapping image. (A) Raman mapping was performed using a 633 nm He–Ne laser (laser power = 1.45 mW). The mapping area was 250 × 250 μm, and 2500 pixels were imaged in total at 5 μm intervals (the size of one pixel was 5 × 5 μm). (B) Raman mapping images for remaining bridge DNAs before (top) and after (bottom) PCR amplification. (C) Magnified SEM images for one pixel (5 × 5 μm). The initial concentration of the target RdRp DNA was 10⁵ copies per μL, and 25 PCR cycles were run. Reprinted with permission from ref. 93, Copyright 2022 Elsevier.

3.3.2. Bioassays using self-assembled nanoparticle arrays using bottom-up approaches. The SERS substrates fabricated through a bottom-up approach, such as the aggregation or self-assembly of Au or Ag nanoparticles, have the advantages of not requiring large equipment and being cost-effective. However, controlling the occurrence of aggregation in the solid phase is challenging, and it has limitations in achieving uniform particle assembly due to phenomena such as the coffee-ring effect.^275,276 Researchers have addressed these issues by treating the plasmonic nanoparticles with various stabilizing agents and creating self-assembly at the water–oil two-phase interface.^277,278 An ordered assembly is achieved by combining various forces such as capillary force, electrostatic repulsion, van der Waals force, and solvation tension.

Ma et al. created an AuNP self-assembled array with mirror-like features at the water/cyclohexane interface.²⁷⁹ They used it as a sensitive and reproducible SERS substrate. They developed a method using this platform for the high-sensitivity quantitative analysis of synthetic amphetamines in urine, where the cyclohexane phase was the extraction solvent for extracting drug components from urine. Furthermore, it plays a dual role in inducing the self-assembly of AuNPs, as shown in Fig. 25. In contrast, Wu et al. created an AuNPs@DNA self-assembly using DNA hybridization at the same water/cyclohexane interface. They used it to detect target miRNA 155 with high sensitivity at a LoD of 1.45 fmol L⁻¹.²⁸⁰ As seen in Fig. 25, AuNPs@DNA self-assembly forms nanogaps at a denser spacing at the interface than that achieved with freely assembled AuNPs.

Fig. 25 (A) Scheme and optical images (A1) and (A4) and optical images (A2) and (A3) displaying the self-assembly of gold nanoparticle (GNP) arrays triggered by urine extract at the liquid–air boundary, employed for surface-enhanced Raman scattering (SERS) detection. (B) Microscopic images (B1)–(B4) capturing the self-assembly progression of large-scale GNP arrays at the interface. Reprinted with permission from ref. 279, Copyright 2016 Wiley-VCH Verlag GmbH & Co. (C) Scanning electron microscopy (SEM) examination provides a detailed view of the spontaneously assembled gold nanoparticle (AuNP) interfaces (i)–(iii) and a corresponding schematic (iv). (D) SEM characterization presents the interface resulting from the self-assembly of DNA-decorated AuNPs (v)–(vii) along with related schematic (viii). Reprinted with permission from ref. 280, Copyright 2021 American Chemical Society.

Liu et al. developed Au plasmonic colloidosomes (PCs) that are useful in applications such as photothermal therapy, biosensors, and drug delivery.²⁸¹ These 3D spherical vesicles are formed through the self-assembly process of AuNPs in a water–butanol emulsion system and exhibit a hexagonal close-packed multilayer structure. Fig. 26 shows the SEM and TEM images of the self-assembled Au PCs, along with their optical properties.

Fig. 26 (A) and (B) SEM images of the self-assembled Au plasmonic colloidosomes (PCs). (C) Scanning-TEM image of single Au PC. (D)–(F) SEM images of the broken Au PCs at different sizes. (E) High magnification SEM image marked in the frame of (D). Insets in (E) and (F) show their corresponding high-magnification SEM images, respectively. (G) and (H) Photos of red colloidal AuNPs and black PCs settled on the bottom of tubes, respectively. (I) Reflection spectra of the PCs deposited on the PTFE substrate measured at angles ranging from 10–65° with a test step of 5°. The inset in (I) shows the scheme of upper reflection. (J) The absorption spectra of black Au PCs (curve 1) and AuNPs (curve 2) suspended in 1-butanol, respectively. The inset in (J) shows the corresponding photograph of PCs (1) and colloidal AuNPs (2) suspended in 1-butanol. Reprinted with permission from ref. 281, Copyright 2015 Wiley-VCH Verlag GmbH & Co.

Wu et al. used the butanol-induced self-assembly of AuNPs to create a reproducible and sensitive nanoplasmonic substrate, which was used as a fast diagnostic platform for detecting SARS-CoV-2 (Fig. 27).²⁸² The deposition of water droplets containing AuNPs and target DNAs onto butanol droplets initially causes the dispersion of the AuNPs and target DNA towards the edge of the water droplet owing to the coffee-ring effect. Then, the water molecules are extracted into the butanol phase, which gradually reduces the volume of the water droplet. This butanol-induced dehydration reduces steric hindrance and electrostatic repulsion, forming a highly concentrated and uniformly assembled AuNP monolayer with narrow nanogaps at the interface between water and butanol. Additionally, target DNAs are enriched in the nanogaps between AuNPs, resulting in strong electromagnetic enhancement. This self-assembled SERS platform detected SARS-CoV-2 target genes at a LoD of 3.1 fM, rendering it 1000 times more sensitive than commercially available fluorescence kits.

Fig. 27 (A) Schematic Illustration of the self-assembly process of AuNPs, and the enrichment of Cy3-DNAs by butanol-induced dehydration. (B) Top (i)–(iv) and side views (v) and (vi) of SEM images measured at different magnifications for an as-prepared SERS substrate. (C) (i) Raman mapping images were measured on a 20 μm × 20 μm area of the SERS substrate. In total, 100 pixels were scanned using a Raman laser at 2 μm × 2 μm intervals to evaluate the pixel-to-pixel uniformity of the SERS signal of Cy3-DNAs present on the AuNP assembly. (ii) Raman mapping image measured by monitoring the Raman peak intensity variation at 1589 cm⁻¹ of the Cy3 attached to the DNA terminal for 1 nM Cy3-DNA. (iii) Raman spectra for 100 pixels of the image in (ii). (iv) The RSD for the Raman peak intensities at 1589 cm⁻¹ was determined to be 6.44%. Reprinted with permission from ref. 282, Copyright 2023 American Chemical Society.

4. Applications of SERS in disease diagnostics

4.1. Cancers

Cancer is a major cause of death worldwide, and its prevalence is expected to increase, which would result in millions of new cases and mortalities. Early cancer diagnosis is vital for improving patient outcomes and reducing mortality rates. Uncontrolled cellular proliferation is a hallmark of cancer; it increases DNA, RNA, and protein production and disrupts lipid metabolism. Notably, these biochemical changes occur much earlier than the onset of clinical symptoms. Therefore, SERS-based methods can be used to investigate and quantify the altered molecular signatures; the spectra obtained would serve as biomarker profiles for early disease classification and tumor grading. Moreover, the salient characteristics of SERS-based bioassay, such as its high sensitivity and specificity, are useful for early cancer detection and could potentially assist surgeons during operations. Additionally, SERS-based diagnosis may reduce cancer-related illnesses and death rates by facilitating personalized treatment management, which would enhance patient care. As early cancer detection remains challenging, with many cases diagnosed only after the tumor has metastasized, attention should be focused on developing sensitive and accurate diagnostic methods to identify cancer in its early stages when treatment options and success rates are more favorable. Currently, different types of SERS detection methods have been developed for sensing cancer cells with the aim of providing early diagnosis, which could lead to effective treatment for various types of cancer. Particularly, the SERS-based assay platforms use multiple biomarkers, rendering them suitable for detecting different kinds of cancer due to their high sensitivity and specificity.

4.1.1. Bladder cancer. Bladder cancer recurrence rates are high because of inadequate transurethral resection. Davis et al. used a nanoparticle and endoscope system to improve detection and resection.²⁸³ They tested active and passive targeting mechanisms and observed that SERS imaging yielded promising results for classifying bladder tissue as normal or cancerous. Passively targeted nanoparticles showed deeper penetration and higher concentrations in cancerous tissue. This indicates potentially enhanced surface permeability and retention effect in bladder cancer (Fig. 28A). Liang et al. designed an internal reference-based ratiometric SERS assay that uses molecular beacons anchored on Au nanoclusters.²⁸⁴ Liu et al. developed SERS-active substrates from highly ordered silver nanopore and nanocap arrays by depositing silver onto porous anodic alumina (PAA) membranes using electron-beam evaporation. They tested these substrates with bladder cancer cells, and the results were promising, with both substrates demonstrating significant SERS enhancement. The substrate derived from ordered silver nanocap arrays exhibited superior SERS and fluorescence quenching effects but no interference spectrum due to oxalate impurity in PAA membranes. Consequently, it enables high-quality Raman spectroscopy of bladder cancer cells.²⁸⁵ The nanoshells demonstrated improved SERS capabilities compared to those of other nanoparticle types. Moreover, the nanoshells exhibited minimal release of Cu species, which could potentially address the issues of accumulation and non-secretion commonly associated with previous inorganic SERS nanoparticles. A SERS platform is presented for the ultrasensitive detection of urinary bladder cancer antigens.²⁸⁶ This method works on the peptide–antibody pairing principle and uses magnetic beads to amplify the signal, resulting in a LOD of 6.25 ng mL⁻¹. The process successfully detected clinical urine samples from bladder cancer patients and healthy volunteers.

Fig. 28 (A) (i) Schematic diagram illustrating the testing principle of multiplex ratiometric Au nanoprobes in cells and outside cells. (ii) Total RNA extracted from urine samples is processed using asymmetric PCR and co-incubated with the nanoprobes for Raman signal detection. Reprinted with permission from ref. 284, Copyright 2022 Springer Nature. (B) Schematic illustration of breath analysis by an Ag@ZIF-67-based tubular SERS sensor. Reprinted with permission from ref. 295, Copyright 2022 American Chemical Society. (C) (i) Schematic of the high-throughput and super-hydrophobic SERS platform for BC, M5, HBV, and N screening. (i) BC, M5, HBV, and N-associated circulating analyses in the bloodstream. (ii) SERS spectroscopic serum date collection using high-throughput Raman spectrometer. (iii) SERS spectra set collected for each patient and analyzed using the DL algorithm. Reprinted with permission from ref. 302, Copyright 2021 Wiley-VCH Verlag GmbH & Co. (D) (i) Schematic illustration of the preparation process for the AgNPs@ZIF-67 structure. (ii) Schematic of the AgNPs@ZIF-67/g-C₃N₄ membrane fabrication process for SERS detection of lung cancer biomarkers. Reprinted with permission from ref. 310, Copyright 2021 Royal Society of Chemistry (E) Schematic illustration of SERS frequency-shift immunoassay developed for highly sensitive detection of the liver cancer biomarkers α-fetoprotein and Glypican-3 at sub-picomolar concentrations in saline solution. Reprinted with permission from ref. 306, Copyright 2016 American Chemical Society (F) Schematic diagram of the AgNW-GFF SERS sensor and urine analysis for the diagnosis of pancreatic cancer and prostate cancer. Reprinted with permission from ref. 316, Copyright 2021 American Chemical Society. (G) (i) Schematic design of the parallel microdroplet channels for simultaneous detection of f-PSA and t-PSA. (ii) Each channel is composed of four compartments with the following functions: (a) droplet generation and immunoreaction, (b) formation of magnetic immunocomplexes, (c) magnetic bar-mediated isolation of immunocomplexes, and (d) SERS detection. Reprinted with permission from ref. 318, Copyright 2018 Elsevier.

4.1.2. Breast cancer. Breast cancer is the most commonly diagnosed cancer worldwide; however, mammography has certain limitations when applied to screening.²⁸⁷ The microfluidic chip has been used to develop a SERS immunoassay biosensor, which accurately and simultaneously detects multiple breast cancer biomarkers in the human serum of clinical samples.²⁸⁸ The biosensor uses immuno-Ag aggregates labeled with different Raman reporters and self-assembled Ag nanoparticles to enhance the Raman signals. The proposed SERS microfluidic biosensor shows excellent specificity and high sensitivity in detecting CA153, CA125, and CEA biomarkers. The results are comparable to those obtained with commercial ELISA kits, confirming the reliability of the SERS-based immunoassay.

Epithelial–mesenchymal transition (EMT) is a crucial process that initiates cancer metastasis. Zhang et al. developed a SERS-based microfluidic immunoassay to identify soluble E-cadherin and N-cadherin, which are markers for EMT.²⁸⁹ The assay simultaneously and accurately detected these markers even at low levels. Hence, it has been used to successfully monitor EMT in breast cancer cells and identify EMT activation in patients with metastatic breast cancer, which potentially provides early indications of cancer invasion or spread. A biosensor was developed to detect breast cancer at an early stage using exosomes as biomarkers.²⁹⁰ The substrate was fabricated with Au@Ag nanoparticles/graphene oxide (Au@Ag NPs/GO) and 4-nitrothiophenol (4-NTP) as the internal standard. The biosensor demonstrated a wide detection range and considerably low LoD without requiring nucleic acid amplification as it selectively recognized exosomes derived from breast cancer cells. Furthermore, a 3D SERS holography strategy has been used to develop a novel approach to sensing breast cancer-associated miRNAs by spatially separating and quantifying miRNAs through modified DNA probes and Raman reporters.²⁹¹ The 3D SERS holography chip exhibits exceptional sensitivity, which was validated against RT-PCR results as it detected nine miRNAs in clinical serum samples, rendering it a promising tool for enhancing breast cancer diagnosis. Choi et al. developed a micro-network gradient platform for detecting two types of DNA oligomer mixtures using programmable SERS.¹⁷⁰ They showed that it successfully performed a quantitative analysis of DNA oligomer mixtures associated with breast cancer. The microfluidic circuit used an electric–hydraulic analogy to generate concentration gradients, and a multi-gradient microfluidic channel was fabricated accordingly. The micro-network structures automatically generated different concentration gradients by mixing labeled DNA oligomers. SERS signals were measured based on the different ratios of DNA oligomer mixtures that adsorbed onto the Ag nanoparticles.

4.1.3. Colon cancer. A SERS technique that used Fe₃O₄/Au/Ag nanoparticles was developed to detect cytidine in urine,²⁹² an early marker of colon cancer. This method demonstrated significantly improved detection sensitivity compared to other diagnostic techniques such as ECL or fluorescence and quantified cytidine at levels as low as 1 nM. The study reported an impressive overall prediction accuracy of 84.1% for colon cancer. Lin et al. developed a porous-plasmonic SERS chip enhanced with the CP05 polypeptide, specifically designed for capturing and differentiating exosomes from various sources.²⁹³ This chip targets the TIMP-1 protein and can discern between exosomes derived from lung and colon cancer cells and those from normal cells down to a single vesicle level. The system is made possible by applying unique Raman spectroscopy and machine learning techniques. This technology holds promising potential in human tumor monitoring and prognosis, offering a fresh perspective on analyzing exosome characteristics at the spectral level. Shi et al. developed a quadratic signal amplification strategy for ultrasensitive SERS detection of telomerase activity²⁹⁴ using telomerase-induced AgNP assembly and Ag⁺-mediated cascade amplification. This approach achieved a limit of detection down to a single HeLa cell and could be potentially applied in telomerase-based colon cancer diagnosis, telomerase inhibitor screening, and drug development.

4.1.4. Gastric cancer. Huang et al. performed breath analysis using a tubular SERS sensor coated with ZIF-67-coated Ag particles.²⁹⁵ This novel method effectively identified eight aldehydes and ketones as volatile organic compound (VOC) biomarkers for gastric cancer. Furthermore, modifying the sensor with 4-aminothiophenol efficiently captured gases and exhibited an impressive 89.83% accuracy in the non-invasive screening of patients with gastric cancer through breath analysis (Fig. 28B). Liu et al. developed a machine learning algorithm to distinguish cancer-derived small extracellular vesicles (sEVs) from non-cancerous ones.²⁹⁶ The prediction accuracies achieved were 90%, 85%, and 72% for tissue, blood, and saliva samples. This method could potentially be adapted for the non-invasive detection of other diseases. Gold nanopyramid arrays are used to enable SERS, holding promise for the non-invasive detection of gastric cancer by analyzing the makeup of Raman-active bonds found in small extracellular vesicles.

Cao et al. proposed a serum analytical platform using SERS and principal component analysis (PCA) combined with a two-layer nearest neighbor (TLNN) algorithm for detecting cisplatin-treated gastric cancer in mice without labels.²⁹⁷ A microarray chip with excellent portability, SERS activity, stability, and uniformity was used to measure SERS spectra of serum at different treatment stages. The PCA-TLNN model successfully distinguished GC mice at different treatment stages and identified key spectral characteristics for determining treatments. Huang et al. developed a microfluidic chip-based assay using SERS frequency shift to detect gastric cancer biomarkers CEA and VEGF.²⁹⁸ The chip allows for the simultaneous analysis of multiple biomarkers in various samples. It is highly sensitive with a low detection limit. By simplifying the detection process and improving specificity, this method shows promise for use in the early diagnosis and prognosis of gastric cancer.

4.1.5. Leukemia. Leukemia represents a diverse group of blood cell cancers identified based on the specific type of white blood cell involved. They are characterized by uncontrolled and rapid proliferation of immature leukocyte progenitors, which fail to mature accurately into white blood cells. Picciolini et al. focused on enhancing the performance of 2D nanopillar arrays with plasmonic crystal properties for SERS applications.²⁹⁹ These arrays detected multiple genetic leukemia biomarkers in a biochemical assay. During fabrication, the crystal surfaces can be customized to improve substrate properties. Moreover, the optimized substrate was functionalized with capture oligonucleotides and utilized in a sandwich assay with labeled AuNPs. Zheng et al. presented a novel method for ultrasensitive detection of circulating miRNA in human serum³⁰⁰ using an enzyme-free quadratic SERS signal amplification technique that combined miRNA-triggered hybridization chain reaction and Ag⁺-mediated cascade amplification to achieve an miRNA detection limit as low as 0.3 fM. This method is applicable for directly detecting circulating miRNAs in human serum collected from patients at various stages of chronic lymphocytic leukemia.

Hoonejani et al. employed a multiplexed labeling and detection strategy on four microparticle populations, each simulating different surface concentrations of up to four epitopes. This approach combines four unique biotags with SERS and microfluidics. The detailed SERS spectra allowed the separation of these populations using principal component analysis. By applying classical least squares, they calculated the relative contributions of each biotag to the overall signal, demonstrating the multiplexing capacity of SERS biotags in potential uses, such as immunophenotyping.³⁰¹ A new automated super-hydrophobic platform integrated with deep learning and SERS has been developed for efficient blood analysis for the early detection of cancers (Fig. 28C).³⁰² This system demonstrated 100% accuracy in categorizing serum SERS signals obtained from healthy individuals and patients with leukemia M5, hepatitis B, and breast cancer. When used with deep learning, it exhibited an outstanding diagnostic accuracy of 98.6% for disease screening.

4.1.6. Liver cancer. Han et al. optimized 3D plasmonic nanostructure substrates to enable precise analysis of biological molecules and differentiation between normal and cancer cells.³⁰³ The substrate successfully differentiates between healthy liver and cancer cells. This result is achieved with high accuracy by examining the spectral attributes of cell membranes and the metabolites expelled by the cells. Liu et al. developed a non-invasive method for detecting liver cancer using a NIR-SERS technique with a polyvinyl alcohol-protected silver nanofilm.³⁰⁴ This technique, which analyzes oxyhemoglobin, has demonstrated excellent sensitivity and specificity in distinguishing between healthy individuals and those with liver cancer. It also shows promise for assessing recovery in patients who have undergone liver cancer surgery. These findings suggest that NIR-SERS OxyHb analysis could be a game-changing clinical tool for detecting liver cancer.

Cheng et al. developed a highly effective sensing strategy for detecting microRNAs using branched DNA³⁰⁵ to detect various liver cancer biomarkers simultaneously by incorporating branched DNA with multiple complementary sticky ends. Sensitivity was significantly improved than that of single-stranded DNA usage. The system demonstrated a remarkable sensitivity with the LOD as low as 10 aM for miR-223 and 10⁻¹² M for alpha-fetoprotein. Tang et al. developed a SERS frequency-shift immunoassay for detecting biomarkers for liver cancer (Fig. 28E).³⁰⁶ The assay exhibited high sensitivity and specificity in the multiplex detection of alpha-fetoprotein and Glypican-3 biomarkers in a saline solution. The method uses Ag nanoparticle films and chemisorbed Raman reporters and functioned effectively with fetal calf serum and serum from a patient with hepatocellular carcinoma.

4.1.7. Lung cancer. Lung cancer is one of the most common cancers with high mortality rates worldwide. A significant challenge in managing lung cancer is obtaining an accurate diagnosis of cancerous lesions, which warrants the development of an efficient and sensitive method that can detect the circulating tumor DNA (ctDNA) in patients with lung cancer, as ctDNA detection is vital for determining prognosis. Qian et al. developed a microfluidic chip that integrated SERS with signal amplification techniques to detect ctDNA.³⁰⁷ This chip exhibited excellent sensitivity and specificity in ctDNA detection, indicating its potential as a valuable tool for evaluating the effectiveness of lung cancer treatment in clinical settings. Gao et al. proposed a 4 inch ultrasensitive SERS substrate for early lung cancer diagnosis³⁰⁸ by harnessing the unique particle-in-cavity structure of the substrate, resulting in excellent SERS performance for the detection of gaseous malignancy biomarkers at an LoD of 0.1 ppb. This large-sized sensor was divided into smaller ones, which significantly boosted the output of the commercial SERS sensors. Additionally, a medical breath bag containing the small chip demonstrated highly specific recognition of lung cancer biomarkers in mixed mimetic exhalation tests.

Cao et al. developed a new method for amplifying signals to monitor the levels of BRAF V600E and KRAS G12V ctDNAs in non-small cell lung cancer³⁰⁹ using a SERS microfluidic chip and a catalytic hairpin assembly technique. This method demonstrated a low LoD, a fast analysis process, and satisfactory selectivity, reproducibility, and uniformity. The results were consistent with RT-PCR, indicating its potential as an alternative tool for clinically diagnosing NSCLC. Fig. 28D shows the SERS platform developed by Huang et al. to detect aldehydes, lung cancer biomarkers found in exhaled breath.³¹⁰ This platform used a multifunctional solid-phase extraction membrane composed of AgNPs@ZIF-67/g-C₃N₄ and demonstrated excellent sensitivity with a detection limit of 1.35 nM. The enhanced sensitivity can be attributed to the synergistic effect of Ag NPs, ZIF-67, and g-C₃N₄. Furthermore, the Ag NPs@ZIF-67/g-C₃N₄ membrane exhibited a self-cleaning capability, which enables reusability.

4.1.8. Melanoma. Identifying biomarkers, such as melanoma chondroitin sulfate proteoglycan (MCSP), in liquid biopsies is crucial for early cancer diagnosis. However, detecting MCSP at the initial stage of the disease is challenging due to its low levels and the need for high precision. Kumar et al. introduced a highly sensitive microchip that uses SERS immunoassay to simultaneously analyze up to 28 samples³¹¹ by incorporating nanofluidic mixing and anisotropic Au–Ag alloy nano boxes to improve the speed and sensitivity of the assay. This resulted in the accurate detection of MCSP. Its performance was validated using simulated samples from patients with melanoma. A therapy that has demonstrated success in treating melanoma is the immune checkpoint blockade. However, identifying patients who respond to this therapy is crucial for enhancing its outcomes and minimizing side effects. Li et al. developed a SERS assay platform that examines circulating tumor cells (CTCs) and predicts responses to ICB therapy to address this need.³¹² The SERS assay measures CTCs at both the group and individual cell levels and offers insights into tumor heterogeneity and the comprehensive CTC phenotype. Using anisotropic Au–Ag alloy nanoboxes enhances signal readouts of CTC surface biomarkers. The assay effectively distinguishes CTCs from different melanoma cell lines and shows promise in predicting responders among melanoma patients undergoing ICB therapy. An advanced extracellular vesicle phenotype analyzer chip (EPAC) for tracking patient responses to treatments was developed by Wang et al.³¹³ The EPAC employs a nano mixing-enhanced microchip and a multiplex SERS system, allowing for direct EV phenotyping without needing EV enrichment. It could discern differences in EV phenotypes and treatment responses in a preclinical model. They identified specific cancer-related EV phenotypes from the plasma of melanoma patients and changes in EV phenotypes of patients receiving targeted therapy to elucidate detailed EV profiles linked to drug resistance.

4.1.9. Pancreatic cancer. Several studies have indicated that the SERS assay performs better than conventional assays in terms of detection limits, readout time, and required sample volume, which are preferred characteristics in potential tools for diagnosing early-stage PC. Beyene et al. crafted a detection method for the pancreatic cancer marker MUC4, utilizing SERS and gold nanoflowers (AuNF).³¹⁴ They integrated a microwell plate during the incubation phase of the capture substrate, which considerably reduced the blank value. They employed Raman mapping across a broad substrate area to lessen spot-to-spot variation. Using AuNFs in the Enhanced Raman Layer and the capture surface has amplified detection sensitivity. Li et al. developed a novel immunoassay for highly sensitive and accurate detection of exosomes derived from pancreatic cancer cells³¹⁵ using polydopamine-modified substrates to capture the exosomes and an ultrathin polydopamine-encapsulated antibody–reporter–Ag(shell)–Au(core) multilayer (PEARL) system for SERS detection. The immunoassay effectively differentiated patients with pancreatic cancer from healthy individuals and metastasized tumors from tumors without metastasis. It enabled the classification of tumor node metastasis (TNM) stages. Thus, this immunoassay is a valuable tool for the early diagnosis, classification, and monitoring of the spread of pancreatic cancer. Metabolomics could potentially facilitate early cancer diagnosis, but it necessitates improved analytical methods. Fig. 28F shows a rapid urine analysis system developed by Phyo et al. that uses SERS with Ag nanowires on a glass fiber filter sensor.³¹⁶ The system effectively differentiated spectral patterns and specific peaks among normal control, pancreatic, and prostate cancer groups. Multivariate analyses distinguished cancer and normal control groups, demonstrating its potential as a non-invasive urine-SERS analysis system for cancer diagnosis and screening.

4.1.10. Prostate cancer. Yu et al. developed a new SERS-based DNA assay to rapidly and sensitively detect prostate cancer antigen 3 (PCA3), mimicking DNA.³¹⁷ This assay does not require DNA amplification through thermocycles as in conventional PCR, rendering it highly sensitive. Instead, it utilizes sandwich DNA complexes and monitors Raman peak intensity to achieve a LoD of 2.7 fM. This LoD is four times more sensitive than that of conventional PCR. Cheng et al. developed a SERS-based immunoassay to accurately determine the ratio of free to total (f/t) prostate-specific antigen (PSA) in clinical serum³⁴ using magnetic beads and SERS nanotags, which showed strong correlation with the results obtained via electrochemiluminescence (ECL) system; however, the precision was improved while simultaneously detecting free PSA (f-PSA) and complexed PSA (c-PSA) in clinical samples. This SERS-based assay could potentially be used for the precise diagnosis of prostate cancer. Fig. 28G showed a new microfluidic device developed by Gao et al. It simultaneously uses SERS to detect free prostate-specific antigen (f-PSA) and total PSA (t-PSA) markers,³¹⁸ enabling fast and sensitive detection of these PSA markers using a droplet-based platform. The results show strong linear response and low detection limits for both markers, indicating that this method is potentially valuable as a clinical tool for prostate cancer screening. Chen et al. proposed a SERS-based vertical flow assay (VFA) for simultaneously detecting multiple prostate cancer biomarkers³¹⁹ using a biosensor fabricated with Raman-encoded core–shell SERS nanotags for broad linear dynamic range and high sensitivity. It could be a potentially rapid and sensitive tool for biomarker detection in POC and cancer diagnosis. Lu et al. developed a new and versatile nanocone array that serves as a solid immunoassay plate and SERS substrate.³²⁰ The nanocone array is coated with a thin Au film, which enhances the surface area and enables efficient antigen capture and conjugation with capture aptamers. This innovative technology enables the simultaneous detection of PSA and thrombin. Moreover, incorporating a microfluidic chip enables rapid and automated detection with high sensitivity limits, rendering it a promising tool for early prostate cancer diagnosis.

4.1.11. Renal cancer. Renal cancer (RC) represents 3% of all cancers, with 2% annual increase in incidence worldwide. Moisoiu et al. explored using SERS profiling of serum as a liquid biopsy approach for detecting renal cell carcinoma (RCC).³²¹ Serum samples from patients with RCC and healthy participants were examined, and the SERS spectra revealed variations in purine metabolites and carotenoids between the two groups. Furthermore, differentiation between the RCC and control groups was achieved by including machine learning algorithms. The average area under the curve was 0.77. This study suggests that SERS liquid biopsy is a promising diagnostic and screening strategy for RCC, although further validation is necessary. A novel tool utilizing a microfluidic device, a photovoltaic SERS-active platform, and shell-isolated nanoparticles (SHINs) has been developed for the efficient and non-invasive detection and analysis of CTCs in blood.³²² The tool allows for the separation of tumor cells from whole blood samples and their molecular characterization, improving the accuracy and sensitivity of analysis. The method challenges current multi-step CTC detection methods regarding simplicity, sensitivity, invasiveness, destructivity, time, and analysis cost.

4.2. Cardiovascular diseases

Atherosclerosis (AS) is the most common factor in cardiovascular and cerebrovascular diseases. IL-10 and MCP-1 (a chemokine) are involved in AS progression, and monitoring their varying levels can indicate AS status and aid in the early diagnosis of AS-associated diseases. Fig. 29A illustrates a new paper-based SERS sensing platform established by Li et al. for detecting these key cytokines. The platform combined a nanoporous networking membrane as the substrate and SERS nanotags as the probes for signal detection. The antibodies used in the capture-end and SERS nanotags demonstrated high specificity in recognizing the target molecules, minimized nonspecific binding, effectively addressed cross-reactivity from two targets of significant biological relevance, and facilitated sensitive quantification of both IL-10 and MCP-1 in human serum using a sandwich design at a remarkably lowest detectable concentration of 0.1 pg mL⁻¹.³²³

Fig. 29 (A) Schematic of the assay workflow on the paper-based substrate for MCP-1 and IL-10 duplex detection; reprinted with permission from ref. 323, Copyright 2020 Royal Society of Chemistry. (B) Schematic illustration of the core–shell SERS nanotag-based multiplex LFA; reprinted with permission from ref. 324, Copyright 2018 Elsevier. (C) Schematic illustration of quantitative LFA for cardiac biomarkers detection on a single T line with RD-encoded core–shell SERS nanotags; reprinted with permission from ref. 325, Copyright 2018 Elsevier. (D) Preparation of Raman SERS probe conjugated with detecting antibodies for targeting a specific cardiac biomarker; reprinted with permission from ref. 326, Copyright 2020 Elsevier. (E) Workflow of the SERS-based finger-pump microfluidic chip for simultaneous detection of dual AMI biomarkers. Three reagents were dropped at the inlets of the chip. The fabrication process of the three-layer finger-pump microfluidic chip is shown at the right-hand side (i). Lateral view of the finger-pump microfluidic chip, and the schematic representation of check valves when the finger-pump was pressed and released by the fingers (ii). Reprinted with permission from ref. 329, Copyright 2023 Elsevier.

Acute myocardial infarction (AMI) is a leading cause of death and the most immediately life-threatening issue worldwide. Hence, the quantitative and multiplex detection of the associated biomarkers using rapid LFA would greatly enhance AMI monitoring and save lives more efficiently. Zhang et al. were the first to report simultaneous rapid and quantitative detection of three cardiac biomarkers using one SERS-LFA platform comprising Ag–Au core–shell bimetallic nanotags as the SERS substrates. As shown in Fig. 29B, the Ag–Au core–shell bimetallic nanotags with Nile Blue A (NBA) in the interior space between the two metals (AgNBA@Au) were encased as an alternative to using Au colloids in the conjugate pad. Three test lines were used in the strip to detect three cardiac biomarkers: Myo, cTnI, and CK-MB, the LoDs for which were 1 ng mL⁻¹, 0.8 ng mL⁻¹, and 0.7 ng mL⁻¹, respectively.³²⁴ Similarly, Zhang et al. also developed a single SERS-LFA test line for quick AMI diagnosis. Fig. 29C demonstrates the use of core–shell SERS nanotags, encoded with methylene blue, Nile blue A, and rhodamine 6G as labels. These were applied to measure CK-MB, cTnI, and Myo simultaneously on a single test line. The spatial separation in the three SERS-LFA test lines were substituted with wavelength separation based on encoded SERS nanotags in the single test SERS-LFA line. The LoDs for CK-MB, cTnI, and Myo were 0.93 pg mL⁻¹, 0.89 pg mL⁻¹, and 4.2 pg mL⁻¹, respectively, which were lower than the cutoff values in the human body and were detected in 17 min. The dynamic ranges for cTnI, CK-MB, and Myo were 0.01–50 ng mL⁻¹, 0.02–90 ng mL⁻¹, and 0.01–500 ng mL⁻¹, respectively. These ranges encompass the clinical spans of the three biomarkers.³²⁵

When dealing with high analyte concentrations, the microfluidic paper-based device (μPAD) and bio-recognition molecule saturation reach their limits. This saturation causes the linear calibration curve to plateau, making it unsuitable for quantitative measurements. It is a significant barrier to its practical use, particularly in the complex process of clinical sample analysis. Lim et al. developed a new, calibration-free microfluidic μPAD. Based on SERS, this device features multiple reaction zones, enabling concurrent quantitative detection of cardiac biomarkers, specifically GPBB, CK-MB, and cTnT. As illustrated in Fig. 29D, three unique Raman reporters, namely, 4-nitroaniline, tert-butylhydroquinone, and methyl red, were bound to the surface of Ag, Au-urchin, and Au nanoparticles, respectively, followed by covering with silica shell and conjugation of specific detecting antibody to form SERS probes. These SERS probes were integrated into the developed μPAD for AMI cardiac biomarker detection. The LoD for GPBB, CK-MB, and cTnT were 8 pg mL⁻¹, 10 pg mL⁻¹, and 1 pg mL⁻¹, respectively, significantly surpassing the clinical cutoff thresholds.³²⁶

A microfluidic chip that fixed the magnets at specific positions was used as the detection platform to obtain accurate and reproducible SERS signals.³²⁷ Microfluidic technology allows precise control of fluids in microchannels, resulting in low sample consumption, fast mass transfer, automatic sampling, etc.³²⁸ Liu et al. introduced a SERS-based microfluidic chip by incorporating check microvalves and a finger-pump for dual AMI biomarker detection. Fig. 29E shows the microfluidic chip, which has four key components: a finger pump, a winding channel, a collection microchamber, and check microvalves. These elements facilitate immunoreactions, immunocomplex collections, multiple reagent additions, and fluid transportation within the channel, respectively. Nile Blue and malachite green isothiocyanate were used as the Raman reporters for cTnI and CK-MB detection, respectively. During flow transport, the SERS nanoprobes, antibodies conjugated to magnetic beads, and antigens reacted and formed the “sandwich” immunocomplexes through antibody–antigen interactions. These immunocomplexes were isolated and captured in the microchamber using an integrated magnet within 5 min without requiring a syringe pump. cTnI and CK-MB were measured at a concentration range of 0.01–100 ng mL⁻¹ at LoDs of 5.04 pg mL⁻¹ and 2.34 pg mL⁻¹, respectively.³²⁹

4.3. Infectious diseases

This subsection reviews SERS-based sensor platforms for detecting three primary infectious agents, namely, bacteria, viruses, and parasites.

4.3.1. Bacterial detection. The rapid and on-site sensing of bacteria could help to assess the effectiveness of antibiotics and combat antimicrobial resistance. A recently developed reliable SERS-based test platform displayed great potential value in pathogen detection. As the SERS performance is highly influenced by the geometry of “hotspots,” intrinsic dielectric constant, and substrate composition, the reliable sensing of bacteria using SERS technology necessitates the rational design of a substrate with high sensitivity, stability, and minimal invasiveness. Wang et al. have presented a multifunctional chip based on SERS that effectively captures, discriminates, and inactivates pathogenic bacteria. As shown in Fig. 30A, Ag nanoparticle decorated-silicon wafer was used as SERS substrate, with 4-mercaptophenylboronic acid as a dual-function molecule, acting as both a Raman reporter and a recognition element. This SERS platform displays high bacterial capture efficiency and antibacterial activity. Meanwhile, it has enabled reproducible and sensitive bacterial detection (approximately 1.0 × 10² cells per mL) using the high-performance SERS chip, which exhibits robust and strong antibacterial activity (antibacterial rate, approximately 97%) and capacity to distinguish different bacteria in human blood.³³⁰

Fig. 30 (A) Schematic illustration (not drawn to scale) of (i) the SERS chip and (ii)–(iv) capture, detection, and inactivation of bacteria with the multifunctional SERS chip (blue ellipses represent bacteria). Reprinted with permission from ref. 330, Copyright 2015 Wiley-VCH Verlag GmbH & Co. (B) (i) Schematic illustration of the SERS-based imaging sensor for the selective detection of bacteria on a 3D substrate. Antibody-conjugated SERS nanotags were bound on the bacterial surface membrane. (ii) SEM images of SERS nanotag-labeled bacteria captured on the PLL-coated 3D SERS substrate. Insert in the SEM image in the upper left side shows the antibody-conjugated SERS nanotags combined successfully with bacteria. (iii) Schematic illustration of S. typhimurium antibody-conjugated SERS nanotags. Reprinted with permission from ref. 331, Copyright 2018 American Chemical Society. (C) Schematic representation of the nanoparticle SERS encoding and functionalization with antibodies and aptamers. Conceptual view of microfluidic-optical device for bacterial quantification and its relevant components. Reprinted with permission from ref. 332, Copyright 2016 Wiley-VCH Verlag GmbH & Co. (D) Schematic diagram of synthesis of 3D GO@Au/Ag SERS nanosticker, preparation of immuno-GO@Au/Ag SERS labels, and mechanism underlying GO@Au/Ag-based SERS-LFA for multiplex detection of four bacteria. Reprinted with permission from ref. 333, Copyright 2022 Elsevier.

4.3.1. Bacterial detection. S. typhimurium is a pathogenic Gram-negative bacterium that causes food poisoning and intestinal infectious diseases. Ko et al. reported a fast SERS mapping technique for detecting S. typhimurium, which is shown in Fig. 30B. The 3D Ag@Au core–shell nanopillar arrays are fabricated using metal deposition onto polyethylene terephthalate (PET) substrates, which are functionalized with positively charged poly(L-lysine). The bacterial pathogens are immobilized onto the positively charged nanopillar arrays, followed by antibody-conjugated SERS nanotags for selective capturing. Then, SERS mapping images are collected in the sandwich structure for bacterial quantification. Raman intensity is measured at a peak intensity of 1615 cm⁻¹ in the 0–10⁶ CFU mL⁻¹ range within 45 min.³³¹ SERS-encoded particles combined with microfluidics have been extensively employed in bacteria detection. Catala et al. have developed a microfluidic device for the rapid quantification of S. aureus in human fluids.³³² As shown in Fig. 30C, the SERS tags modified with antibodies or aptamers were gathered on the surface of S. aureus, producing hot spots. The mixture flows through a microfluidic channel with a pump for real-time analysis. Targeted bacteria trigger a significant SERS signal increase, revealing spectral fingerprints for pathogen identification and quantification. The optical detection of S. aureus at a short acquisition time (10 min mL⁻¹) has been applied to actual human fluids, including urine, blood, pleural, and ascites fluids, achieving bacterial concentrations as low as <15 CFU mL⁻¹.

LFA works on the principle of rapid chromatographic separation and antibody–antigen specific recognition. Despite developing into the most popular POC technology, LFA techniques, such as directly detecting bacteria, still face significant challenges.

However, integrating LFA with SERS could considerably improve LFA sensitivity, quantitation capacity, and multiplex detection ability. Wang et al. developed a bi-channel SERS-based LFA using 3D membrane-like SERS tags as nano stickers (named GO@Au/Ag).³³³ As shown in Fig. 30D, the GO@Au/Ag membrane structure consisted of three key elements: flexible 2D GO@Au nanosheet, polyethyleneimine interlayer, and numerous externally assembled Ag satellites enabling large and even surface for accommodating Raman dye molecules (DTNB and 4-MBA) and bacterial binding. This SERS platform enables the simultaneous detection and quantification of four key pathogenic bacteria within 20 min.

4.3.2. Virus detection. Chen et al. developed a SERS-based aptasensor platform for the highly sensitive and reproducible detection of the influenza A/H1N1 virus. A 3D nano-popcorn plasmonic substrate, which was fabricated using a thermal evaporation method, was used as the SERS substrate to enable good signal reproducibility and strong enhancement capability. Cy3-labeled aptamer probes were immobilized onto the three-dimensional nano-popcorn substrate to create a strong Raman signal. In the presence of H1N1 virus, the aptamer DNA was released from the 3D nano-popcorn substrate, which reduced the Raman signal intensities. This variation enabled a highly accurate quantitative evaluation of the A/H1N1 virus with a LoD of 97 PFU mL⁻¹ in 20 min.²⁶⁸

The detection of SARS-CoV-2 has been the subject of intense research since 2020. To rapidly detect specific antigens of SARS-CoV-2 infection, Guan et al. introduced a POC SERS detection platform that targets the SARS-CoV-2 spike protein. This platform can specifically detect SARS-CoV-2 antigen in a single step through the interaction of the capture substrates and detecting probes based on aptamer-specific recognition. Three aptamers, namely, CoV-2-1C, CoV-2-4C, and CoV-2-6C, were selected for this platform owing to their advantages such as enhanced binding affinity, reduced steric hindrance, and minimized mutational escape for the aptamer in recognizing the coronavirus. These three aptamers targeted SARS-CoV-2 RBD with high binding affinity and selectivity without recognizing spike proteins from other viruses. This SARS-CoV-2-detection platform was rapid, sensitive, cost-effective, and portable, and it detected SARS-CoV-2 within 5 min.¹¹³

LFA strips can be used for POC detection of SARS-CoV-2. Srivastav et al. developed a rapid and sensitive SERS-based LFA to detect SARS-CoV-2-specific IgM/IgG. As shown in Fig. 31A, the SERS-based LFAs use the NIR dye as a reporter, and Au nanostars were used as the SERS substrate, providing much higher sensitivity than traditional LFA. SARS-CoV-2-specific IgM and IgG presence was detected even when traditional naked-eye detection showed no visible test line. The sensitivity of this SERS-based LFA for COVID-19 serum samples was approximately 10 times higher than that of conventional LFAs. SERS sensitivity in detecting total IgM in the buffer improved approximately 7 times compared to conventional LFAs.³³⁴ In general, COVID-19 and flu have similar symptoms; hence, they are difficult to distinguish without an accurate diagnosis. Therefore, quickly and accurately determining the infecting virus is crucial to prescribing the appropriate treatment for the infected person.³³⁵

Fig. 31 (A) Schematics of the SERS-based LFA platform for SARS-CoV-2 specific IgM detection. Reprinted with permission from ref. 334, Copyright 2021 American Chemical Society. (B) Schematic of the synthetic procedure for dual-signal MoS₂@Au–Au tags and the principle of MPXV detection using colorimetric–SERS dual-mode ICA. Reprinted with permission from ref. 336, Copyright 2023 Elsevier.

Lu et al. developed a dual-mode SERS-based LFA strip capable of accurately diagnosing both SARS-CoV-2 and influenza A virus to mitigate the false-negative issue prevalent in commercial colorimetric LFA strips. AuNPs modified Raman reporter, and the relevant antibodies were employed as SERS probes; when the target emerged in the T line, a strong Raman signal was produced. This dual-mode SERS-LFA strip detected SARS-CoV-2 and influenza A virus simultaneously with a LoD of 5.2 PFU mL⁻¹ and 23 HAU mL⁻¹, respectively, 10 and 40 times more sensitive than ELISA.

The unexpected monkeypox outbreak in 2022 underscores the urgency of developing a highly rapid and sensitive virus detection platform. In response, Yu et al. developed a two-dimensional molybdenum disulfide and polyethyleneimine interlayer with controllable thickness, which could capture AuNPs via electrostatic interaction. They designed 3D thin-film-shaped MoS₂@Au–Au tags to ensure high-performance dual signal ICA. As shown in Fig. 31B, the SERS-ICA platform comprised a thickness-controlled polyethyleneimine interlayer (1 nm) coated onto a two-dimensional molybdenum disulfide (MoS₂) nanosheet to enable the electrostatic adsorption of two layers of dense 30 nm AuNPs. This improved the colorimetric ability and created numerous efficient SERS hotspots. The proposed assay enables a rapid MPXV detection within 15 min with high sensitivity achieved at 0.2 ng mL⁻¹.³³⁶

4.3.3. Parasite detection. Malaria is a fatal disease that claims millions of lives worldwide, especially in third-world countries. It is caused by the malarial parasite that enters the human bloodstream through a bite from a female Anopheles mosquito. Early diagnosis of malaria is essential to mitigate the spread of the parasite.³³⁷ SERS-based analytical platforms enable the ultra-sensitive diagnosis of malaria. Mhlanga et al. manufactured SERS probes with high sensitivity and selectivity for the early detection of malaria. As shown in Fig. 32A, lactate dehydrogenase (LDH) malarial antibody (mAb) probes were immobilized on SERS substrates to capture malaria antigens against Plasmodium falciparum (Pf). Herein, AgNPs modified with the second LDH mAb were used as a SERS substrate. Detecting hybrids binds antigens to specific epitopes and interprets sandwich structures using vibrational Raman spectroscopy, indirectly confirming the PfLDH malaria antigen using Raman reporter labeling within 3 min.³³⁷ Currently, SERS-based chips are a common detection method in malarial field diagnosis, and Yuen et al. have reported a novel SERS-based chip for rapid malarial diagnosis. As shown in Fig. 32B, water and a drop of malaria-infected blood are mixed with dried chemicals on the chip to induce SERS nanoparticle synthesis near hemozoin for subsequent SERS measurements. A 785 nm laser is used for excitation, and the emitted Raman signal is detected through an objective lens and analyzed with a spectral resolution of 4 cm⁻¹. The spectral acquisition time was 20 s cumulatively four times. This strategy can mitigate the limitations of ready-made SERS substrates and achieve a detection limit of 0.0025% parasite level (125 parasites per μL).³³⁸

Fig. 32 (A) Summary of SERS probe preparation. Reprinted with permission from ref. 337, Copyright 2020 Wiley-VCH Verlag GmbH & Co. (B) Schematic drawings and photographs of various subassemblies of the SERS fluidic chip. (i) Partially expanded schematic and the constituent subassembly components of the chip. (ii) Detailed dimension of (I) reaction module. (iii)–(vii) Photographs of subassembly components corresponding to modules I–IV, the entire assembled SERS lab-on-chip, and the manual syringe pump. Reprinted with permission from ref. 338, Copyright 2021 Elsevier.

5. Future perspectives

5.1. Development of SERS-based POC system

Infectious diseases such as COVID-19 have caused significant social and economic losses worldwide. Respiratory infections inherently spread rapidly, necessitating technologies for the quick and accurate POC detection of infection. Recently, integrating a portable Raman system with the microdevice mentioned earlier into a POC system that uses SERS-based bioassay methods has shown promise in addressing these issues. Particularly, Tran et al. developed a portable Raman reader using optical fibers to mitigate the low sensitivity issue associated with LFA strips (Fig. 33A). The reader measures the SERS signal from the test and control lines on LFA strips³³⁹ using a line-scanning method instead of point-mapping, which significantly reduces the detection time. Compared with a pregnancy test LFA strip, which uses human chorionic gonadotropin (hCG) as the target marker, the SERS-based LFA strip with a portable line-scanning Raman reader shows approximately 16 times higher sensitivity. Joung et al. recently developed a portable Raman reader for LFA strips for commercial COVID-19 testing,²⁸ which detects the nucleocapsid protein biomarker of COVID-19 (Fig. 33B). The POC SERS-based assay system achieved a low LoD of 3.5 PFU mL⁻¹ as opposed to 350 PFU mL⁻¹ of the commercial colorimetric LFA strip. This system was tested on 54 clinical samples, and sensitivity increased to 96% from 57%, as observed with the commercial LFA strip. This demonstrates its potential in addressing the primary issue with LFA strips: the false-negative diagnosis for individuals with low viral concentrations.

Fig. 33 (A) Portable Raman/SERS reader comprising a custom-designed optical fiber probe with laser line scanning. Reprinted with permission from ref. 339, Copyright 2019 Wiley-VCH Verlag GmbH & Co. (B) Combination of a SERS LFA strip with a portable Raman reader to enhance the sensitivity of the commercial LFA strip. Reprinted with permission from ref. 28, Copyright 2022 American Chemical Society.

5.2. Integration of SERS technology and machine learning

Integration of SERS technology and machine learning (ML) for bioassays have been reported, as depicted in Fig. 34.

Fig. 34 Machine learning in SERS for bioassays. Human samples, including saliva, nasal swabs, blood, and tissues, are collected and extracted for bioassay on functionalized substrates using spectroscopic methods with laser light. SERS signals can then be processed using a machine learning AI algorithm to determine and identify diseases.

The advanced SERS platform can potentially revolutionize the authentic and perceptible identification of pharmacodynamic substances. Integrating SERS spectroscopy technology and ML would provide analytical and empirical support for novel approaches to identify various physiologically active materials (Fig. 35).³⁴⁰ Advances in ML techniques can considerably improve rapid investigation and automated data manipulation in molecular diagnostics and screening.³⁴¹ ML algorithms have been assisting nanomaterial-based optical sensor arrays in emerging as a preferred analytical approach for microbial identification owing to their advantages, such as quick responsiveness, efficiency, and user-friendly operation.³⁴² ncorporating microfluidic platforms can significantly simplify and automate assays, ultimately creating a substantial database for algorithmic categorization.³⁴³

Fig. 35 Integration of SERS and machine learning for highly sensitive discriminatory prediction of cancer and cardiovascular diseases and detection of bacteria, viruses, and parasites.

We believe that SERS would play an essential role in virus detection as it combines nanotechnology, microfluidics, and ML, which ensures the possibility of spectrum-related reproduction and streamlining of the specimen handling and identification processes.³⁴⁴ The selection of the appropriate chemometric/ML method has helped to transform SERS from an alternative detection strategy into a widely applicable analytical technique for bioassay applications.³⁴⁵ The use of SERS in various medical applications could help to identify intermittent disease sources, which may lead to better measures for infection prevention and strategies for providing effective treatments and implementing preventive measures.³⁴⁶ Extracellular vehicles (EVs) that are released by cancer cells into bodily fluids may contain valuable biochemical information regarding the underlying illnesses and could be potential cancer biomarkers. Better detection of such EVs could advance diagnostic methods such as liquid biopsy through optical measurements based on nanostructures and various ML models.³⁴⁷ Exosomes are a specific type of EVs released by cells; they could serve as potential non-invasive biomarkers for early disease diagnosis and treatment, particularly in the case of cancer. Applying the ML approach to exosome detection would benefit non-invasive and specific disease analysis and postoperative assessment.³⁴⁸ Using SERS in live cells enables selective and sensitive investigations of chemical structures, organelle probing, vibrational fingerprinting, and the integration of novel nanostructures. Moreover, ML could be applied to extract information from highly complex cellular systems.³⁴⁹De novo ML methods for diagnosing complex molecular structures are applied to address the remaining challenges through integrated developments.³⁵⁰

ML represents an innovative approach for extracting information from large and complex datasets in microspectroscopy, such as in SERS methods, where extensive datasets of complex vibrational structures of complexes are collected for analytes or imaging living organisms.³⁵¹ A summary of the advantages and drawbacks of the most popular ML algorithms is that ML is based on data analysis. Applying various ML approaches, such as recurrent neural networks and convolutional neural networks to various ML-supported SERS sensors, bridges deep learning-based algorithms with biosensors and introduces chemometrics into detection and diagnosis.³⁵² For example, identifying antibiotic-resistant pathogens may aid in tracking the onset of sporadic infections, which may prevent epidemics and promote improved biomedical solutions and preventive measures.³⁴⁶ The prospect involves using nanostructures to develop diagnostic methods for hemorrhagic fever viruses and address current challenges in a clinical context.³⁵³ ML-based SERS technologies have advanced in the automated configuration of complex data layers, data processing, and decision-making, owing to the use of ML methods to address problems and the trend towards de novo molecular diagnostics.³⁴¹

We present further examples of combined SERS–ML applications. An ML-guided Raman sensor enables the simultaneous use of specific vibrational fingerprint data from multiple receptors and enhances multiplex analysis at the ppm level.³⁵⁴ A SERS substrate with Au nanopillars was used to detect quinoline in wastewater, with an LOD of 5.01 ppb, owing to the use of ML algorithms.³⁵⁵ A combination of non-labeled SERS probing, microfluidic channels, and various ML methods was implemented to obtain specific molecular fingerprints in the time-dependent variations of tumor secretomes.³⁵⁶ ML-supported plasmonic SERS substrates in providing a fast and reliable method for identifying and quantifying antibiotics in dairy products, as illustrated in Fig. 36.³⁵⁷

Fig. 36 (A) Comprehensive SERS profiles for predictive analytics using machine learning (ML). Integration of SERS and ML. Reprinted with permission from ref. 354, Copyright 2021 American Chemical Society. (B) ML-based SERS platforms for the non-labeled monitoring of quinoline for antibiotics present in wastewater. Reprinted with permission from ref. 355, Copyright 2023 American Chemical Society. (C) Workflow of ML-classification method-based strategy for SERS classification of cell secretomes. Reprinted with permission from ref. 356, Copyright 2023 Wiley-VCH Verlag GmbH & Co. (D) Schematic diagram of SERS platform for antibiotic monitoring in milk using ML without any labels. Reprinted with permission from ref. 357, Copyright 2022 Wiley-VCH Verlag GmbH & Co.

As depicted in Fig. 37, ML approaches leverage the practical attributes of each Raman spectrum, providing an early prognosis of heart failure.³⁵⁸ Single extracellular vesicle SERS data were analyzed using ML algorithms to stratify molecular changes during liquid biopsy for patients with glioblastoma.³⁵⁹ Stroke-specific metabolites that correlated with SERS spectra were used for rapid screening and precise diagnosis with the help of ML.³⁶⁰ The evaluation of convolutional neural network training data indicates that vibrational functional groups offer specificity to nucleic acids, lipids, and proteins, which enables the identification of cancer cell lines and aids in distinguishing them from metabolites of normal cells.³⁶¹

Fig. 37 (A) Scheme of the SERS-introduced platform for ML-supported investigation and preventive intervention of heart attacks. Reprinted with permission from ref. 358, Copyright 2023 Wiley-VCH Verlag GmbH & Co. (B) Concept of single extracellular vesicle SERS approach in liquid biopsy for patients with glioblastoma. Reprinted with permission from ref. 359, Copyright 2023 American Chemical Society (C) Extraction of serum vibrational and metabolic estimation toward stroke diagnosis using ML. Reprinted with permission from ref. 360, Copyright 2023 Wiley-VCH Verlag GmbH & Co. (D) Implementation of ML for SERS result interpretation. Reprinted with permission from ref. 361, Copyright 2020 Elsevier.

By integrating ML and Raman spectroscopy, a nanomaterial substrate was developed for sensing exhaled volatile organic mixtures, particularly gaseous aldehyde species.³⁶² Early exposure and prediction of cancer metastasis-initiating cells were investigated as dynamic markers using ML-assisted nanosensors.³⁶³ Label-free SERS technology was used to examine the relationship between the Raman signals of pathogenic microorganisms and purine metabolites. A deep learning CNN model, which may be a potential novel method for pathogen identification, was successfully developed, and it showed a high accuracy rate of 99.7% in recognizing six distinct pathogenic Vibrio species within 15 min.³⁶⁴ The simulated SERS characteristic amplifier integrated an RF model, enabling the qualitative analysis of each polycyclic aromatic compound, as illustrated in Fig. 38.³⁶⁵

Fig. 38 (A) SERS probing platform for monitoring exhaled volatile organic species of gaseous aldehyde using ML-supported Raman spectroscopy. Reprinted with permission from ref. 362, Copyright 2023 American Chemical Chemistry. (B) Methodology for determining prognosis and prediction of cancer metastasis. Reprinted with permission from ref. 363, Copyright 2022 Elsevier. (C) Use of ML and portable Raman reader in the identification and quantification of pathogenic Vibrio species that cause seafood contamination. Reprinted with permission from ref. 364, Copyright 2023 Royal Society of Chemistry. (D) Flowchart of the data processing of spectra using stochastic forest chain model. Reprinted with permission from ref. 365, Copyright 2022 American Chemical Society.

In conclusion, it is anticipated that a diagnostic system combining various miniaturized microdevices such as microfluidic channels, LFA strips, or 3D microarrays, along with a portable Raman spectrophotometer, will be developed for on-site diagnosis of infectious diseases in the future. Furthermore, machine learning algorithms are expected to be embedded in the software of the developed system, enhancing the diagnostic accuracy of the portable SERS-based assay system. Finally, a collaborative research effort involving spectroscopists developing the principles of SERS-based assays, engineers creating on-site diagnostic systems, software engineers developing machine learning algorithms, and medical doctors utilizing the developed systems in clinical settings is crucial. This collaborative approach is necessary to rapidly translate SERS-based diagnostic technology from the laboratory to an approved medical research stage and ultimately to a technology that can be used in clinical settings. Through this process, the SERS-based assay platform is expected to serve as a new diagnostic platform technology, surpassing the limitations of optical measurement technologies currently used in clinical settings.

Conflicts of interest

There are no conflicts to declare.

Acknowledgements

This study was supported by the National Research Foundation of Korea (grant numbers 2019R1A2C3004375 and 2020R1A5A1018052) and the National Natural Science Foundation of China (22376216).

References

1. G. F. Blackburn, H. P. Shah, J. H. Kenten, J. Leland, R. A. Kamin, J. Link, J. Peterman, M. J. Powell, A. Shah and D. B. Talley, Clin. Chem., 1991, 37, 1534–1539.
2. W. Miao, Chem. Rev., 2008, 108, 2506–2553.
3. M. Dequaire, C. Degrand and B. Limoges, Anal. Chem., 2000, 72, 5521–5528.
4. M. Shen, Y. Zhou, J. Ye, A. A. Abdullah AL-maskri, Y. Kang, S. Zeng and S. Cai, J. Pharm. Anal., 2020, 10, 97–101.
5. V. M. Corman, O. Landt, M. Kaiser, R. Molenkamp, A. Meijer, D. K. Chu, T. Bleicker, S. Brünink, J. Schneider, M. L. Schmidt, D. G. Mulders, B. L. Haagmans, B. van der Veer, S. van den Brink, L. Wijsman, G. Goderski, J.-L. Romette, J. Ellis, M. Zambon, M. Peiris, H. Goossens, C. Reusken, M. P. Koopmans and C. Drosten, Euro Surveill., 2020, 25, 2000045.
6. A.-I. Henry, B. Sharma, M. F. Cardinal, D. Kurouski and R. P. Van Duyne, Anal. Chem., 2016, 88, 6638–6647.
7. L. Xie, K. Gong, Y. Liu and L. Zhang, Environ. Sci. Technol., 2023, 57, 25–43.
8. O. Guselnikova, H. Lim, H.-J. Kim, S. H. Kim, A. Gorbunova, M. Eguchi, P. Postnikov, T. Nakanishi, T. Asahi, J. Na and Y. Yamauchi, Small, 2022, 18, 2107182.
9. S. Wang, J. Yao, X. Lu and T. Ding, Adv. Opt. Mater., 2022, 10, 2102238.
10. R. Goel, V. Awasthi, P. Rai and S. K. Dubey, Opt. Mater., 2021, 122, 111735.
11. S. Spaziani, G. Quero, S. Managò, G. Zito, D. Terracciano, P. E. Macchia, F. Galeotti, M. Pisco, A. C. De Luca and A. Cusano, Biosens. Bioelectron., 2023, 233, 115322.
12. S. Gullace, V. Montes-García, V. Martín, D. Larios, V. Girelli Consolaro, F. Obelleiro, G. Calogero, S. Casalini and P. Samorì, Small, 2021, 17, 2100755.
13. A. Castro-Grijalba, V. Montes-García, M. J. Cordero-Ferradás, E. Coronado, J. Pérez-Juste and I. Pastoriza-Santos, ACS Sens., 2020, 5, 693–702.
14. H. D. Trinh, S. Kim, J. Park and S. Yoon, Nanoscale, 2022, 14, 17003–17012.
15. G.-S. Park, K. S. Min, H. Kwon, S. Yoon, S. Park, J.-H. Kwon, S. Lee, J. Jo, M. Kim and S. K. Kim, Adv. Mater., 2021, 33, 2100653.
16. L. T. M. Huynh, S. Kim and S. Yoon, ACS Photonics, 2022, 9, 3260–3267.
17. S. Hu, E. Elliott, A. Sánchez-Iglesias, J. Huang, C. Guo, Y. Hou, M. Kamp, E. S. A. Goerlitzer, K. Bedingfield, B. de Nijs, J. Peng, A. Demetriadou, L. M. Liz-Marzán and J. J. Baumberg, Adv. Sci., 2023, 10, 2207178.
18. S.-Y. Ding, E.-M. You, Z.-Q. Tian and M. Moskovits, Chem. Soc. Rev., 2017, 46, 4042–4076.
19. R. Zhang, Y. Zhang, Z. C. Dong, S. Jiang, C. Zhang, L. G. Chen, L. Zhang, Y. Liao, J. Aizpurua, Y. Luo, J. L. Yang and J. G. Hou, Nature, 2013, 498, 82–86.
20. B. Liu, X. Zhao, W. Jiang, D. Fu and Z. Gu, Nanoscale, 2016, 8, 17465–17471.
21. K. Saha, S. S. Agasti, C. Kim, X. Li and V. M. Rotello, Chem. Rev., 2012, 112, 2739–2779.
22. Y. C. Cao, R. Jin and C. A. Mirkin, Science, 2002, 297, 1536–1540.
23. M. McKee and D. Stuckler, Nat. Med., 2020, 26, 640–642.
24. J. Gao, Y. Yin, K. R. Myers, K. R. Lakhani and D. Wang, Nat. Commun., 2021, 12, 6188.
25. K. Kim, L. Kashefi-Kheyrabadi, Y. Joung, K. Kim, H. Dang, S. G. Chavan, M.-H. Lee and J. Choo, Sens. Actuators, B, 2021, 329, 129214.
26. S. T. Sanjay, G. Fu, M. Dou, F. Xu, R. Liu, H. Qi and X. Li, Analyst, 2015, 140, 7062–7081.
27. M. Lee, S. Lee, J. Lee, H. Lim, G. H. Seong, E. K. Lee, S.-I. Chang, C. H. Oh and J. Choo, Biosens. Bioelectron., 2011, 26, 2135–2141.
28. Y. Joung, K. Kim, S. Lee, B.-S. Chun, S. Lee, J. Hwang, S. Choi, T. Kang, M.-K. Lee, L. Chen and J. Choo, ACS Sens., 2022, 7, 3470–3480.
29. J. H. Yoon, J. Lim and S. Yoon, ACS Nano, 2012, 6, 7199–7208.
30. D. S. Grubisha, R. J. Lipert, H.-Y. Park, J. Driskell and M. D. Porter, Anal. Chem., 2003, 75, 5936–5943.
31. M. D. Porter, R. J. Lipert, L. M. Siperko, G. Wang and R. Narayanan, Chem. Soc. Rev., 2008, 37, 1001–1011.
32. S. P. Ng, G. Qiu, N. Ding, X. Lu and C.-M. L. Wu, Biosens. Bioelectron., 2017, 89, 468–476.
33. T. Demeritte, B. P. Viraka Nellore, R. Kanchanapally, S. S. Sinha, A. Pramanik, S. R. Chavva and P. C. Ray, ACS Appl. Mater. Interfaces, 2015, 7, 13693–13700.
34. Z. Cheng, N. Choi, R. Wang, S. Lee, K. C. Moon, S.-Y. Yoon, L. Chen and J. Choo, ACS Nano, 2017, 11, 4926–4933.
35. J. Park, M. Hwang, B. Choi, H. Jeong, J. Jung, H. K. Kim, S. Hong, J. Park and Y. Choi, Anal. Chem., 2017, 89, 6695–6701.
36. S. Feng, D. Lin, J. Lin, B. Li, Z. Huang, G. Chen, W. Zhang, L. Wang, J. Pan, R. Chen and H. Zeng, Analyst, 2013, 138, 3967–3974.
37. Y. Zhou, Y. Lu, Y. Liu, X. Hu and H. Chen, Biosens. Bioelectron., 2023, 228, 115231.
38. P. Zhou, S. Cheng, Q. Li, Y. Pang and R. Xiao, Chem. Eng. J., 2023, 471, 144514.
39. E. Stepula, X.-P. Wang, S. Srivastav, M. Koenig, J. Levermann, S. Kasimir-Bauer and S. Schluecker, ACS Appl. Mater. Interfaces, 2020, 12, 32321–32327.
40. W. Wang, Y. Li, A. Nie, G.-C. Fan and H. Han, Analyst, 2021, 146, 848–854.
41. J. Yao, Z. Jin and Y. Zhao, ACS Appl. Mater. Interfaces, 2023, 15, 10240–10249.
42. V. Karunakaran, V. N. Saritha, A. N. Ramya, V. P. Murali, K. G. Raghu, K. Sujathan and K. K. Maiti, Anal. Chem., 2021, 93, 11140–11150.
43. J. H. Yu, M. S. Jeong, E. O. Cruz, I. S. Alam, S. K. Tumbale, A. Zlitni, S. Y. Lee, Y. I. Park, K. Ferrara, S.-H. Kwon, S. S. Gambhir and J. Rao, ACS Nano, 2023, 17, 2554–2567.
44. R. E. Jr. Wilson, R. O’Connor, C. E. Gallops, E. A. Kwizera, B. Noroozi, B. I. Morshed, Y. Wang and X. Huang, ACS Appl. Mater. Interfaces, 2020, 12, 47220–47232.
45. E. Stepula, M. König, X.-P. Wang, J. Levermann, T. Schimming, S. Kasimir-Bauer, B. Schilling and S. Schlücker, J. Biophotonics, 2020, 13, e201960034.
46. T. Fukuoka, T. Yasunaga, K. Namura, M. Suzuki and A. Yamaguchi, Adv. Mater. Interfaces, 2023, 10, 2300157.
47. N. Choi, H. Dang, A. Das, M. S. Sim, I. Y. Chung and J. Choo, Biosens. Bioelectron., 2020, 164, 112326.
48. J. Ham, B. J. Yun and W.-G. Koh, Sens. Actuators, B, 2021, 341, 129989.
49. E. Y. Hwang, J. H. Lee, M. J. Kang and D. W. Lim, J. Mater. Chem. B, 2023, 11, 1692–1704.
50. J. Wang, K. M. Koo and M. Trau, Anal. Chem., 2022, 94, 14906–14916.
51. N. Choi, M. Adanalic, A. Dankov, R. Grzeschik, M. Hechler and S. Schlücker, J. Raman Spectrosc., 2023, 54, 929–939.
52. Z. Zhang and J. Wang, Analyst, 2021, 146, 7005–7020.
53. H. Li, X. Huang, Md Mehedi Hassan, M. Zuo, X. Wu, Y. Chen and Q. Chen, Microchem. J., 2020, 154, 104563.
54. M. Chen, B. Wang, J. Wang, H. Liu, Z. Chen, X. Xu and X. Zhao, Nanomaterials, 2022, 12, 1626.
55. O. J. Achadu, F. Abe, T. Suzuki and E. Y. Park, ACS Appl. Mater. Interfaces, 2020, 12, 43522–43534.
56. Z. Du, Y. Qi, J. He, D. Zhong and M. Zhou, Wiley Interdiscip. Rev.: Nanomed. Nanobiotechnol., 2021, 13, e1672.
57. A. Li, L. Tang, D. Song, S. Song, W. Ma, L. Xu, H. Kuang, X. Wu, L. Liu, X. Chen and C. Xu, Nanoscale, 2016, 8, 1873–1878.
58. W. Shen, C. Wang, S. Zheng, B. Jiang, J. Li, Y. Pang, C. Wang, R. Hao and R. Xiao, J. Hazard. Mater., 2022, 437, 129347.
59. W. Teng, J. Zhao, Q. Li, P. Shi, J. Zhang, M. Yan and S. Zhang, Sens. Diagn., 2023, 2, 132–139.
60. Y. Ding, H. Shang, X. Wang and L. Chen, Analyst, 2020, 145, 6079–6084.
61. Z. Hu, X. Zhou, J. Duan, X. Wu, J. Wu, P. Zhang, W. Liang, J. Guo, H. Cai, P. Sun, H. Zhou and Z. Jiang, Sens. Actuators, B, 2021, 334, 129640.
62. X. Bai, W. Gong, Y. Guo, D. Zhu and X. Li, Analyst, 2023, 148, 2327–2334.
63. Y. Chen, W. Cheng, Y. Yang, D. Wu, Y. Zhang and X. Tang, Sens. Actuators, B, 2023, 393, 134329.
64. A. Su, Y. Liu, X. Cao, J. Zhao, W. Xu, C. Liang, P. Li and S. Xu, Anal. Chem., 2023, 95, 5927–5936.
65. S.-M. You, K. Luo, J.-Y. Jung, K.-B. Jeong, E.-S. Lee, M.-H. Oh and Y.-R. Kim, ACS Appl. Mater. Interfaces, 2020, 12, 18292–18300.
66. T. Xie, D. Xu, Y. Shang, Y. Li, Y. Gu, G. Yang and L. Qu, Sens. Actuators, B, 2023, 375, 132897.
67. L. Ngo, W. Zhang, S. S. T. Hnit and Y. Wang, Analyst, 2023, 148, 3074–3086.
68. E. Witkowska, A. M. Lasica, K. Nicinski, J. Potempa and A. Kaminska, ACS Sens., 2021, 6, 1621–1635.
69. C. Tian, L. Zhao, G. Qi, J. Zhu and S. Zhang, Sens. Actuators, B, 2022, 371, 132445.
70. E. J. Kim, H. Kim, E. Park, T. Kim, D. R. Chung, Y.-M. Choi and M. Kang, Anal. Chem., 2021, 93, 3677–3685.
71. W. Zhang, L. Wang, D. Li, D. H. Campbell, B. J. Walsh, N. H. Packer, Q. Dong, E. Wang and Y. Wang, Anal. Methods, 2022, 14, 2255–2265.
72. C. Wei, R. Sun, Y. Jiang, X. Guo, Y. Ying, Y. Wen, H. Yang and Y. Wu, Sens. Actuators, B, 2021, 345, 130410.
73. P. R. Potluri, V. K. Rajendran, A. Sunna and Y. Wang, Analyst, 2020, 145, 2789–2794.
74. H. Li, W. Geng, Z. Qi, W. Ahmad, S. A. Haruna and Q. Chen, Biosens. Bioelectron., 2023, 226, 115122.
75. K. Wang, Z. Chen, Y. Li and Y. Zhang, Mater. Chem. Front., 2023, 7, 1624–1632.
76. Y. Lin, P. Qi, J. Liu, Y. Zhuo, C. Cai, H. Zhang, X. Li, X. Liu, L. Chen, S. Luo and X. Tian, Adv. Mater. Technol., 2023, 8, 2300367.
77. S. J. Yang, J. U. Lee, M. J. Jeon and S. J. Sim, Anal. Chim. Acta, 2022, 1195, 339445.
78. A. Garg, E. Mejia, W. Nam, P. Vikesland and W. Zhou, Small, 2022, 18, 2204517.
79. L. Zhang, C. Meng, G. Zhang, D. Bai, F. Gao, L. Xu, W. Zhang, T. Mei and J. Zhao, Opt. Express, 2021, 29, 28086–28095.
80. C.-F. Shi, Z.-Q. Li, C. Wang, J. Li and X.-H. Xia, Biosens. Bioelectron., 2021, 178, 113040.
81. D. Scherrer, D. Vogel, U. Drechsler, A. Olziersky, C. Sparr, M. Mayor and E. Lortscher, Angew. Chem., Int. Ed., 2021, 60, 17981–17988.
82. Y. Han, L. Qiang, Y. Gao, J. Gao, Q. He, H. Liu, L. Han and Y. Zhang, Appl. Surf. Sci., 2021, 541, 148456.
83. M. Imanbekova, A. M. Saridag, M. Kahraman, J. Liu, H. Caglayan and S. Wachsmann-Hogiu, ACS Appl. Opt. Mater., 2023, 1, 329–339.
84. X. Gui, J. Xie, W. Wang, B. Hou, J. Min, P. Zhai, L. Cai, J. Tang, R. Zhu, X. Wu, J. Duan, J. Liu and H. Yao, ACS Appl. Nano Mater., 2023, 6, 11049–11060.
85. I. A. Brezeştean, D. Marconi, N. E. Dina, M. Suciu and A. Colniţă, Chemosensors, 2023, 11, 441.
86. F. Zhang, X. Yang, D. Wang, L. Yu, Y. Hu, A. Zhu, C. Zhang, Y. Zheng, T. Yin, Y. Zhang and W. Xie, J. Raman Spectrosc., 2022, 53, 1227–1237.
87. J. Yang, F. I. T. Petrescu, Y. Li, D. Song and G. Shi, Nanomaterials, 2022, 12, 3127.
88. H. Park, J. Park, G. Lee, W. Kim and J. Park, Materials, 2022, 15, 3454.
89. Y. Liu, L. Zhang, X. Liu, Y. Zhang, Y. Yan and Y. Zhao, Spectrochim. Acta, Part A, 2022, 270, 120803.
90. R. Rastogi, H. Arianfard, D. Moss, S. Juodkazis, P.-M. Adam and S. Krishnamoorthy, ACS Appl. Mater. Interfaces, 2021, 13, 9113–9121.
91. Y. Lu, B. Lei, Q. Zhao, X. Yang, Y. Wei, T. Xiao, S. Zhu, Y. Ouyang, H. Zhang and W. Cai, ACS Omega, 2023, 8, 29836–29846.
92. Z. Liao, X. Hu, J. Chu, Y. Wu, T. Ji, H. Zhu, W. Peng, J. Wang, H. Zhao and M. Chen, Opt. Express, 2023, 31, 25230–25244.
93. Y. Wu, H. Dang, S.-G. Park, L. Chen and J. Choo, Biosens. Bioelectron., 2022, 197, 113736.
94. S. Mehla, A. Kandjani, V. Coyle, C. J. Harrison, M. X. Low, R. B. Kaner, Y. Sabri and S. K. Bhargava, ACS Appl. Nano Mater., 2022, 5, 1873–1890.
95. C. Zhu, Q. Zhao, D. Huo, X. Hu and X. Wang, Mater. Chem. Phys., 2021, 263, 124388.
96. J. Li, A. Wuethrich, A. A. I. Sina, H.-H. Cheng, Y. Wang, A. Behren, P. N. Mainwaring and M. Trau, Nat. Commun., 2021, 12, 1087.
97. J. Bar, A. de Barros, D. H. S. de Camargo, M. P. Pereira, L. Merces, F. M. Shimizu, F. A. Sigoli, C. C. B. Bufon and I. O. Mazali, ACS Appl. Mater. Interfaces, 2021, 13, 36482–36491.
98. Y. Chen, H. Yin, D. Sikdar, H. Liu, Q. Zhu, G. Yao, H. Qi and N. Gu, Adv. Mater. Interfaces, 2020, 7, 2000248.
99. J. Chen, J. Yang, W. Chen, Y. Wang, G. Song, H. He, H. Wang, P. Li and G. P. Wang, J. Colloid Interface Sci., 2022, 608, 2266–2277.
100. M. Wan, H. Zhao, L. Peng, Y. Zhao and L. Sun, ACS Appl. Bio Mater., 2021, 4, 6549–6557.
101. Z. Guo, J. Zhao, H. He and Z. Liu, Chem. – Asian J., 2022, 17, e202200844.
102. Y. Wang, M. Guan, F. Mi, P. Geng and G. Chen, Anal. Chim. Acta, 2023, 1272, 341523.
103. J. E. L. Villa, I. Garcia, D. Jimenez de Aberasturi, V. Pavlov, M. D. P. T. Sotomayor and L. M. Liz-Marzan, Biosens. Bioelectron., 2020, 165, 112418.
104. R. Wang, H. Chon, S. Lee, Z. Cheng, S. H. Hong, Y. H. Yoon and J. Choo, ACS Appl. Mater. Interfaces, 2016, 8, 10665–10672.
105. H. Chon, S. Lee, S.-Y. Yoon, S.-I. Chang, D. W. Lim and J. Choo, Chem. Commun., 2011, 47, 12515–12517.
106. H. Chon, S. Lee, S.-Y. Yoon, E. K. Lee, S.-I. Chang and J. Choo, Chem. Commun., 2014, 50, 1058–1060.
107. Z. Wang, S. Zong, Y. Wang, N. Li, L. Li, J. Lu, Z. Wang, B. Chen and Y. Cui, Nanoscale, 2018, 10, 9053–9062.
108. T. Yang and J. Jiang, Small, 2016, 12, 4980–4985.
109. W. Zhang, L. Jiang, R. J. Diefenbach, D. H. Campbell, B. J. Walsh, N. H. Packer and Y. Wang, ACS Sens., 2020, 5, 764–771.
110. B. Zou, S. Lou, J. Wang, S. Zhou and Y. Wang, Anal. Chem., 2022, 94, 11557–11563.
111. H. T. Ngo, N. Gandra, A. M. Fales, S. M. Taylor and T. Vo-Dinh, Biosens. Bioelectron., 2016, 81, 8–14.
112. M. S. Litwin and H.-J. Tan, JAMA, 2017, 317, 2532–2542.
113. P.-C. Guan, H. Zhang, Z.-Y. Li, S.-S. Xu, M. Sun, X.-M. Tian, Z. Ma, J.-S. Lin, M.-M. Gu, H. Wen, F.-L. Zhang, Y.-J. Zhang, G.-J. Yu, C. Yang, Z.-X. Wang, Y. Song and J.-F. Li, Anal. Chem., 2022, 94, 17795–17802.
114. D. Antoine, M. Mohammadi, M. Vitt, J. M. Dickie, S. S. Jyoti, M. A. Tilbury, P. A. Johnson, K. E. Wawrousek and J. G. Wall, ACS Sens., 2022, 7, 866–873.
115. H. Cha, H. Kim, Y. Joung, H. Kang, J. Moon, H. Jang, S. Park, H.-J. Kwon, I.-C. Lee, S. Kim, D. Yong, S.-W. Yoon, S.-G. Park, K. Guk, E.-K. Lim, H. G. Park, J. Choo, J. Jung and T. Kang, Biosens. Bioelectron., 2022, 202, 114008.
116. A. Y. Mironenko, M. V. Tutov, A. K. Chepak and S. Y. Bratskaya, Anal. Chim. Acta, 2022, 1229, 340388.
117. M. Mohammadi, D. Antoine, M. Vitt, J. M. Dickie, S. Sultana Jyoti, J. G. Wall, P. A. Johnson and K. E. Wawrousek, Anal. Chim. Acta, 2022, 1229, 340290.
118. V.-T. Nguyen, S. Song, S. Park and C. Joo, Biosens. Bioelectron., 2020, 152, 112015.
119. F. Li, M. You, S. Li, J. Hu, C. Liu, Y. Gong, H. Yang and F. Xu, Biotechnol. Adv., 2020, 39, 107442.
120. T. Mahmoudi, M. de la Guardia, B. Shirdel, A. Mokhtarzadeh and B. Baradaran, TrAC, Trends Anal. Chem., 2019, 116, 13–30.
121. E. B. Bahadır and M. K. Sezgintürk, TrAC, Trends Anal. Chem., 2016, 82, 286–306.
122. M. Shen, N. Li, Y. Lu, J. Cheng and Y. Xu, Lab Chip, 2020, 20, 2626–2634.
123. L. C. Brazaca, J. R. Moreto, A. Martín, F. Tehrani, J. Wang and V. Zucolotto, ACS Nano, 2019, 13, 13325–13332.
124. A. V. Ivanov, I. V. Safenkova, A. V. Zherdev and B. B. Dzantiev, Talanta, 2020, 210, 120616.
125. T. T. Le, P. Chang, D. J. Benton, J. W. McCauley, M. Iqbal and A. E. G. Cass, Anal. Chem., 2017, 89, 6781–6786.
126. J. C. Phan, J. Pettitt, J. S. George, L. S. Fakoli III, F. M. Taweh, S. L. Bateman, R. S. Bennett, S. L. Norris, D. A. Spinnler, G. Pimentel, P. K. Sahr, F. K. Bolay and R. J. Schoepp, J. Infect. Dis., 2016, 214, S222–S228.
127. L. Khelifa, Y. Hu, N. Jiang and A. K. Yetisen, Lab Chip, 2022, 22, 2451–2475.
128. M. L. Dufau, Annu. Rev. Physiol., 1998, 60, 461–496.
129. A. P. Espejo, Y. Akgun, A. F. Al Mana, Y. Tjendra, N. C. Millan, C. Gomez-Fernandez and C. Cray, Am. J. Clin. Pathol., 2020, 154, 293–304.
130. Z. Chen, Z. Zhang, X. Zhai, Y. Li, L. Lin, H. Zhao, L. Bian, P. Li, L. Yu, Y. Wu and G. Lin, Anal. Chem., 2020, 92, 7226–7231.
131. R. Aman, A. Mahas and M. Mahfouz, ACS Synth. Biol., 2020, 9, 1226–1233.
132. Z. Qin, R. Peng, I. K. Baravik and X. Liu, Matter, 2020, 3, 628–651.
133. Y. Zhou, Y. Wu, L. Ding, X. Huang and Y. Xiong, TrAC, Trends Anal. Chem., 2021, 145, 116452.
134. A. N. Baker, G. W. Hawker-Bond, P. G. Georgiou, S. Dedola, R. A. Field and M. I. Gibson, Chem. Soc. Rev., 2022, 51, 7238–7259.
135. K. Dighe, P. Moitra, M. Alafeef, N. Gunaseelan and D. Pan, Biosens. Bioelectron., 2022, 200, 113900.
136. R. Gupta, P. Gupta, S. Wang, A. Melnykov, Q. Jiang, A. Seth, Z. Wang, J. J. Morrissey, I. George, S. Gandra, P. Sinha, G. A. Storch, B. A. Parikh, G. M. Genin and S. Singamaneni, Nat. Biomed. Eng., 2023, 7, 1556–1570.
137. N. Higashi-Kuwata, K. Tsuji, H. Hayashi, H. Bulut, M. Kiso, M. Imai, H. Ogata-Aoki, T. Ishii, T. Kobayakawa, K. Nakano, N. Takamune, N. Kishimoto, S. Hattori, D. Das, Y. Uemura, Y. Shimizu, M. Aoki, K. Hasegawa, S. Suzuki, A. Nishiyama, J. Saruwatari, Y. Shimizu, Y. Sukenaga, Y. Takamatsu, K. Tsuchiya, K. Maeda, K. Yoshimura, S. Iida, S. Ozono, T. Suzuki, T. Okamura, S. Misumi, Y. Kawaoka, H. Tamamura and H. Mitsuya, Nat. Commun., 2023, 14, 1076.
138. R. Mothes, A. Pascual-Reguant, R. Koehler, J. Liebeskind, A. Liebheit, S. Bauherr, L. Philipsen, C. Dittmayer, M. Laue, R. von Manitius, S. Elezkurtaj, P. Durek, F. Heinrich, G. A. Heinz, G. M. Guerra, B. Obermayer, J. Meinhardt, J. Ihlow, J. Radke, F. L. Heppner, P. Enghard, H. Stockmann, T. Aschman, J. Schneider, V. M. Corman, L. E. Sander, M.-F. Mashreghi, T. Conrad, A. C. Hocke, R. A. Niesner, H. Radbruch and A. E. Hauser, Nat. Commun., 2023, 14, 791.
139. K. Jibin, J. S. Prasad, G. Saranya, S. J. Shenoy, K. K. Maiti and R. S. Jayasree, Biomater. Sci., 2020, 8, 3381–3391.
140. Z. Li, L. Leustean, F. Inci, M. Zheng, U. Demirci and S. Wang, Biotechnol. Adv., 2019, 37, 107440.
141. Z. Huang, S. Siddhanta, G. Zheng, T. Kickler and I. Barman, Angew. Chem., Int. Ed., 2020, 59, 5972–5978.
142. S. Kim, T. G. Kim, S. H. Lee, W. Kim, A. Bang, S. W. Moon, J. Song, J.-H. Shin, J. S. Yu and S. Choi, ACS Appl. Mater. Interfaces, 2020, 12, 7897–7904.
143. X. Fu, J. Wen, J. Li, H. Lin, Y. Liu, X. Zhuang, C. Tian and L. Chen, Nanoscale, 2019, 11, 15530–15536.
144. J. Jeon, S. H. Lee, Y. Joung, K. Kim, N. Choi and J. Choo, Sens. Actuators, B, 2020, 321, 128521.
145. Y. Pang, Q. Li, C. Wang, Z. Shuai, Z. Sun and R. Xiao, Chem. Eng. J., 2022, 429, 132109.
146. R. Wang, K. Kim, N. Choi, X. Wang, J. Lee, J. H. Jeon, G. Rhie and J. Choo, Sens. Actuators, B, 2018, 270, 72–79.
147. N. Panchal, V. Jain, R. Elliott, Z. Flint, P. Worsley, C. Duran, T. Banerjee and S. Santra, Anal. Chem., 2022, 94, 13968–13977.
148. J. Hwang, S. Lee and J. Choo, Nanoscale, 2016, 8, 11418–11425.
149. S. H. Lee, J. Hwang, K. Kim, J. Jeon, S. Lee, J. Ko, J. Lee, M. Kang, D. R. Chung and J. Choo, Anal. Chem., 2019, 91, 12275–12282.
150. K. Xu, R. Zhou, K. Takei and M. Hong, Adv. Sci., 2019, 6, 1900925.
151. X. Fu, Z. Cheng, J. Yu, P. Choo, L. Chen and J. Choo, Biosens. Bioelectron., 2016, 78, 530–537.
152. X. Wang, N. Choi, Z. Cheng, J. Ko, L. Chen and J. Choo, Anal. Chem., 2017, 89, 1163–1169.
153. K. Kim, D. K. Han, N. Choi, S. H. Kim, Y. Joung, K. Kim, N. T. Ho, S.-W. Joo and J. Choo, Anal. Chem., 2021, 93, 6673–6681.
154. S. Liu, X. Luo, R. Shu, Y. Liao, L. Dou, T. Bu, S. Wang, Y. Li, J. Sun, D. Zhang, M. Zhu and J. Wang, Small, 2022, 18, 2204859.
155. X. Huang, L. Chen, W. Zhi, R. Zeng, G. Ji, H. Cai, J. Xu, J. Wang, S. Chen, Y. Tang, J. Zhang, H. Zhou and P. Sun, Anal. Chem., 2023, 95, 13101–13112.
156. M. M. Gong and D. Sinton, Chem. Rev., 2017, 117, 8447–8480.
157. C. Wang, M. Liu, Z. Wang, S. Li, Y. Deng and N. He, Nano Today, 2021, 37, 101092.
158. H.-K. Huang, H.-W. Cheng, C.-C. Liao, S.-J. Lin, Y.-Z. Chen, J.-K. Wang, Y.-L. Wang and N.-T. Huang, Lab Chip, 2020, 20, 2520–2528.
159. A. Kamińska, E. Witkowska, K. Winkler, I. Dzięcielewski, J. L. Weyher and J. Waluk, Biosens. Bioelectron., 2015, 66, 461–467.
160. K. K. Reza, S. Dey, A. Wuethrich, J. Wang, A. Behren, F. Antaw, Y. Wang, A. A. I. Sina and M. Trau, ACS Nano, 2021, 15, 11231–11243.
161. T. Park, S. Lee, G. H. Seong, J. Choo, E. K. Lee, Y. S. Kim, W. H. Ji, S. Y. Hwang, D.-G. Gweon and S. Lee, Lab Chip, 2005, 5, 437–442.
162. S. Park, C. Su Jeon, N. Choi, J.-I. Moon, K. Min Lee, S. Hyun Pyun, T. Kang and J. Choo, Chem. Eng. J., 2022, 446, 137085.
163. M. Lafuente, F. Almazan, E. Bernad, I. Florea, R. Arenal, M. A. Urbiztondo, R. Mallada and M. P. Pina, Lab Chip, 2023, 23, 3160–3171.
164. 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.
165. S. Sevim, C. Franco, X.-Z. Chen, A. Sorrenti, D. Rodriguez-San-Miguel, S. Pane, A. J. deMello and J. Puigmarti-Luis, Adv. Sci., 2020, 7, 1903172.
166. A. Jain, A. Graveline, A. Waterhouse, A. Vernet, R. Flaumenhaft and D. E. Ingber, Nat. Commun., 2016, 7, 10176.
167. T. G. G. Alicia, C. Yang, Z. Wang and N.-T. Nguyen, Lab Chip, 2016, 16, 368–376.
168. H. Chon, C. Lim, S.-M. Ha, Y. Ahn, E. K. Lee, S.-I. Chang, G. H. Seong and J. Choo, Anal. Chem., 2010, 82, 5290–5295.
169. M. Lee, K. Lee, K. H. Kim, K. W. Oh and J. Choo, Lab Chip, 2012, 12, 3720–3727.
170. N. Choi, K. Lee, D. W. Lim, E. K. Lee, S.-I. Chang, K. W. Oh and J. Choo, Lab Chip, 2012, 12, 5160–5167.
171. E. Chung, R. Gao, J. Ko, N. Choi, D. W. Lim, E. K. Lee, S.-I. Chang and J. Choo, Lab Chip, 2013, 13, 260–266.
172. L. Ngo, L. Q. A. Pham, A. Tukova, A. Hassanzadeh-Barforoushi, W. Zhang and Y. Wang, Lab Chip, 2023, 23, 2899–2921.
173. H. Song, D. L. Chen and R. F. Ismagilov, Angew. Chem., Int. Ed., 2006, 45, 7336–7356.
174. M. Srisa-Art, A. J. deMello and J. B. Edel, Phys. Rev. Lett., 2008, 101, 014502.
175. X. Niu, S. Gulati, J. B. Edel and A. J. deMello, Lab Chip, 2008, 8, 1837–1841.
176. N. Bremond, A. R. Thiam and J. Bibette, Phys. Rev. Lett., 2008, 100, 024501.
177. S.-Y. Teh, R. Lin, L.-H. Hung and A. P. Lee, Lab Chip, 2008, 8, 198–220.
178. N. Choi, J. Lee, J. Ko, J. H. Jeon, G. Rhie, A. J. deMello and J. Choo, Anal. Chem., 2017, 89, 8413–8420.
179. J. Jeon, N. Choi, H. Chen, J.-I. Moon, L. Chen and J. Choo, Lab Chip, 2019, 19, 674–681.
180. L. Morelli, L. Serioli, F. A. Centorbi, C. B. Jendresen, M. Matteucci, O. Ilchenko, D. Demarchi, A. T. Nielsen, K. Zor and A. Boisen, Lab Chip, 2018, 18, 869–877.
181. R. Gorkin, J. Siegrist, M. Amasia, B. S. Lee, J.-M. Park, J. Kim, H. Kim, M. Madou and Y.-K. Cho, Lab Chip, 2010, 10, 1758–1773.
182. D. A. Duford, Y. Xi and E. D. Salin, Anal. Chem., 2013, 85, 7834–7841.
183. D. G. Rackus, M. D. M. Dryden, J. Lamanna, A. Zaragoza, B. Lam, S. O. Kelley and A. R. Wheeler, Lab Chip, 2015, 15, 3776–3784.
184. W. Liu, Z. Wang, Z. Liu, J. Chen, L. Shi, L. Huang, Y. Liu, S. Cui and X. He, ACS Sens., 2023, 8, 1733–1741.
185. Y. Wang, Q. Ruan, Z.-C. Lei, S.-C. Lin, Z. Zhu, L. Zhou and C. Yang, Anal. Chem., 2018, 90, 5224–5231.
186. A. Das, S. Fehse, M. Polack, R. Panneerselvam and D. Belder, Anal. Chem., 2023, 95, 1262–1272.
187. H. Chon, S. Lee, S. W. Son, C. H. Oh and J. Choo, Anal. Chem., 2009, 81, 3029–3034.
188. Q. Xiong, C. Y. Lim, J. Ren, J. Zhou, K. Pu, M. B. Chan-Park, H. Mao, Y. C. Lam and H. Duan, Nat. Commun., 2018, 9, 1743.
189. K. Wu, T. Li, M. S. Schmidt, T. Rindzevicius, A. Boisen and S. Ndoni, Adv. Funct. Mater., 2018, 28, 1704818.
190. X. Zhang, X. Zhang, C. Luo, Z. Liu, Y. Chen, S. Dong, C. Jiang, S. Yang, F. Wang and X. Xiao, Small, 2019, 15, 1805516.
191. G. Cossio and E. T. Yu, Nano Lett., 2020, 20, 5090–5096.
192. J.-A. Huang, Y.-Q. Zhao, X.-J. Zhang, L.-F. He, T.-L. Wong, Y.-S. Chui, W.-J. Zhang and S.-T. Lee, Nano Lett., 2013, 13, 5039–5045.
193. Z. Xu, J. Jiang, X. Wang, K. Han, A. Ameen, I. Khan, T.-W. Chang and G. L. Liu, Nanoscale, 2016, 8, 6162–6172.
194. X. Xu, H. Li, D. Hasan, R. S. Ruoff, A. X. Wang and D. L. Fan, Adv. Funct. Mater., 2013, 23, 4332–4338.
195. L. Zhang, R. Hao, D. Zhang, H. You, Y. Dai, W. Liu and J. Fang, Anal. Chem., 2020, 92, 9838–9846.
196. Z. Lao, Y. Zheng, Y. Dai, Y. Hu, J. Ni, S. Ji, Z. Cai, Z. J. Smith, J. Li, L. Zhang, D. Wu and J. Chu, Adv. Funct. Mater., 2020, 30, 1909467.
197. Y. Ma, D. Sikdar, A. Fedosyuk, L. Velleman, D. J. Klemme, S.-H. Oh, A. R. J. Kucernak, A. A. Kornyshev and J. B. Edel, ACS Nano, 2020, 14, 328–336.
198. S. Lee, M. Kim and S. Yoon, Chem. Commun., 2019, 55, 1466–1469.
199. P. Kumar, R. Khosla, M. Soni, D. Deva and S. K. Sharma, Sens. Actuators, B, 2017, 246, 477–486.
200. L. Petti, R. Capasso, M. Rippa, M. Pannico, P. La Manna, G. Peluso, A. Calarco, E. Bobeico and P. Musto, Vib. Spectrosc., 2016, 82, 22–30.
201. J. Cai, Z. Wang, M. Wang and D. Zhang, Appl. Surf. Sci., 2021, 541, 148374.
202. G. Su, P. Hu, J. Hu, X. Wang, G. Wu, Y. Shang, P. Zhan, F. Liu and W. Wu, J. Raman Spectrosc., 2023, 54, 6–12.
203. C. Zhang, P. Yi, L. Peng, X. Lai, J. Chen, M. Huang and J. Ni, Sci. Rep., 2017, 7, 39814.
204. P. Zhang, G. Liu, S. Feng, X. Zhou, W. Xu and W. Cai, Nanotechnology, 2020, 31, 035303.
205. J. H. Shin, H. G. Kim, G. M. Baek, R. Kim, S. Jeon, J. H. Mun, H.-B.-R. Lee, Y. S. Jung, S. O. Kim, K. N. Kim and G. Y. Yeom, RSC Adv., 2016, 6, 70756–70762.
206. P. Sha, Q. Su, P. Dong, T. Wang, C. Zhu, W. Gao and X. Wu, RSC Adv., 2021, 11, 27107–27114.
207. T. Zhu, Y. Sun, C. Li, Y. Xia, G. Wang, W. Lu, M. Shao, B. Man and C. Yang, Opt. Express, 2021, 29, 1360.
208. M. Zhang, H. Sun, X. Chen, J. Yang, L. Shi, T. Chen, Z. Bao, J. Liu and Y. Wu, ACS Sens., 2019, 4, 1670–1681.
209. A. Kaminska, I. Dziecielewski, J. L. Weyher, J. Waluk, S. Gawinkowski, V. Sashuk, M. Fialkowski, M. Sawicka, T. Suski, S. Porowski and R. Holyst, J. Mater. Chem., 2011, 21, 8662–8669.
210. W. Li, X. Lu, R. Yang, F. Liang, W. Chen, Z. Xie, J. Zheng, J. Zhu, Y. Huang, W. Yue, L. Li and Y. Su, Appl. Surf. Sci., 2022, 597, 153654.
211. L. Wu, W. Wang, W. Zhang, H. Su, Q. Liu, J. Gu, T. Deng and D. Zhang, NPG Asia Mater., 2018, 10, e462–e462.
212. X. Liu, Y. Shao, Y. Tang and K.-F. Yao, Sci. Rep., 2014, 4, 5835.
213. E. Cara, L. Mandrile, F. Ferrarese Lupi, A. M. Giovannozzi, M. Dialameh, C. Portesi, K. Sparnacci, N. De Leo, A. M. Rossi and L. Boarino, Sci. Rep., 2018, 8, 11305.
214. J. Jiang, Z. Xu, A. Ameen, F. Ding, G. Lin and G. L. Liu, Nanotechnology, 2016, 27, 385205.
215. J. Yang, J. Li, Z. Du, Q. Gong, J. Teng and M. Hong, Sci. Rep., 2014, 4, 6657.
216. A. K. Verma, R. Das and R. K. Soni, MRS Adv., 2017, 2, 2379–2384.
217. Z. Zhu, X. Shi, Y. Feng, M. He, C. Ye, H. Zhou, M. Zhang, W. Zhang, J. Li and C. Jiang, Spectrochim. Acta, Part A, 2023, 288, 122211.
218. T. Lee, S. Kwon, S. Jung, H. Lim and J.-J. Lee, Nano Res., 2019, 12, 2554–2558.
219. P. K. Jain, W. Huang and M. A. El-Sayed, Nano Lett., 2007, 7, 2080–2088.
220. Z. Liu, Nat. Commun., 2017, 8, 14910.
221. J. Xiang, Y. Wang, Y. Wu, H. Fang, L. Shui, Z. Liu and T. Ding, ACS Appl. Nano Mater., 2021, 4, 11644–11650.
222. Y. Zhao, Y.-L. Zhang, J.-A. Huang, Z. Zhang, X. Chen and W. Zhang, J. Mater. Chem. A, 2015, 3, 6408–6413.
223. Q. Liu, K. Vanmol, S. Lycke, J. Van Erps, P. Vandenabeele, H. Thienpont and H. Ottevaere, RSC Adv., 2020, 10, 14274–14282.
224. C. Zhu, Q. Zhao, G. Meng, X. Wang, X. Hu, F. Han and Y. Lei, Nanotechnology, 2020, 31, 205303.
225. S. Lee, A. Ongko, H. Y. Kim, S.-G. Yim, G. Jeon, H. J. Jeong, S. Lee, M. Kwak and S. Y. Yang, Nanotechnology, 2016, 27, 315301.
226. M. Zheng, X. Zhu, Y. Chen, Q. Xiang and H. Duan, Nanotechnology, 2017, 28, 045303.
227. P. Wang, O. Liang, W. Zhang, T. Schroeder and Y.-H. Xie, Adv. Mater., 2013, 25, 4918–4924.
228. X. Zhang, Z. Dai, S. Si, X. Zhang, W. Wu, H. Deng, F. Wang, X. Xiao and C. Jiang, Small, 2017, 13, 1603347.
229. J.-E. Lee, C. Park, K. Chung, J. W. Lim, F. Marques Mota, U. Jeong and D. H. Kim, Nanoscale, 2018, 10, 4105–4112.
230. C. Zhu, G. Meng, Q. Huang, X. Wang, Y. Qian, X. Hu, H. Tang and N. Wu, Nano Res., 2015, 8, 957–966.
231. E. H. Koh, J.-Y. Moon, S.-Y. Kim, W.-C. Lee, S.-G. Park, D.-H. Kim and H. S. Jung, Analyst, 2021, 146, 305–314.
232. Q. Zhao, H. Hilal, J. Kim, W. Park, M. Haddadnezhad, J. Lee, W. Park, J.-W. Lee, S. Lee, I. Jung and S. Park, J. Am. Chem. Soc., 2022, 144, 13285–13293.
233. X. Zhao, S. Campbell, G. Q. Wallace, A. Claing, C. G. Bazuin and J.-F. Masson, ACS Sens., 2020, 5, 2155–2167.
234. A. B. Beyene, W.-N. Su, H.-C. Tsai, W. A. Tegegne, C.-H. Chen, C.-C. Huang, D. Mares, V. Prajzler, W.-H. Huang and B. J. Hwang, ACS Appl. Nano Mater., 2022, 5, 11567–11576.
235. Y. Li, L. Feng, J. Li, X. Li, J. Chen, L. Wang, D. Qi, X. Liu and G. Shi, Sens. Actuators, B, 2021, 330, 129357.
236. X. Zhao, X. Luo, C. G. Bazuin and J.-F. Masson, ACS Appl. Mater. Interfaces, 2020, 12, 55349–55361.
237. X. Liu, S. Yang, X. Lyu, S. Liu, Y. Wang, Y. Li, B. Wang, W. Chen, W. Wang, J. Guo and X. Ma, Anal. Chem., 2021, 93, 16628–16637.
238. A. Das, N. Gupta, A. K. Agrawal and A. Dhawan, RSC Adv., 2022, 12, 9645–9652.
239. X. Liu, J. Ma, P. Jiang, J. Shen, R. Wang, Y. Wang and G. Tu, ACS Appl. Mater. Interfaces, 2020, 12, 45332–45341.
240. L. Xing, C. Wang, Y. Cao, J. Zhang and H. Xia, Nanoscale, 2021, 13, 14925–14934.
241. B. Lin, P. Kannan, B. Qiu, Z. Lin and L. Guo, Food Chem., 2020, 307, 125528.
242. C. Huang, H. Zhao, Z. Peng, B. Zheng, C. Zhang and J. Wang, Appl. Surf. Sci., 2021, 561, 149856.
243. X. Zhao, Y. Wang, Y. Yao, L. Chen, B. Lin, W. Zheng, Y. Zeng, L. Li, Y. She and L. Guo, Anal. Chem., 2023, 95, 6836–6845.
244. A. Fularz, S. Almohammed and J. H. Rice, ACS Appl. Nano Mater., 2020, 3, 1666–1673.
245. M. J. Jeon, X. Ma, J. U. Lee, H. Roh, C. C. Bagot, W. Park and S. J. Sim, J. Phys. Chem. C, 2021, 125, 2502–2510.
246. Y. Li, X. Xin, T. Zhang, W. Li, J. Li and R. Lu, Chem. Eng. J., 2021, 422, 129983.
247. J. Kim, J. Park, J. Ki, H. W. Rho, Y.-M. Huh, E. Kim, H. Y. Son and S. Haam, Biosens. Bioelectron., 2022, 207, 114143.
248. L. Golubewa, A. Klimovich, I. Timoshchenko, Y. Padrez, M. Fetisova, H. Rehman, P. Karvinen, A. Selskis, S. Adomaviciute-Grabusove, I. Matulaitiene, A. Ramanavicius, R. Karpicz, T. Kulahava, Y. Svirko and P. Kuzhir, ACS Appl. Nano Mater., 2023, 6, 4770–4781.
249. Q. Han, Z. Lu, W. Gao, M. Wu, Y. Wang, Z. Wang, J. Qi and J. Dong, J. Mater. Chem. C, 2020, 8, 12599–12606.
250. K. Yang, K. Zhu, Y. Wang, Z. Qian, Y. Zhang, Z. Yang, Z. Wang, L. Wu, S. Zong and Y. Cui, ACS Nano, 2021, 15, 12996–13006.
251. Z. Ding, Y. Wang, W. Zhou, Y. Shui, Z. Zhu, M. Zhang, Y. Huang, C. Jiang, J. Li and Y. Wu, RSC Adv., 2023, 13, 8270–8280.
252. W. Nam, J. Song, S. A. Safiabadi Tali, H. J. Lezec, A. Agrawal and W. Zhou, ACS Appl. Nano Mater., 2021, 4, 3175–3184.
253. T. Lee, S. Kwon and J.-J. Lee, ACS Appl. Nano Mater., 2020, 3, 5920–5927.
254. Q. Luo, Y. Teng, M. He, X. Cai, L. Zhang, J. Zang, Y. Jin, J. Tang, T. Xu and C. Wang, Opt. Mater., 2023, 143, 114241.
255. C. Song, J. Zhang, Y. Liu, X. Guo, Y. Guo, X. Jiang and L. Wang, Sens. Actuators, B, 2020, 325, 128970.
256. R. Gao, X. Song, C. Zhan, C. Weng, S. Cheng, K. Guo, N. Ma, H. Chang, Z. Guo, L.-B. Luo and L. Yu, Sens. Actuators, B, 2020, 314, 128081.
257. H. Wang, G. Rao, Y. Wang, X. Du, M. Zhang, X. Wang, A. Hu, Y. Hu, J. Huang, J. Chu, X. Wang, L. Qian and J. Xiong, J. Phys. Chem. C, 2021, 125, 4710–4719.
258. S. Kang, W. Nam, W. Zhou, I. Kim and P. J. Vikesland, ACS Appl. Nano Mater., 2021, 4, 5768–5777.
259. D. Su, S. Yang, Y. Xiong, X. Yang, X. Yu, P. Zeng, Y. Wu, Y. Cheng, T. Zhang, H. Shen, H. Liang, G. Zhang, B. Song and S. Jiang, ACS Appl. Nano Mater., 2022, 5, 17067–17077.
260. J. Dong, W. Zhou, C. Yang, H. Wu, Q. Han, C. Zhang, W. Gao, X. Yan and M. Sun, ACS Appl. Mater. Interfaces, 2023, 15, 28840–28848.
261. S. H. Cho, K. M. Baek, H. J. Han, M. Kim, H. Park and Y. S. Jung, Adv. Funct. Mater., 2020, 30, 2000612.
262. K. H. Lee, H. Jang, Y. S. Kim, C.-H. Lee, S. H. Cho, M. Kim, H. Son, K. B. Bae, D. V. Dao, Y. S. Jung and I.-H. Lee, Adv. Sci., 2021, 8, 2100640.
263. S. Liu, J. Wu, C. Wang and J. Fang, Opt. Mater., 2022, 131, 112693.
264. S. Li, N. Zhang, N. Zhang, D. Lin, X. Hu and X. Yang, Sens. Actuators, B, 2020, 321, 128519.
265. L. Chen, C. Hu, Y. Dong, Y. Li, Q. Shi, G. Liu, R. Long and Y. Xiong, J. Phys. Chem. C, 2023, 127, 8167–8174.
266. Z. Dou, Z. Zhao, M. Zhang, Y. Xie, W. Yu and Y. Chen, ACS Appl. Nano Mater., 2020, 3, 2008–2015.
267. B. Liu, X. Yao, S. Chen, H. Lin, Z. Yang, S. Liu and B. Ren, Adv. Funct. Mater., 2018, 28, 1802263.
268. H. Chen, S.-G. Park, N. Choi, J.-I. Moon, H. Dang, A. Das, S. Lee, D.-G. Kim, L. Chen and J. Choo, Biosens. Bioelectron., 2020, 167, 112496.
269. S.-G. Park, C. Mun, X. Xiao, A. Braun, S. Kim, V. Giannini, S. A. Maier and D.-H. Kim, Adv. Funct. Mater., 2017, 27, 1703376.
270. H. Chen, S.-G. Park, N. Choi, H.-J. Kwon, T. Kang, M.-K. Lee and J. Choo, ACS Sens., 2021, 6, 2378–2385.
271. S.-G. Park, X. Xiao, J. Min, C. Mun, H. S. Jung, V. Giannini, R. Weissleder, S. A. Maier, H. Im and D.-H. Kim, Adv. Funct. Mater., 2019, 29, 1904257.
272. X. Wang, S.-G. Park, J. Ko, X. Xiao, V. Giannini, S. A. Maier, D.-H. Kim and J. Choo, Small, 2018, 14, 1801623.
273. H. Dang, S.-G. Park, Y. Wu, N. Choi, J.-Y. Yang, S. Lee, S.-W. Joo, L. Chen and J. Choo, Adv. Funct. Mater., 2021, 31, 2105703.
274. N. N. Nam, T. L. Bui, S. J. Son and S.-W. Joo, Adv. Funct. Mater., 2019, 29, 1809146.
275. Y. Xu, Y. Zhang, C. Li, Z. Ye and S. E. J. Bell, Acc. Chem. Res., 2023, 56, 2072–2083.
276. H. Kang, J. T. Buchman, R. S. Rodriguez, H. L. Ring, J. He, K. C. Bantz and C. L. Haynes, Chem. Rev., 2019, 119, 664–699.
277. P.-P. Fang, S. Chen, H. Deng, M. D. Scanlon, F. Gumy, H. J. Lee, D. Momotenko, V. Amstutz, F. Cortés-Salazar, C. M. Pereira, Z. Yang and H. H. Girault, ACS Nano, 2013, 7, 9241–9248.
278. L. Song, B. B. Xu, Q. Cheng, X. Wang, X. Luo, X. Chen, T. Chen and Y. Huang, Sci. Adv., 2021, 7, eabk2852.
279. Y. Ma, H. Liu, M. Mao, J. Meng, L. Yang and J. Liu, Anal. Chem., 2016, 88, 8145–8151.
280. C. Wu, S. Huang, Y. Wang, Y. Chai, R. Yuan and X. Yang, Anal. Chem., 2021, 93, 11019–11024.
281. D. Liu, F. Zhou, C. Li, T. Zhang, H. Zhang, W. Cai and Y. Li, Angew. Chem., Int. Ed., 2015, 54, 9596–9600.
282. Y. Wu, Q. Yu, Y. Joung, C. S. Jeon, S. Lee, S. H. Pyun, S.-W. Joo, L. Chen and J. Choo, Anal. Chem., 2023, 95, 12710–12718.
283. R. M. Davis, B. Kiss, D. R. Trivedi, T. J. Metzner, J. C. Liao and S. S. Gambhir, ACS Nano, 2018, 12, 9669–9679.
284. X. Liang, P. Zhang, M. Ma, T. Yang, X. Zhao, R. Zhang, M. Jing, R. Song, L. Wang and J. Fan, Nano Res., 2022, 15, 3487–3495.
285. Y. Liu, L. Huang, J. Wang, H. Tong, L. Yuan, L. Zhao, W. Zhang, L. Wang and J. Zhu, Spectrosc. Spectr. Anal., 2012, 32, 386–390.
286. Y. Hou, C. Yang, C. Liu, L. Gao, Z. Ma, Y. Yu and W. Wang, Anal. Bioanal. Chem., 2022, 414, 8289–8297.
287. Y. Wang, S. Kang, A. Khan, G. Ruttner, S. Y. Leigh, M. Murray, S. Abeytunge, G. Peterson, M. Rajadhyaksha, S. Dintzis, S. Javid and J. T. C. Liu, Sci. Rep., 2016, 6, 21242.
288. Z. Zheng, L. Wu, L. Li, S. Zong, Z. Wang and Y. Cui, Talanta, 2018, 188, 507–515.
289. Z. Zhang, J. Wang, K. B. Shanmugasundaram, B. Yeo, A. Möller, A. Wuethrich, L. L. Lin and M. Trau, Small, 2020, 16, 1905614.
290. Q. Zhang, R. Ma, Y. Zhang, J. Zhao, Y. Wang and Z. Xu, ACS Sens., 2023, 8, 875–883.
291. S. Meng, R. Chen, J. Xie, J. Li, J. Cheng, Y. Xu, H. Cao, X. Wu, Q. Zhang and H. Wang, Biosens. Bioelectron., 2021, 190, 113470.
292. P. Janik, B. Zawisza, E. Talik and R. Sitko, Microchim. Acta, 2018, 185, 117.
293. C. Lin, S. Liang, Y. Li, Y. Peng, Z. Huang, Z. Li, Y. Yang and X. Luo, Biosens. Bioelectron., 2022, 211, 114372.
294. M. Shi, J. Zheng, C. Liu, G. Tan, Z. Qing, S. Yang, J. Yang, Y. Tan and R. Yang, Biosens. Bioelectron., 2016, 77, 673–680.
295. L. Huang, Y. Zhu, C. Xu, Y. Cai, Y. Yi, K. Li, X. Ren, D. Jiang, Y. Ge, X. Liu, W. Sun, Q. Zhang and Y. Wang, ACS Sens., 2022, 7, 1439–1450.
296. Z. Liu, T. Li, Z. Wang, J. Liu, S. Huang, B. H. Min, J. Y. An, K. M. Kim, S. Kim, Y. Chen, H. Liu, Y. Kim, D. T. W. Wong, T. J. Huang and Y.-H. Xie, ACS Appl. Nano Mater., 2022, 5, 12506–12517.
297. D. Cao, H. Lin, Z. Liu, J. Qiu, S. Ge, W. Hua, X. Cao, Y. Qian, H. Xu and X. Zhu, Sens. Actuators, B, 2023, 375, 132903.
298. Y. Huang, Z. Liu, X. Qin, J. Liu, Y. Yang and W. Wei, Analyst, 2023, 148, 3295–3305.
299. S. Picciolini, D. Mehn, C. Morasso, R. Vanna, M. Bedoni, P. Pellacani, G. Marchesini, A. Valsesia, D. Prosperi, C. Tresoldi, F. Ciceri and F. Gramatica, ACS Nano, 2014, 8, 10496–10506.
300. J. Zheng, D. Ma, M. Shi, J. Bai, Y. Li, J. Yang and R. Yang, Chem. Commun., 2015, 51, 16271–16274.
301. M. R. Hoonejani, A. Pallaoro, G. B. Braun, M. Moskovits and C. D. Meinhart, Nanoscale, 2015, 7, 16834–16840.
302. X. Lin, D. Lin, Y. Chen, J. Lin, S. Weng, J. Song and S. Feng, Adv. Funct. Mater., 2021, 31, 2103382.
303. Y. Han, S.-R. Wu, X.-D. Tian and Y. Zhang, ACS Appl. Mater. Interfaces, 2020, 12, 28965–28974.
304. R. Liu, Y. Xiong, W. Tang, Y. Guo, X. Yan and M. Si, J. Raman Spectrosc., 2013, 44, 362–369.
305. L. Cheng, Z. Zhang, D. Zuo, W. Zhu, J. Zhang, Q. Zeng, D. Yang, M. Li and Y. Zhao, ACS Appl. Mater. Interfaces, 2018, 10, 34869–34877.
306. B. Tang, J. Wang, J. A. Hutchison, L. Ma, N. Zhang, H. Guo, Z. Hu, M. Li and Y. Zhao, ACS Nano, 2016, 10, 871–879.
307. Y. Qian, Y. Gu, J. Deng, Z. Cai, Y. Wang, R. Zhou, D. Zhu, H. Lu and Z. Wang, Int. J. Nanomed., 2023, 18, 3429–3442.
308. Y. Gao, H. Zhu, X. Wang, R. Shen, X. Zhou, X. Zhao, Z. Li, C. Zhang, F. Lei and J. Yu, Small, 2023, 19, 2207324.
309. X. Cao, S. Ge, X. Zhou, Y. Mao, Y. Sun, W. Lu and M. Ran, Biosens. Bioelectron., 2022, 205, 114110.
310. Y. Huang, T. Xie, K. Zou, Y. Gu, G. Yang, F. Zhang, L.-L. Qu and S. Yang, Nanoscale, 2021, 13, 13344–13352.
311. A. R. Kumar, K. B. Shanmugasundaram, J. Li, Z. Zhang, A. A. Ibn Sina, A. Wuethrich and M. Trau, RSC Adv., 2020, 10, 28778–28785.
312. J. Li, A. Wuethrich, Z. Zhang, J. Wang, L. L. Lin, A. Behren, Y. Wang and M. Trau, Anal. Chem., 2022, 94, 14573–14582.
313. J. Wang, A. Wuethrich, A. A. I. Sina, R. E. Lane, L. L. Lin, Y. Wang, J. Cebon, A. Behren and M. Trau, Sci. Adv., 2020, 6, eaax3223.
314. A. B. Beyene, B. J. Hwang, W. A. Tegegne, J.-S. Wang, H.-C. Tsai and W.-N. Su, Microchem. J., 2020, 158, 105099.
315. T.-D. Li, R. Zhang, H. Chen, Z.-P. Huang, X. Ye, H. Wang, A.-M. Deng and J.-L. Kong, Chem. Sci., 2018, 9, 5372–5382.
316. J. B. Phyo, A. Woo, H. J. Yu, K. Lim, B. H. Cho, H. S. Jung and M.-Y. Lee, Anal. Chem., 2021, 93, 3778–3785.
317. J. Yu, J. Jeon, N. Choi, J. O. Lee, Y.-P. Kim and J. Choo, Sens. Actuators, B, 2017, 251, 302–309.
318. R. Gao, Z. Cheng, X. Wang, L. Yu, Z. Guo, G. Zhao and J. Choo, Biosens. Bioelectron., 2018, 119, 126–133.
319. R. Chen, B. Liu, H. Ni, N. Chang, C. Luan, Q. Ge, J. Dong and X. Zhao, Analyst, 2019, 144, 4051–4059.
320. Y. Lu, C. Zhan, L. Yu, Y. Yu, H. Jia, X. Chen, D. Zhang and R. Gao, Sens. Actuators, B, 2023, 376, 133046.
321. T. Moisoiu, S. D. Iancu, D. Burghelea, M. P. Dragomir, G. Iacob, A. Stefancu, R. G. Cozan, O. Antal, Z. Bálint, V. Muntean, R. I. Badea, E. Licarete, N. Leopold and F. I. Elec, Biomedicines, 2022, 10, 233.
322. K. Niciński, J. Krajczewski, A. Kudelski, E. Witkowska, J. Trzcińska-Danielewicz, A. Girstun and A. Kamińska, Sci. Rep., 2019, 9, 9267.
323. C. Li, Y. Liu, X. Zhou and Y. Wang, J. Mater. Chem. B, 2020, 8, 3582–3589.
324. D. Zhang, L. Huang, B. Liu, H. Ni, L. Sun, E. Su, H. Chen, Z. Gu and X. Zhao, Biosens. Bioelectron., 2018, 106, 204–211.
325. D. Zhang, L. Huang, B. Liu, E. Su, H.-Y. Chen, Z. Gu and X. Zhao, Sens. Actuators, B, 2018, 277, 502–509.
326. W. Y. Lim, C.-H. Goh, T. M. Thevarajah, B. T. Goh and S. M. Khor, Biosens. Bioelectron., 2020, 147, 111792.
327. R. Gao, Z. Cheng, A. J. deMello and J. Choo, Lab Chip, 2016, 16, 1022–1029.
328. R. Gao, C. Zhan, C. Wu, Y. Lu, B. Cao, J. Huang, F. Wang and L. Yu, Lab Chip, 2021, 21, 3888–3898.
329. Y. Liu, R. Gao, Y. Zhuo, Y. Wang, H. Jia, X. Chen, Y. Lu, D. Zhang and L. Yu, Anal. Chim. Acta, 2023, 1239, 340673.
330. H. Wang, Y. Zhou, X. Jiang, B. Sun, Y. Zhu, H. Wang, Y. Su and Y. He, Angew. Chem., Int. Ed., 2015, 54, 5132–5136.
331. J. Ko, S.-G. Park, S. Lee, X. Wang, C. Mun, S. Kim, D.-H. Kim and J. Choo, ACS Appl. Mater. Interfaces, 2018, 10, 6831–6840.
332. C. Catala, B. Mir-Simon, X. Feng, C. Cardozo, N. Pazos-Perez, E. Pazos, S. Gómez-de Pedro, L. Guerrini, A. Soriano, J. Vila, F. Marco, E. Garcia-Rico and R. A. Alvarez-Puebla, Adv. Mater. Technol., 2016, 1, 1600163.
333. C. Wang, C. Wang, J. Li, Z. Tu, B. Gu and S. Wang, Biosens. Bioelectron., 2022, 214, 114525.
334. S. Srivastav, A. Dankov, M. Adanalic, R. Grzeschik, V. Tran, S. Pagel-Wieder, F. Gessler, I. Spreitzer, T. Scholz, B. Schnierle, O. E. Anastasiou, U. Dittmer and S. Schlücker, Anal. Chem., 2021, 93, 12391–12399.
335. M. Lu, Y. Joung, C. S. Jeon, S. Kim, D. Yong, H. Jang, S. H. Pyun, T. Kang and J. Choo, Nano Converge., 2022, 9, 39.
336. Q. Yu, J. Li, S. Zheng, X. Xia, C. Xu, C. Wang, C. Wang and B. Gu, J. Hazard. Mater., 2023, 459, 132136.
337. N. Mhlanga, T. Domfe, A. Skepu and T. A. Ntho, J. Raman Spectrosc., 2020, 51, 2416–2424.
338. C. Yuen, X. Gao, J. J. M. Yong, P. Prakash, C. R. Shobana, P. A. T. Kaushalya, Y. Luo, Y. Bai, C. Yang, P. R. Preiser and Q. Liu, Sens. Actuators, B, 2021, 343, 130162.
339. V. Tran, B. Walkenfort, M. Koenig, M. Salehi and S. Schluecker, Angew. Chem., Int. Ed., 2019, 58, 442–446.
340. W. Yuan, X. Han, G. Shi, M. Wang, W. Zhou, J. Cui, F. Liu, Z. Li, Y. Wu and L. Wang, Opt. Laser Technol., 2024, 169, 109911.
341. H. Zhou, L. Xu, Z. Ren, J. Zhu and C. Lee, Nanoscale Adv., 2023, 5, 538–570.
342. J. Yang, S. Lu, B. Chen, F. Hu, C. Li and C. Guo, TrAC, Trends Anal. Chem., 2023, 159, 116945.
343. Y. Tang, T. J. Hardy and J.-Y. Yoon, Biosens. Bioelectron., 2023, 234, 115361.
344. J. Lukose, A. K. Barik, N. Mithun, M. Sanoop Pavithran, S. D. George, V. M. Murukeshan and S. Chidangil, Biophys. Rev., 2023, 15, 199–221.
345. D. P. dos Santos, M. M. Sena, M. R. Almeida, I. O. Mazali, A. C. Olivieri and J. E. L. Villa, Anal. Bioanal. Chem., 2023, 415, 3945–3966.
346. N. E. Dina, M. A. Tahir, S. Z. Bajwa, I. Amin, V. K. Valev and L. Zhang, Biosens. Bioelectron., 2023, 219, 114843.
347. C. del Real Mata, O. Jeanne, M. Jalali, Y. Lu and S. Mahshid, Adv. Healthcare Mater., 2023, 12, 2202123.
348. X. Diao, X. Li, S. Hou, H. Li, G. Qi and Y. Jin, Anal. Chem., 2023, 95, 7552–7559.
349. C. Spedalieri and J. Kneipp, Nanoscale, 2022, 14, 5314–5328.
350. J. Son, G.-H. Kim, Y. Lee, C. Lee, S. Cha and J.-M. Nam, J. Am. Chem. Soc., 2022, 144, 22337–22351.
351. F. Lussier, V. Thibault, B. Charron, G. Q. Wallace and J.-F. Masson, TrAC, Trends Anal. Chem., 2020, 124, 115796.
352. F. Cui, Y. Yue, Y. Zhang, Z. Zhang and H. S. Zhou, ACS Sens., 2020, 5, 3346–3364.
353. M. Bao, J. Waitkus, L. Liu, Y. Chang, Z. Xu, P. Qin, J. Chen and K. Du, Lab Chip, 2023, 23, 4173–4200.
354. Y. X. Leong, Y. H. Lee, C. S. L. Koh, G. C. Phan-Quang, X. Han, I. Y. Phang and X. Y. Ling, Nano Lett., 2021, 21, 2642–2649.
355. Y.-H. Huang, H. Wei, P. J. Santiago, W. J. Thrift, R. Ragan and S. Jiang, Environ. Sci. Technol., 2023, 57, 4880–4891.
356. J. Plou, P. S. Valera, I. García, D. Vila-Liarte, C. Renero-Lecuna, J. Ruiz-Cabello, A. Carracedo and L. M. Liz-Marzán, Small, 2023, 19, 2207658.
357. G. Fang, X. Lin, X. Liang, J. Wu, W. Xu, W. Hasi and B. Dong, Small, 2022, 18, 2204588.
358. Z. Liu, D. Meng, G. Su, P. Hu, B. Song, Y. Wang, J. Wei, H. Yang, T. Yuan, B. Chen, T.-H. Ou, S. Hossain, M. Miller, F. Liu and W. Wu, Small, 2023, 19, 2204719.
359. M. Jalali, C. del Real Mata, L. Montermini, O. Jeanne, I. I. Hosseini, Z. Gu, C. Spinelli, Y. Lu, N. Tawil, M. C. Guiot, Z. He, S. Wachsmann-Hogiu, R. Zhou, K. Petrecca, W. W. Reisner, J. Rak and S. Mahshid, ACS Nano, 2023, 17, 12052–12071.
360. W. Shu, M. Zhang, C. Zhang, R. Li, C. Pei, Y. Zeng, L. Zhao, J. Zhao and J. Wan, Adv. Funct. Mater., 2023, 33, 2210267.
361. M. Erzina, A. Trelin, O. Guselnikova, B. Dvorankova, K. Strnadova, A. Perminova, P. Ulbrich, D. Mares, V. Jerabek, R. Elashnikov, V. Svorcik and O. Lyutakov, Sens. Actuators, B, 2020, 308, 127660.
362. J. Xu, Y. Xu, J. Li, J. Zhao, X. Jian, J. Xu, Z. Gao and Y.-Y. Song, ACS Sens., 2023, 8, 3487–3497.
363. S. Ganesh, K. Venkatakrishnan and B. Tan, Sens. Actuators, B, 2022, 361, 131655.
364. H. Yu, Z. Yang, S. Fu, Y. Zhang, R. Panneerselvamc, B. Li, L. Zhang, Z. Chen, X. Wang and J. Li, Chem. Commun., 2023, 59, 5779–5782.
365. S. Luo, W. Wang, Z. Zhou, Y. Xie, B. Ren, G. Liu and Z. Tian, Anal. Chem., 2022, 94, 10151–10158.

---