SERS aptasensor for simultaneous detection of ochratoxin A and zearalenone utilizing a rigid enhanced substrate (ITO/AuNPs/GO) combined with Au@AgNPs.

The contamination of mycotoxins poses a serious threat to global food security, hence the urgent need for simultaneous detection of multiple mycotoxins. Herein, two SERS nanoprobes were synthesized by embedded SERS tags (4-mercaptopyridine, 4MPy; 4-mercaptobenzonitrile, TBN) into the Au and Ag core-shell structure, and each was coupled with the aptamers specific to ochratoxin A (OTA) and zearalenone (ZEN). Meanwhile, a rigid enhanced substrate Indium tin oxide glass/AuNPs/Graphene oxide (ITO/AuNPs/GO) was combined with aptamer functionalized Au@AgNPs via π-π stacking interactions between the aptamer and GO to construct a surface-enhanced Raman spectroscopy (SERS) aptasensor, thereby inducing a SERS enhancement effect for the effective and swift simultaneous detection of both OTA and ZEN. The presence of OTA and ZEN caused signal probes dissociation, resulting in an inverse correlation between Raman signal intensity (1005 cm-1 and 2227 cm-1) and the concentrations of OTA and ZEN, respectively. The SERS aptasensor exhibited wide linear detection ranges of 0.001-20 ng/mL for OTA and 0.1-100 ng/mL for ZEN, with low detection limits (LOD) of 0.94 pg/mL for OTA and 59 pg/mL for ZEN. Furthermore, the developed SERS aptasensor demonstrated feasible applicability in the detection of OTA and ZEN in maize, showcasing its substantial potential for practical implementation.

Rapid detection of fertilizer information based on Raman spectroscopy and machine learning.

The rapid detection of fertilizer nutrient information is a crucial element in enabling intelligent and precise variable fertilizer application. However, traditional detection methods possess limitations, such as the difficulty in quantifying multiple components and cross-contamination. In this study, a rapid detection method was proposed, leveraging Raman spectroscopy combined with machine learning, to identify five types of fertilizers: K2SO4, (CO(NH2)2, KH2PO4, KNO3, and N:P:K (15-15-15), along with their concentrations. Qualitative and quantitative models of fertilizers were constructed using three machine learning algorithms combined with five spectral preprocessing methods. Two variable selection methods were used to optimize the quantitative model. The results showed that the classification accuracy of the five fertilizer solutions obtained by random forest (RF) was 100 %. Moreover, in terms of regression, partial least squares regression (PLSR) outperformed extreme learning machine (ELM) and least squares support vector machine (LSSVM), yielding prediction Rp2 within the range of 0.9843-0.9990 and a root mean square error in the range of 0.0486-0.1691. In addition, this study evaluated the impact of different water types (deionized water, well water, and industrial transition water) on the detection of fertilizer information via Raman spectroscopy. The results showed that while different water types did not notably affect the identification of fertilizer nutrients, they did exert a pronounced effect on the quantification of concentrations. This study highlights the efficacy of combining Raman spectroscopy with machine learning in detecting fertilizer nutrients and their concentration information effectively.

Examination of primary aromatic amines content in polylactic acid straws and migration into food simulants using SERS with LC-MS.

Polylactic acid (PLA) straws hold eco-friendly potential; however, residual diisocyanates used to enhance the mechanical strength can generate carcinogenic primary aromatic amines (PAAs), posing health risks. Herein, we present a rapid, comprehensive strategy to detecting PAAs in 18 brands of food-grade PLA straws and assessing their migration into diverse food simulants. Surface-enhanced Raman spectroscopy was conducted to rapidly screen straws for PAAs. Subsequently, qualitative determination of migrating PAAs into various food simulants (4 % acetic acid, 10 % ethanol, 50 % ethanol) occurred at 70 °C for 2 h using liquid chromatography-mass spectrometry. Three PAAs including 4,4'-methylenedianiline, 2,4'-methylenedianiline, and 2,4-diaminotoluene were detected in all straws. Specifically, 2,4-diaminotoluene in 50 % ethanol exceeded specific migration limit of 2 µg/kg, raising safety concerns. Notably, PAAs migration to 10 % and 50 % ethanol surpassed that to 4 % acetic acid within a short 2-hour period. Moreover, PLA straws underwent varying degrees of shape changes before and after migration. Straws with poly(butylene succinate) resisted deformation compared to those without, indicating enhanced heat resistance, while poly(butyleneadipate-co-terephthalate) improved hydrolysis resistance. Importantly, swelling study unveiled swelling effect wasn't the primary factor contributing to the increased PAAs migration in ethanol food simulant, as there was no significant disparity in swelling degrees across different food simulants. FT-IR and DSC analysis revealed higher PAAs content in 50 % ethanol were due to highly concentrated polar ethanol disrupting hydrogen bonds and van der Waal forces holding PLA molecules together. Overall, minimizing contact between PLA straws and alcoholic foods is crucial to avoid potential safety risks posed by PAAs.

A water probe for direct pH measurement of individual particles via micro-Raman spectroscopy.

The acidity of atmospheric aerosols influences fundamental physicochemical processes that affect climate and human health. We recently developed a novel and facile water-probe-based method for directly measuring of the pH for micrometer-size droplets, providing a promising technique to better understand aerosol acidity in the atmosphere. The complex chemical composition of fine particles in the ambient air, however, poses certain challenges to using a water-probe for pH measurement, including interference from interactions between compositions and the influence of similar compositions on water structure. To explore the universality of our method, it was employed to measure the pH of ammonium, nitrate, carbonate, sulfate, and chloride particles. The pH of particles covering a broad range (0-14) were accurately determined, thereby demonstrating that our method can be generally applied, even to alkaline particles. Furthermore, a standard spectral library was developed by integrating the standard spectra of common hydrated ions extracted through the water-probe. The library can be employed to identify particle composition and overcome the spectral overlap problem resulting from similar effects. Using the spectral library, all ions were identified and their concentrations were determined, in turn allowing successful pH measurement of multicomponent (ammonium-sulfate-nitrate-chloride) particles. Insights into the synergistic effect of Cl-, NO3-, and NH4+ depletion obtained with our approach revealed the interplay between pH and volatile partitioning. Given the ubiquity of component partitioning and pH variation in particles, the water probe may provide a new perspective on the underlying mechanisms of aerosol aging and aerosol-cloud interaction.

Label-free detection of kidney stones urine combined with SERS and multivariate statistical algorithm.

Kidney stones are a common urological disease with an increasing incidence worldwide. Traditional diagnostic methods for kidney stones are relatively complex and time-consuming, thus necessitating the development of a quicker and simpler diagnostic approach. This study investigates the clinical screening of kidney stones using Surface-Enhanced Raman Scattering (SERS) technology combined with multivariate statistical algorithms, comparing the classification performance of three algorithms (PCA-LDA, PCA-LR, PCA-SVM). Urine samples from 32 kidney stone patients, 30 patients with other urinary stones, and 36 healthy individuals were analyzed. SERS spectra data were collected in the range of 450-1800 cm-1 and analyzed. The results showed that the PCA-SVM algorithm had the highest classification accuracy, with 92.9 % for distinguishing kidney stone patients from healthy individuals and 92 % for distinguishing kidney stone patients from those with other urinary stones. In comparison, the classification accuracy of PCA-LR and PCA-LDA was slightly lower. The findings indicate that SERS combined with PCA-SVM demonstrates excellent performance in the clinical screening of kidney stones and has potential for practical clinical application. Future research can further optimize SERS technology and algorithms to enhance their stability and accuracy, and expand the sample size to verify their applicability across different populations. Overall, this study provides a new method for the rapid diagnosis of kidney stones, which is expected to play an important role in clinical diagnostics.

Identification of blood at simulated crime scenes using silver nanoparticles with SERS.

The analysis of substances and samples obtained from a crime scene is very important in solving forensic cases. To determine the variables involved in a crime and to expedite the investigation process, the rapid analysis of body fluids in small quantities and within environments containing diverse components is particularly necessary. For this reason, it is of great importance to analyze biological fluids with rapid, noncontaminating, nondestructive, low-cost, and accurate techniques. In recent years, with advancements in laser technology, spectroscopic methods have been introduced as analytical techniques in forensic medicine and chemical studies. This study focuses on surface-enhanced Raman spectroscopy (SERS) to demonstrate the detection of blood samples in simulated crime scenes. To minimize the background signal from fluorescent biomolecules in blood, dilution was performed with two different components and Raman analysis was performed for four different concentrations of blood. In general, a decrease in noise in the spectra was observed as the blood was diluted. Crime scenes consisting of pure blood, blood diluted with ethanol and distilled water (1:2, 1:4, and 1:8), a blood-mineral water mixture, a blood-cherry juice mixture, and silver nanoparticle-added mixtures were simulated, and their spectra were examined. Chemometric analyses of the data were performed. Despite high noise and low peak intensities, blood-identifying signals were detected when examining different blood concentrations. It was observed that silver nanoparticles provided high enhancement of blood peaks thanks to their strong plasmonic properties.

Raman laser intensity and sample clarification on biochemical monitoring over Zika-VLP upstream stages.

In the current biopharmaceutical scenario, constant bioprocess monitoring is crucial for the quality and integrity of final products. Thus, process analytical techniques, such as those based on Raman spectroscopy, have been used as multiparameter tracking methods in pharma bioprocesses, which can be combined with chemometric tools, like Partial Least Squares (PLS) and Artificial Neural Networks (ANN). In some cases, applying spectra pre-processing techniques before modeling can improve the accuracy of chemometric model fittings to observed values. One of the biological applications of these techniques could have as a target the virus-like particles (VLP), a vaccine production platform for viral diseases. A disease that has drawn attention in recent years is Zika, with large-scale production sometimes challenging without an appropriate monitoring approach. This work aimed to define global models for Zika VLP upstream production monitoring with Raman considering different laser intensities (200 mW and 495 mW), sample clarification (with or without cells), spectra pre-processing approaches, and PLS and ANN modeling techniques. Six experiments were performed in a benchtop bioreactor to collect the Raman spectral and biochemical datasets for modeling calibration. The best models generated presented a mean absolute error and mean relative error respectively of 3.46 × 105 cell/mL and 35 % for viable cell density (Xv); 4.1 % and 5 % for cell viability (CV); 0.245 g/L and 3 % for glucose (Glc); 0.006 g/L and 18 % for lactate (Lac); 0.115 g/L and 26 % for glutamine (Gln); 0.132 g/L and 18 % for glutamate (Glu); 0.0029 g/L and 3 % for ammonium (NH4+); and 0.0103 g/L and 2 % for potassium (K+). Sample without conditioning (with cells) improved the models' adequacy, except for Glutamine. ANN better predicted CV, Gln, Glu, and K+, while Xv, Glc, Lac, and NH4+ presented no statistical difference between the chemometric tools. For most of the assessed experimental parameters, there was no statistical need for spectra pre-filtering, for which the models based on the raw spectra were selected as the best ones. Laser intensity impacts quality model predictions in some parameters, Xv, Gln, and K+ had a better performance with 200 mW of intensity (for PLS, ANN, and ANN, respectively), for CV the 495 mW laser intensity was better (for PLS), and for the other biochemical variables, the use of 200 or 495 mW did not impact model fitting adequacy.

Pressure-induced phase changes in natural fluorapophyllite-(K) studied by Raman spectroscopy.

The high-pressure Raman spectra in the low-wavenumber region (50-1200 cm-1) and the hydroxyl stretching vibration region (2600-3800 cm-1) of fluorapophyllite-(K) were collected in the interval of 0.0-50.7 GPa. Experimental results show that fluorapophyllite-(K) undergoes a crystalline-crystalline phase transition from a tetragonal structure (P4/mnc) to an orthorhombic structure (Pnnm) and then amorphization under high pressure conditions. Discontinuous changes in Raman peaks in the hydroxyl vibration region (2600-3800 cm-1) and the low-wavenumber region (50-1200 cm-1) occur at different onset pressures, suggesting that the destabilization of sub-lattices in fluorapophyllite-(K) under high pressure is not synchronous; the weak interlayer H2O structural units are more prone to destabilization at lower pressures than the rigid SiO4 tetrahedral layer units. Therefore, H2O groups in fluorapophyllite-(K) serve as sensitive local probes during the pressure-induced phase transition and amorphization.

Validation of optimised intracranial spectroscopic probe for instantaneous in-situ monitoring and classification of traumatic brain injury.

The development of an optical interface to directly distinguish the brain tissue's biochemistry is the next step in understanding traumatic brain injury (TBI) pathophysiology and the best and most appropriate treatment in cases where in-hospital intracranial access is required. Despite TBI being a globally leading cause of morbidity and mortality in patients under 40, there is still a lack of objective diagnostical tools. Further, given its pathophysiological complexity the majority of treatments provided are purely symptomatic without standardized therapeutic targets. Our tailor-engineered prototype of the intracranial Raman spectroscopy probe (Intra-RSP) is designed to bridge the gap and provide real-time spectroscopic insights to monitor TBI and its evolution as well as identify patient-specific molecular targets for timely intervention. Raman spectroscopy being rapid, label-free and non-destructive, renders it an ideal portable diagnostics tool. In combination with our in-house developed software, using machine learning algorithms for multivariate analysis, the Intra-RSP is shown to accurately differentiate simulated TBI conditions in rat brains from the healthy controls, directly from the brain surface as well as through the rat's skull. Using clinically pre-established methods of cranial entry, the Intra-RSP can be inserted into a 2-piece optimised cranial bolt with integrated focussing and correctly identify a sample in real-life conditions with an accuracy >80 %. To further validate the Intra-RSP's efficiency as a TBI monitoring device, rat brains mildly damaged from inflicted spinal cord injury were found to be correctly classified with 94.5 % accuracy. Through optimization and rigorous in-vivo validation, the Intra-RSP prototype is envisioned to seamlessly integrate into existing standards of neurological care, serving as a minimally invasive, in-situ neuromonitoring tool. This transformative approach has the potential to revolutionize the landscape of neurological care by providing clinicians with unprecedented insights into the nature of brain injuries and fostering targeted, timely and effective therapeutic interventions.

Raman microspectroscopy and laser-induced breakdown spectroscopy for the analysis of polyethylene microplastics in human soft tissues.

People are exposed to microplastics (MPs) on a large scale in everyday life. However, it is not clear whether MPs can also be distributed and retained in certain tissues. Therefore, the development of analytical methods capable of detecting MPs in specific human organs/tissues is of utmost importance. In this study, the use and combination of spectroscopic techniques, namely Raman microspectroscopy and laser-induced breakdown spectroscopy (LIBS), was tested for the detection of polyethylene (PE) MPs in human tonsils. Preliminary results showed that Raman microspectroscopy was able to detect MPs down to 1 µm in size and LIBS down to 10 µm. In the next step, human tonsils were spiked with PE MPs, and digested. The filtered particles were analyzed using Raman microspectroscopy and LIBS, and complemented by X-ray fluorescence (XRF). The results showed that Raman microspectroscopy could reliably detect PE MPs in spiked human tonsils, while LIBS and XRF served as a reference analytical method to characterize particles that could not be classified by Raman microspectroscopy for their non-organic origin. The results of this study, supported by a current feasibility study conducted on clinical samples, demonstrated the reliability and feasibility of this approach for monitoring MPs in biotic samples.

SERS analysis of single cells and subcellular components: A review.

SERS is a rapidly advancing and non-destructive technique that has been proven to be more reliable and convenient than other traditional analytical methods. Due to its sensitivity and specificity, this technique is earning its place as a routine and powerful tool in biological and medical studies, especially for the analysis of living cells and subcellular components. This paper reviewed the research progress of single-cell SERS that has been made in the last few years and discussed challenges and future perspectives of this technique. The reviewed SERS platforms have been categorized according to their nature into the following types: (1) colloid-based, substrate-based, or hybrid; (2) ligand-based or ligand-free, and (3) label-based or label-free. The advantages and disadvantages of each type and their potential applications in various fields are thoroughly discussed.

Rapid determination of total phenolic content and antioxidant capacity of maple syrup using Raman spectroscopy and deep learning.

Total phenolic content (TPC) and antioxidant capacity of maple syrup were determined using Raman spectroscopy and deep learning. TPC was determined by Folin-Ciocalteu assay, while the antioxidant capacity was measured by 2,2-diphenyl-1picrylhydrazyl (DPPH) assay, oxygen radical absorbance capacity (ORAC) assay, and ferric reducing antioxidant power (FRAP) assay. A total of 360 spectra were collected from 36 maple syrup samples of different colours (dark, amber, light) by both benchtop and portable Raman spectrometers. These spectra were used to establish predictive models for assessing the antioxidant profiles of maple syrup. Deep learning models developed along with portable Raman spectroscopy exhibited comparable predictive performance to those developed along with benchtop Raman spectroscopy. Base on the spectral dataset collected using portable Raman spectroscopy, the developed deep learning models exhibited low RMSEs (root mean square errors, 7.2-17.9 % of mean reference values), low MAEs (mean absolute errors, 5.2-13.1 % of mean reference values) and high R2 values (>0.88). The results showed a great goodness of fit and accuracy for predicting the antioxidant profiles of maple syrup, indicating the potential of using portable Raman spectrometer for on-site analysis of antioxidant profiles of maple syrup.

Enhancing functional properties of compostable materials with biobased plasticizers for potential food packaging applications.

The demand of non-toxic and biobased plasticizers is substantially growing, particularly in biodegradable thermoplastics-based packaging applications. Herein, a derivative of citric acid (CITREM-LR10), usually used as food additive, was evaluated for the first time as plasticizer in PLA and Ecovio® biopolymers. Films containing 10 %(w/w) of CITREM-LR10 were prepared and compared with films plasticized with another biobased compound, SOFT-NSAFE, derived from acetic acid. The incorporation of both plasticizers provokes a slight reduction of the glass transition, however, only CITREM-LR10 was able to augment the elongation at break value of PLA films. A further evaluation of the films by Raman confocal microscopy showed the segregation of the CITREM-LR10 in microdomains, which could explain the enhanced elongation at break value, behaving as stress concentrators. In addition, CITREM-LR10 provides antimicrobial activity against S. aureus and both plasticizers give antioxidant properties, and almost negligible diffusion in food simulated solution. Composting studies showed that the plasticizers do not have effect on the disintegration rate of the films. In spite of these outstanding properties, the water vapour and oxygen barrier properties of the films worsen with its incorporation, therefore, the inclusion of fillers in the material together with the plasticizers would be necessary to improve such properties for food packaging applications.

Biaxial Strain Transfer in Monolayer MoS2 and WSe2 Transistor Structures.

Monolayer transition metal dichalcogenides are intensely explored as active materials in 2D material-based devices due to their potential to overcome device size limitations, sub-nanometric thickness, and robust mechanical properties. Considering their large band gap sensitivity to mechanical strain, single-layered TMDs are well-suited for strain-engineered devices. While the impact of various types of mechanical strain on the properties of a variety of TMDs has been studied in the past, TMD-based devices have rarely been studied under mechanical deformations, with uniaxial strain being the most common one. Biaxial strain on the other hand, which is an important mode of deformation, remains scarcely studied as far as 2D material devices are concerned. Here, we study the strain transfer efficiency in MoS2- and WSe2-based flexible transistor structures under biaxial deformation. Utilizing Raman spectroscopy, we identify that strains as high as 0.55% can be efficiently and homogeneously transferred from the substrate to the material in the transistor channel. In particular, for the WSe2 transistors, we capture the strain dependence of the higher-order Raman modes and show that they are up to five times more sensitive compared to the first-order ones. Our work demonstrates Raman spectroscopy as a nondestructive probe for strain detection in 2D material-based flexible electronics and deepens our understanding of the strain transfer effects on 2D TMD devices.

Monitoring the Dissolution Behavior of Novel Pharmaceutical Cocrystals Consisting of Antimalarial Drug Artemisinin with Probe-Type Low-Frequency Raman Spectrometer.

Artemisinin (ART) is a most promising antimalarial agent. However, its low aqueous solubility limits its oral absorption, resulting in low bioavailability. In this study, we have successfully discovered a novel cocrystal with 2-methyl resorcinol (ART-2MRE) providing improved solubility compared with a previously reported cocrystal with resorcinol (ART-RES). Single crystal X-ray structure analysis revealed that the ART-2MRE cocrystal was composed of ART and 2MRE in a molar ratio of 2 : 1. Though the ART-2MRE and ART-RES cocrystals were found to have similarities in their crystal structures, with one layer of a cocrystal former and two layers of ART arranged in alternating rows, the ART-2MRE cocrystal showed higher dissolution rate than ART-RES cocrystal. In situ real-time low-frequency (LF) Raman monitoring and powder X-ray diffraction (PXRD) measurements of the crystals during the dissolution test proved useful to investigate the dissolution behavior of the cocrystals. Low-frequency Raman monitoring revealed that as dissolution progressed, there was a continuous shift from the peak unique to the ART-2MRE cocrystal to the peak unique to the ART stable form. Similar observations were obtained in PXRD measurements as well. Furthermore, experiments were conducted by adding a polymer to the dissolution test solution to investigate the dissolution behavior under supersaturation, indicating the possibility of differences in the dissolution behavior between the ART-2MRE cocrystal and ART-RES cocrystal. Understanding the dissolution behavior from cocrystals is essential in developing cocrystals.

Plasmonic Nanotrenches with 1 nm Nanogaps for Surface-Enhanced Raman Scattering-Based Screening of His-Tagged Proteins.

This study represents a highly sensitive and selective approach to protein screening using surface-enhanced Raman scattering (SERS) facilitated by octahedral Au nanotrenches (OANTs). OANTs are a novel class of nanoparticles characterized by narrow, trench-like excavations indented into the eight facets of a Au octahedron. This unique configuration maximizes electromagnetic near-field focusing as the gap distance decreases to ∼1 nm. Owing to geometrical characteristics of the OANTs, near-field focusing can be maximized through the confinement and reflectance of light trapped within the trenches. We used Ni ions and molecular linkers to confer selective binding affinity for His-tagged proteins on the surfaces of the OANTs for SERS-based protein screening. Remarkably, SERS-based protein screening with the surface-modified OANTs yielded outstanding screening capabilities: 100% sensitivity and 100% selectivity in distinguishing His-tagged human serum albumin (HSA) from native HSA. This highlights the significantly enhanced protein screening capabilities achieved through the synergistic combination of SERS and the OANTs.

SERS-based Ag NCs@PDMS flexible substrate combined with chemometrics for rapid detection of foodborne pathogens on egg surface.

An innovative method is introduced based on the combination of label-free surface-enhanced Raman scattering with advanced multivariate analysis. This technique allows both quantitative and qualitative assessment of Salmonella typhimurium and Escherichia coli on eggshells. Using silver nanocubes embedded in polydimethylsiloxane, we consistently achieved Raman spectra of bacteria. The stability of the Ag NCs@PDMS substrate is confirmed using rhodamine 6G over 30 days under standard conditions. Principal component analysis (PCA) effectively distinguishes between S. typhimurium and E. coli spectra. Partial least squares regression (PLS) models were developed for quantitative determination of bacteria on egg surfaces, yielding accurate results with minimal error. The S. typhimurium model achieves Rc2 = 0.9563 and RMSEC = 0.601 in calibration, and Rv2 = 0.9113 and RMSEV = 0.907 in validation. Similarly, the E. coli model achieves Rc2 = 0.9877 and RMSEC = 0.322 in calibration, and Rv2 = 0.9606 and RMSEV = 0.579 in validation. Recoveries validate PLS predictions by inoculating egg surfaces with varying bacterial amounts. Our study demonstrates the feasibility of SERS-PLS for quantitative determination of S. typhimurium and E. coli on eggshells, promising enhanced food safety protocols.

Speciation Analysis of Metals and Metalloids by Surface Enhanced Raman Spectroscopy.

The presence of metalloids and heavy metals in the environment is of critical concern due to their toxicological impacts. However, not all metallic species have the same risk level. Specifically, the physical, chemical, and isotopic speciation of the metal(loids) dictate their metabolism, toxicity, and environmental fate. As such, speciation analysis is critical for environmental monitoring and risk assessment. In the past two decades, surface-enhanced Raman spectroscopy (SERS) has seen significant developments regarding trace metal(loid) sensing due to its ultrahigh sensitivity, readiness for in situ real-time applications, and cost-effectiveness. However, the speciation of metal(loid)s has not been accounted for in the design and application of SERS sensors. In this Perspective, we examine the potential of SERS for metal(loid) speciation analysis and highlight the advantages, progress, opportunities, and challenges of this application.

Rapid detection of African swine fever virus via SERS probe-modified sandwich hybridization assay.

Rapid and reliable detection method for African swine fever virus (ASFV) is proposed by surface-enhanced Raman spectroscopy (SERS). The ASFV target DNA can be specifically captured by sandwich hybridization between nanomagnetic beads and a SERS probe. Experimental results show that the significant Raman signal of the SERS probe with gold nanoparticles and a molecular reporter DTNB (5,5'-dimercapto-bis (2-nitrobenzoic acid)) can be adopted for detecting the hybridization chain reaction of ASFV DNA. The advantage of the SERS sandwich hybridization assay is the large response range from the single molecule level to 108 copies per mL, which not only can overcome the tedious time required for the amplification reaction but also provides a comparative method to polymerase chain reaction. Furthermore, real samples of African swine fever virus were detected from different subjects of swine fever virus including porcine reproductive respiratory syndrome virus and Japanese encephalitis virus. The proposed biosensor method can rapidly detect ASFV correctly within 15 min as a simple, convenient, low-cost detection approach. The biosensor can be used as a platform for the determination in biological, food, and environmental analytical fields.

Single-Molecule Surface-Enhanced Raman Spectroscopy: Challenges, Opportunities, and Future Directions.

Single-molecule surface-enhanced Raman spectroscopy (SM-SERS) is a powerful experimental technique for label-free sensing, imaging, and chemical analysis. Although Raman spectroscopy itself is an extremely "feeble" phenomenon, the intense interaction of optical fields with metallic nanostructures in the form of plasmonic hotspots can generate Raman signals from single molecules. While what constitutes a true single-molecule signal has taken some years for the scientific community to establish, many SERS experiments, even those not specifically attempting single-molecule sensitivity, have observed fluctuation in both the SERS intensity and spectral features. In this Perspective, we discuss the impact that fluctuating SERS signals have had on the continuing advancement of SM-SERS, along with challenges and current and potential future applications.