Surface enhanced Raman scattering artificial nose for high dimensionality fingerprinting

Oppositely-charged silver nanoparticles enable selective SERS molecular enhancement through electrostatic interactions

Label-free surface-enhanced Raman spectroscopy (SERS) has attracted extensive attention as an emerging technique for molecular phenotyping of biological samples. However, the selective enhancement property of SERS mediated by complicated interactions between substrates and analytes is unfavorable for molecular profiling. The electrostatic force is among the most dominating interactions that can cause selective adsorption of molecules to charged substrates. This means if only negatively- or positively-charged SERS substrates are applied, then considerable SERS information from a portion of analytes would be lost, hindering comprehensive SERS sensing. In this work, we utilize both negatively- and positively-charged colloidal silver (Ag) nanoparticles (NPs) to detect various charged molecules. The negatively-charged citrate-stabilized Ag and the positively-charged Ag prepared via a cetyltrimethyl-ammonium chloride-based charge reversal protocol have been adopted as SERS substrates. The Ag NPs are all relatively well-dispersed with good uniformity. After applying the oppositely-charged NPs to the detection of charged molecules, we find the SERS results explicitly demonstrate the electrostatically-driven SERS selective enhancement, which is further supported and clarified by molecular electrostatic potential calculations. Our work highlights the importance of developing SERS substrates modified with appropriate surface charges for various analytes, and enlightens us that potentially more molecular SERS information can be acquired from complex bio-samples using combinations of oppositely-charged substrates.

Molecular-Sieving Label-Free Surface-Enhanced Raman Spectroscopy for Sensitive Detection of Trace Small-Molecule Biomarkers in Clinical Samples

Small-molecule biomarkers are ubiquitous in biological fluids with pathological implications, but major challenges persist in their quantitative analysis directly in complex clinical samples. Herein, a molecular-sieving label-free surface-enhanced Raman spectroscopy (SERS) biosensor is reported for selective quantitative analysis of trace small-molecule trimetazidine (TMZ) in clinical samples. Our biosensor is fabricated by decorating a superhydrophobic monolayer of microporous metal-organic frameworks (MOF) shell-coated Au nanostar nanoparticles on a silicon substrate. The design strategy principally combines the hydrophobic surface-enabled physical confinement and preconcentration, MOF-assisted molecular enrichment and sieving of small molecules, and sensitive SERS detection. Our biosensor utilizes such a "molecular confinement-and-sieving" strategy to achieve a five orders-of-magnitude dynamic detection range and a limit of detection of ≈0.5 nM for TMZ detection in either urine or whole blood. We further demonstrate the applicability of our biosensing platform for longitudinal label-free SERS detection of the TMZ level directly in clinical samples in a mouse model.

Integrating, Validating, and Expanding Information Space in Single-Molecule Surface-Enhanced Raman Spectroscopy for Biomolecules

Single-molecule surface-enhanced Raman spectroscopy (SM-SERS) is an ultrahigh-resolution spectroscopic method for directly obtaining the complex vibrational mode information on individual molecules. SM-SERS offers a wide range of submolecular information on the hidden heterogeneity in its functional groups and varying structures, dynamics of conformational changes, binding and reaction kinetics, and interactions with the neighboring molecule and environment. Despite the richness in information on individual molecules and potential of SM-SERS in various detection targets, including large and complex biomolecules, several issues and practical considerations remain to be addressed, such as the requirement of long integration time, challenges in forming reliable and controllable interfaces between nanostructures and biomolecules, difficulty in determining hotspot size and shape, and most importantly, insufficient signal reproducibility and stability. Moreover, utilizing and interpreting SERS spectra is challenging, mainly because of the complexity and dynamic nature of molecular fingerprint Raman spectra, and this leads to fragmentary analysis and incomplete understanding of the spectra. In this Perspective, we discuss the current challenges and future opportunities of SM-SERS in views of system approaches by integrating molecules of interest, Raman dyes, plasmonic nanostructures, and artificial intelligence, particularly for detecting and analyzing biomolecules to realize the validation and expansion of information space in SM-SERS.

Rapid Identification of Drug Mechanisms with Deep Learning-Based Multichannel Surface-Enhanced Raman Spectroscopy

Rapid identification of drug mechanisms is vital to the development and effective use of chemotherapeutics. Herein, we develop a multichannel surface-enhanced Raman scattering (SERS) sensor array and apply deep learning approaches to realize the rapid identification of the mechanisms of various chemotherapeutic drugs. By implementing a series of self-assembled monolayers (SAMs) with varied molecular characteristics to promote heterogeneous physicochemical interactions at the interfaces, the sensor can generate diversified SERS signatures for directly high-dimensionality fingerprinting drug-induced molecular changes in cells. We further train the convolutional neural network model on the multidimensional SAM-modulated SERS data set and achieve a discriminatory accuracy toward 99%. We expect that such a platform will contribute to expanding the toolbox for drug screening and characterization and facilitate the drug development process.

3D-Printed SERS Chips for Highly Specific Detection of Denatured Type I and IV Collagens in Blood for Early Hepatic Fibrosis Diagnosis

Hepatic fibrosis, the insidious progression of chronic liver scarring leading to life-threatening cirrhosis and hepatocellular carcinoma, necessitates the urgent development of noninvasive and precise diagnostic methodologies. Denatured collagen emerges as a critical biomarker in the pathogenesis of hepatic fibrosis. Herein, we have for the first time developed 3D-printed collagen capture chips for highly specific surface-enhanced Raman scattering (SERS) detection of denatured type I and type IV collagen in blood, facilitating the early diagnosis of hepatic fibrosis. Employing a novel blend of denatured collagen-targeting peptide-modified silver nanoparticle probes (Ag@DCTP) and polyethylene glycol diacrylate (PEGDA), we engineered a robust ink for the 3D fabrication of these collagen capture chips. The chips are further equipped with specialized SERS peptide probes, Ag@ICTP@R1 (S-I) and Ag@IVCTP@R2 (S-IV), tailored for the targeted detection of type I and IV collagen, respectively. The SERS chip platform demonstrated exceptional specificity and sensitivity in capturing and detecting denatured type I and IV collagen, achieving detection limits of 3.5 ng/mL for type I and 3.2 ng/mL for type IV collagen within a 10-400 ng/mL range. When tested on serum samples from hepatic fibrosis mouse models across a spectrum of fibrosis stages (S0-S4), the chips consistently measured denatured type I collagen and detected a progressive increase in type IV collagen concentration, which correlated with the severity of fibrosis. This novel strategy establishes a benchmark for the multiplexed detection of collagen biomarkers, enhancing our capacity to assess the stages of hepatic fibrosis.

Harnessing Cooperative Multivalency in Thioguanine for Surface-Enhanced Raman Scattering (SERS)-Based Differentiation of Polyfunctional Analytes Differing by a Single Functional Group

Small-molecule receptors are increasingly employed to probe various functional groups for (bio)chemical analysis. However, differentiation of polyfunctional analogs sharing multiple functional groups remains challenging for conventional mono- and bidentate receptors because their insufficient number of binding sites limits interactions with the least reactive yet property-determining functional group. Herein, we introduce 6-thioguanine (TG) as a supramolecular receptor for unique tridentate receptor-analyte complexation, achieving ≥97 % identification accuracy among 16 polyfunctional analogs across three classes: glycerol derivatives, disubstituted propane, and vicinal diols. Crucially, we demonstrate distinct spectral changes induced by the tridentate interaction between TG's three anchoring points and all the analyte's functional groups, even the least reactive ones. Notably, hydrogen bond (H-bond) networks formed in the TG-analyte complexes demonstrate additive effects in binding strength originating from good bond linearity, cooperativity, and resonance, thus strengthening complexation events and amplifying the differences in spectral changes induced among analytes. It also enhances spectral consistency by selectively forming a sole configuration that is stronger than the respective analyte-analyte interaction. Finally, we achieve 95.4 % accuracy for multiplex identification of a mixture consisting of multiple polyfunctional analogs. We envisage that extension to other multidentate non-covalent interactions enables the development of interference-free small molecule-based sensors for various (bio)chemical analysis applications.

Chiral-Induced Surface-Enhanced Raman Optical Activity on a Single-Particle Substrate

Surface-enhanced Raman optical activity (SEROA) is a promising method for analyzing chiral molecules' molecular chirality and structural changes. However, conventional SEROA measurements face challenges related to substrate stability, signal uniformity, and interference from electronic circular dichroism (ECD). Therefore, in this study, we present a uniform and stable substrate for SEROA measurements by utilizing Au nanoparticles on the Au nanofilm structure to confine hotspots to the film-particle junctions and minimize ECD interference. This method also uses the induction of chirality from chiral molecules to achiral molecules to overcome the limitation of chiral molecules in SEROA measurements, specifically their lower signal efficiency. Successful chirality transfer is demonstrated through distinguishable SEROA signals when the l/d-alanine mixture is present. Enantiomeric discrimination of different l/d-alanine ratios was achieved with linear responses in the circular intensity difference (CID). Altogether, the proposed chiral-induced SEROA on the AuNP_on_AuNF substrate shows promising potential for detecting and characterizing structural changes in biomolecules, thus making it a valuable tool for various research applications.

Paper/GO/e-Au flexible SERS sensors for in situ detection of tricyclazole in orange juice and on cucumber skin at the sub-ppb level: machine learning-assisted data analysis

Despite being an excellent surface enhanced Raman scattering (SERS) active material, gold nanoparticles were difficult to be loaded onto the surface of filter paper to fabricate flexible SERS substrates. In this study, electrochemically synthesized gold nanoparticles (e-AuNPs) were deposited on graphene oxide (GO) nanosheets in solution by ultrasonication, resulting in the formation of a GO/Au hybrid material. Thanks to the support of GO, the hybrid material could adhere onto the surface of filter paper, which was immersed into a GO/Au solution for 24 h and dried naturally at room temperature. The paper-based materials were then employed as substrates for a surface enhanced Raman scattering (SERS) sensing platform to detect tricyclazole (TCZ), a widely used pesticide, resulting in better sensitivity compared to the use of paper/Au SERS sensors. With the most optimal GO content of 4%, paper/GO/Au SERS sensors could achieve a limit of detection of 1.32 × 10-10 M in standard solutions. Furthermore, the filter paper-based SERS sensors also exhibited significant advantages in sample collection in real samples. On one hand, the sensors were dipped into orange juice, allowing TCZ molecules in this real sample to be adsorbed onto their SERS active surface. On the other hand, they were pasted onto cucumber skin to collect the analytes. As a result, the paper/GO/Au SERS sensors could sense TCZ in orange juice and on cucumber skin at concentrations as low as 10-9 M (∼2 ppb). In addition, a machine learning model was designed and developed, allowing the sensing system to discriminate TCZ from nine other organic compounds and predict the presence of TCZ on cucumber skin at concentrations down to 10-9 M.

Surface-functionalized SERS platform for deep learning-assisted diagnosis of Alzheimer's disease

Early diagnosis of Alzheimer's disease is crucial to stall the deterioration of brain function, but conventional diagnostic methods require complicated analytical procedures or inflict acute pain on the patient. Then, label-free Surface-enhanced Raman spectroscopy (SERS) analysis of blood-based biomarkers is a convenient alternative to rapidly obtain spectral information from biofluids. However, despite the rapid acquisition of spectral information from biofluids, it is challenging to distinguish spectral features of biomarkers due to interference from biofluidic components. Here, we introduce a deep learning-assisted, SERS-based platform for separate analysis of blood-based amyloid β (1-42) and metabolites, enabling the diagnosis of Alzheimer's disease. SERS substrates consisting of Au nanowire arrays are fabricated and functionalized in two characteristic ways to compare the validity of different Alzheimer's disease biomarkers measured on our SERS system. The 6E10 antibody is immobilized for the capture of amyloid β (1-42) and analysis of its oligomerization process, while various self-assembled monolayers are attached for different dipole interactions with blood-based metabolites. Ultimately, SERS spectra of blood plasma of Alzheimer's disease patients and human controls are measured on the substrates and classified via advanced deep learning techniques that automatically extract informative features to learn generalizable representations. Accuracies up to 99.5% are achieved for metabolite-based analyses, which are verified with an explainable artificial intelligence technique that identifies key spectral features used for classification and for deducing significant biomarkers.

Food for Thought: Optical Sensor Arrays and Machine Learning for the Food and Beverage Industry

Arrays of cross-reactive sensors, combined with statistical or machine learning analysis of their multivariate outputs, have enabled the holistic analysis of complex samples in biomedicine, environmental science, and consumer products. Comparisons are frequently made to the mammalian nose or tongue and this perspective examines the role of sensing arrays in analyzing food and beverages for quality, veracity, and safety. I focus on optical sensor arrays as low-cost, easy-to-measure tools for use in the field, on the factory floor, or even by the consumer. Novel materials and approaches are highlighted and challenges in the research field are discussed, including sample processing/handling and access to significant sample sets to train and test arrays to tackle real issues in the industry. Finally, I examine whether the comparison of sensing arrays to noses and tongues is helpful in an industry defined by human taste.

Detection and Identification of Pesticides in Fruits Coupling to an Au-Au Nanorod Array SERS Substrate and RF-1D-CNN Model Analysis

In this research, a method was developed for fabricating Au-Au nanorod array substrates through the deposition of large-area Au nanostructures on an Au nanorod array using a galvanic cell reaction. The incorporation of a granular structure enhanced both the number and intensity of surface-enhanced Raman scattering (SERS) hot spots on the substrate, thereby elevating the SERS performance beyond that of substrates composed solely of an Au nanorod. Calculations using the finite difference time domain method confirmed the generation of a strong electromagnetic field around the nanoparticles. Motivated by the electromotive force, Au ions in the chloroauric acid solution were reduced to form nanostructures on the nanorod array. The size and distribution density of these granular nanostructures could be modulated by varying the reaction time and the concentration of chloroauric acid. The resulting Au-Au nanorod array substrate exhibited an active, uniform, and reproducible SERS effect. With 1,2-bis(4-pyridyl)ethylene as the probe molecule, the detection sensitivity of the Au-Au nanorod array substrate was enhanced to 10-11 M, improving by five orders of magnitude over the substrate consisting only of an Au nanorod array. For a practical application, this substrate was utilized for the detection of pesticides, including thiram, thiabendazole, carbendazim, and phosmet, within the concentration range of 10-4 to 5 × 10-7 M. An analytical model combining a random forest and a one-dimensional convolutional neural network, referring to the important variable-one-dimensional convolutional neural network model, was developed for the precise identification of thiram. This approach demonstrated significant potential for biochemical sensing and rapid on-site identification.

Machine learning-based exosome profiling of multi-receptor SERS sensors for differentiating adenocarcinoma in situ from early-stage invasive adenocarcinoma

Exosomes, extracellular vesicles released by cells, hold potential as diagnostic markers for the early detection of lung cancer. Despite their clinical promise, current technologies lack rapid and effective means to discriminate between exosomes derived from adenocarcinoma in situ (AIS) and early-stage invasive adenocarcinoma (IAC). This challenge arises from the intrinsic structural heterogeneity of exosomes, necessitating the development of advanced methodologies for precise differentiation. Here, we demonstrate a novel approach for plasma exosome detection utilizing multi-receptor surface-enhanced Raman spectroscopy (SERS) technology to differentiate between AIS and early-stage IAC. To accomplish this, we synthesized a stable and uniform two-dimensional SERS substrate (BC/Au NPs film) by fabricating gold nanoparticles onto bacterial cellulose. We then enhanced its capabilities by introducing multi-receptor SERS functionality via modifying the substrate with both low-specificity and physicochemical-selective molecules. Furthermore, by strategically combining all capturer-exosome SERS spectra, comprehensive "combined-SERS spectra" are reconstructed to enhance spectral variations of the exosome. Combining these features with partial least squares regression-discriminant analysis (PLS-DA) modeling significantly improved discriminatory accuracy, achieving 90% sensitivity and 95% specificity in distinguishing AIS from early-stage IAC. Our developed SERS sensor provides an effective method for early detection of lung cancer, thereby paving a new way for innovative advancements in diagnosing lung cancer.

Rapid and Precise Differentiation and Authentication of Agricultural Products via Deep Learning-Assisted Multiplex SERS Fingerprinting

Accurate and rapid differentiation and authentication of agricultural products based on their origin and quality are crucial to ensuring food safety and quality control. However, similar chemical compositions and complex matrices often hinder precise identification, particularly for adulterated samples. Herein, we propose a novel method combining multiplex surface-enhanced Raman scattering (SERS) fingerprinting with a one-dimensional convolutional neural network (1D-CNN), which enables the effective differentiation of the category, origin, and grade of agricultural products. This strategy leverages three different SERS-active nanoparticles as multiplex sensors, each tailored to selectively amplify the signals of preferentially adsorbed chemicals within the sample. By strategically combining SERS spectra from different NPs, a 'SERS super-fingerprint' is constructed, offering a more comprehensive representation of the characteristic information on agricultural products. Subsequently, utilizing a custom-designed 1D-CNN model for feature extraction from the 'super-fingerprint' significantly enhances the predictive accuracy for agricultural products. This strategy successfully identified various agricultural products and simulated adulterated samples with exceptional accuracy, reaching 97.7% and 94.8%, respectively. Notably, the entire identification process, encompassing sample preparation, SERS measurement, and deep learning analysis, takes only 35 min. This development of deep learning-assisted multiplex SERS fingerprinting establishes a rapid and reliable method for the identification and authentication of agricultural products.

Cancer screening through surface-enhanced Raman spectroscopy fingerprinting analysis of urinary metabolites using surface-carbonized silver nanowires on a filter membrane

BACKGROUND

Label-free surface-enhanced Raman spectroscopy (SERS)-based metabolic profiling has great potential for early cancer diagnosis, but further advancements in analytical methods and clinical evidence studies are required for clinical applications. To improve the cancer diagnostic accuracy of label-free SERS spectral analysis of complex biological fluids, it is necessary to obtain specifically enhanced SERS signals of cancer-related metabolites present at low concentrations.

RESULTS

This study presents a novel 3D SERS sensor, comprising a surface-carbonized silver nanowire (AgNW)-stacked filter membrane, alongside an optimized urine/methanol/chloroform extraction technique, which specifically changes the molecular adsorption and orientation of aromatic metabolites onto SERS substrates. By analyzing the pretreated urine samples on the surface-carbonized AgNW 3D SERS sensor, distinct and highly enhanced SERS peaks derived from semi-polar aromatic metabolites were observed for pancreatic cancer and prostate cancer samples compared with normal controls. Urine metabolite analysis using SERS fingerprinting successfully differentiated pancreatic cancer and prostate cancer groups from normal control group: normal control (n = 56), pancreatic cancer (n = 40), and prostate cancer (n = 39).

SIGNIFICANCE AND NOVELTY

We confirmed the clinical feasibility of performing fingerprint analysis of urinary metabolites based on the surface-carbonized AgNW 3D SERS sensor and methanol/chloroform extraction for noninvasive cancer screening. This technology holds potential for large-scale screening owing to its high accuracy, and cost effective, simple and rapid detection method.

Artificial Intelligence for Surface-Enhanced Raman Spectroscopy

Surface-enhanced Raman spectroscopy (SERS), well acknowledged as a fingerprinting and sensitive analytical technique, has exerted high applicational value in a broad range of fields including biomedicine, environmental protection, food safety among the others. In the endless pursuit of ever-sensitive, robust, and comprehensive sensing and imaging, advancements keep emerging in the whole pipeline of SERS, from the design of SERS substrates and reporter molecules, synthetic route planning, instrument refinement, to data preprocessing and analysis methods. Artificial intelligence (AI), which is created to imitate and eventually exceed human behaviors, has exhibited its power in learning high-level representations and recognizing complicated patterns with exceptional automaticity. Therefore, facing up with the intertwining influential factors and explosive data size, AI has been increasingly leveraged in all the above-mentioned aspects in SERS, presenting elite efficiency in accelerating systematic optimization and deepening understanding about the fundamental physics and spectral data, which far transcends human labors and conventional computations. In this review, the recent progresses in SERS are summarized through the integration of AI, and new insights of the challenges and perspectives are provided in aim to better gear SERS toward the fast track.

Surface-enhanced Raman spectroscopy as a powerful method for the analysis of Chinese herbal medicines

Chinese herbal medicines (CHMs) derived from nature have received increasing attention and become more popular. Due to their diverse production processes, complex ingredients, and different storage conditions, it is highly desirable to develop simple, rapid, efficient and trace detection methods to ensure the drug quality. Surface-enhanced Raman spectroscopy has the advantages of being time-saving, non-destructive, usable in aqueous environments, and highly compatible with various biomolecular samples, providing a promising analytical method for CHM. In this review, we outline the major advances in the application of SERS to the identification of raw materials, detection of bioactive constituents, characterization of adulterants, and detection of contaminants. This clearly shows that SERS has strong potential in the quality control of CHM, which greatly promotes the modernization of CHM.

Machine Learning-Assisted High-Throughput SERS Classification of Cell Secretomes

During the response to different stress conditions, damaged cells react in multiple ways, including the release of a diverse cocktail of metabolites. Moreover, secretomes from dying cells can contribute to the effectiveness of anticancer therapies and can be exploited as predictive biomarkers. The nature of the stress and the resulting intracellular responses are key determinants of the secretome composition, but monitoring such processes remains technically arduous. Hence, there is growing interest in developing tools for noninvasive secretome screening. In this regard, it has been previously shown that the relative concentrations of relevant metabolites can be traced by surface-enhanced Raman scattering (SERS), thereby allowing label-free biofluid interrogation. However, conventional SERS approaches are insufficient to tackle the requirements imposed by high-throughput modalities, namely fast data acquisition and automatized analysis. Therefore, machine learning methods were implemented to identify cell secretome variations while extracting standard features for cell death classification. To this end, ad hoc microfluidic chips were devised, to readily conduct SERS measurements through a prototype relying on capillary pumps made of filter paper, which eventually would function as the SERS substrates. The developed strategy may pave the way toward a faster implementation of SERS into cell secretome classification, which can be extended even to laboratories lacking highly specialized facilities.

Identifying Surface-Enhanced Raman Spectra with a Raman Library Using Machine Learning

Since its discovery, surface-enhanced Raman spectroscopy (SERS) has shown outstanding promise of identifying trace amounts of unknown molecules in rapid, portable formats. However, the many different types of nanoparticles or nanostructured metallic SERS substrates created over the past few decades show substantial variability in the SERS spectra they provide. These inconsistencies have even raised speculation that substrate-specific SERS spectral libraries must be compiled for practical use of this type of spectroscopy. Here, we report a machine learning (ML) algorithm that can identify chemicals by matching their SERS spectra to those of a standard Raman spectral library. We use an approach analogous to facial recognition that utilizes feature extraction in the presence of multiple nuisance variables for spectral recognition. The key element is a metric we call "Characteristic Peak Similarity" (CaPSim) that focuses on the characteristic peaks in the SERS spectra. It has the flexibility to accommodate substrate-specific variability when quantifying the degree of similarity to a Raman spectrum. Analysis shows that CaPSim substantially outperforms existing spectral matching algorithms in terms of accuracy. This ML-based approach could greatly facilitate the spectroscopic identification of molecules in fieldable SERS applications.

Development of a Low-Cost Paper-Based Platform for Coffee Ring-Assisted SERS

The need for highly sensitive, low-cost, and timely diagnostic technologies at the point of care is increasing. Surface-enhanced Raman spectroscopy (SERS) is a vibrational spectroscopic technique that is an advantageous technique to address this need, as it can rapidly detect analytes in small or dilute samples with improved sensitivity compared to conventional Raman spectroscopy. Despite the many advantages of SERS, one drawback of the technique is poor reproducibility due to variable interactions between nanoparticles and target analytes. To overcome this limitation, coupling SERS with the coffee ring effect has been implemented to concentrate and localize analyte-nanoparticle conjugates for improved signal reproducibility. However, current coffee ring platforms require laborious fabrication steps. Herein, we present a low-cost, two-step fabrication process for coffee ring-assisted SERS, utilizing wax-printed nitrocellulose paper. The platform was designed to produce a highly hydrophobic paper substrate that supports the coffee ring effect and tested using gold nanoparticles for SERS sensing. The nanoparticle concentration and solvent were varied to determine the effect of solution composition on ring formation and center clearance. The SERS signal was validated using 4-mercaptobenzoic acid (MBA) and tested with Moraxella catarrhalis bacteria to ensure functionality for chemical and biological applications. The limit of detection using MBA is 41.56 nM, and the biochemical components of the bacterial cell wall were enhanced with low spectral variability. The developed platform is advantageous due to ease of fabrication and use, representing the next step toward implementing low-cost coffee ring-assisted SERS for point-of-care sensing.

Plasmonic Cavity for Self-Powered Chemical Detection and Performance Boosted Surface-Enhanced Raman Scattering Detection

With the popularization of the Internet of Things, the application of chemical sensors has become more and more extensive. However, it is difficult for a single functional sensor to meet multiple needs at the same time. For the next generation of chemical sensors, in addition to rapid qualitative and quantitative detection, it is also necessary to solve the problem of a distributed sensor power supply. Triboelectric nanogenerator (TENG) and surface-enhanced Raman scattering (SERS) are two emerging technologies that can be used for chemical testing. The combination of TENG and SERS technology is proposed to be an attractive research strategy to implement qualitative and quantitative analysis, as well as self-powered detection in one device. Herein, the Ag nanoparticle (NP)@polydimethylsiloxane (PDMS) plasmonic cavity is demonstrated, which can be exploited not only as a SERS substrate for qualitative analysis of the target molecules but also as a TENG based self-powered chemical sensor for rapid quantitative analysis. More importantly, the as-designed plasmonic cavity enables prolonged triboelectric field generated by the phenomena of triboelectricity, which in turn enhances the "hot spot" intensities from Ag NPs in the cavity and boosts the SERS signals. In this way, the device can have good feasibility and versatility for chemical detection. Specifically, the measurement of the concentration of many analytes can be successfully realized, including ions and small molecules. The results verify that the proposed sensor system has the potential for self-powered chemical sensors for environmental monitoring and analytical chemistry.

Fabrication of Silver Nanobowl Arrays on Patterned Sapphire Substrate for Surface-Enhanced Raman Scattering

The current article discusses surface-enhanced Raman spectroscopy (SERS) as a powerful technique for detecting molecules or ions by analyzing their molecular vibration signals for fingerprint peak recognition. We utilized a patterned sapphire substrate (PSS) featuring periodic micron cone arrays. Subsequently, we prepared a three-dimensional (3D) PSS-loaded regular Ag nanobowls (AgNBs) array using self-assembly and surface galvanic displacement reactions based on polystyrene (PS) nanospheres. The SERS performance and structure of the nanobowl arrays were optimized by manipulating the reaction time. We discovered that the PSS substrates featuring periodic patterns exhibited superior light-trapping effects compared to the planar substrates. The SERS performance of the prepared AgNBs-PSS substrates was tested under the optimized experimental parameters with 4-mercaptobenzoic acid (4-MBA) as the probe molecule, and the enhancement factor (EF) was calculated to be 8.96 × 10⁴. Finite-difference time-domain (FDTD) simulations were conducted to explain that the AgNBs arrays' hot spots were distributed at the bowl wall locations. Overall, the current research offers a potential route for developing high-performance, low-cost 3D SERS substrates.

Towards a scalable and controllable preparation of highly-uniform surface-enhanced Raman scattering substrates: Defect-free nanofilms as templates

The uniformity and reproducibility of substrates highly determine the applicability of surface-enhanced Raman scattering (SERS). Production of them, however, remains a challenge. Herein, we report a template-based strategy for the strictly controllable and handily scalable preparation of a very uniform SERS substrate, Ag nanoparticles (AgNPs)/nanofilm, where the template used is a flexible, transparent, self-standing, defect-free and robust nanofilm. Importantly, the obtained AgNPs/nanofilm is self-adhesive to surfaces of different properties and morphologies, ensuring in-situ and at real-time SERS detection. The enhancement factor (EF) of the substrate for rhodamine 6G (R6G) could reach 5.8 × 10¹⁰ with a detection limit (DL) of 1.0 × 10^-15 mol L^-1. Moreover, 500 bending tests and one-month storage showed no observable performance degradation, and up to 50.0 cm² scaled-up preparation depicted negligible effect upon the structure and the sensing performance. The real-life applicability of AgNPs/nanofilm was demonstrated by the sensitive detection of tetramethylthiuram disulfide on cherry tomato and fentanyl in methanol with a routine handheld Raman spectrometer. This work thus provides a reliable strategy for large area wet-chemical preparation of high-quality SERS substrates.

Machine-learning-assisted SERS nanosensor platform toward chemical fingerprinting of Baijiu flavors

A novel fingerprinting platform for multiplex detection of flavor molecules in Baijiu was developed by using a surface-enhanced Raman scattering (SERS) nanosensor array in combination with machine learning. The SERS sensors were constructed by core-shell Fe₃O₄@Ag nanoparticles modified with molecules carrying end-groups of hydroxyl, pyridyl, methyl, and amino, respectively, which interacted with flavors and led to changes in the sensors' spectra. All the Raman spectra acquired from the nanosensor array contacting with the sample were concatenated into a single SERS super-spectrum, representing the flavor fingerprint which was recognized through machine learning. Principal component analysis, support vector machine, and partial least squares were utilized to build classification and quantitation models for predictive analyses. The SERS nanosensor array was successfully used for fingerprinting ten typical flavors in Baijiu including four esters, three alcohols, and three acids, with an accuracy of 100%, linear detection ranges over two orders of magnitude, and limits of detection ranging from 3.45 × 10^-3 mg/L of phenylethyl acetate to 1.21 × 10^-2 mg/L of ethyl hexanoate. It was also demonstrated that satisfactory accuracies (recoveries) ranging from 96.2 to 104% and relative standard deviations ranging from 0.65 to 2.78% were obtained for the simultaneous quantification of 3-methylbutyl acetate and phenylethyl acetate in eighteen Baijiu samples of three flavor types including sauce flavor, strong flavor, and light flavor. Compared with the existing detection techniques, this chemical fingerprinting platform is easy to use, highly sensitive, and can perform multiplex detection, which has great potential for practical applications.

Self-Assembly of Silver Nanowire Films for Surface-Enhanced Raman Scattering Applications

The development of SERS detection technology is challenged by the difficulty in obtaining SERS active substrates that are easily prepared, highly sensitive, and reliable. Many high-quality hotspot structures exist in aligned Ag nanowires (NWs) arrays. This study used a simple self-assembly method with a liquid surface to prepare a highly aligned AgNW array film to form a sensitive and reliable SERS substrate. To estimate the signal reproducibility of the AgNW substrate, the RSD of SERS intensity of 1.0 × 10^-10 M Rhodamine 6G (R6G) in an aqueous solution at 1364 cm^-1 was calculated to be as low as 4.7%. The detection ability of the AgNW substrate was close to the single molecule level, and even the R6G signal of 1.0 × 10^-16 M R6G could be detected with a resonance enhancement factor (EF) as high as 6.12 × 10¹¹ under 532 nm laser excitation. The EF without the resonance effect was 2.35 × 10⁶ using 633 nm laser excitation. FDTD simulations have confirmed that the uniform distribution of hot spots inside the aligned AgNW substrate amplifies the SERS signal.

Sensing Antibiotics in Wastewater Using Surface-Enhanced Raman Scattering

Rapid and cost-effective detection of antibiotics in wastewater and through wastewater treatment processes is an important first step in developing effective strategies for their removal. Surface-enhanced Raman scattering (SERS) has the potential for label-free, real-time sensing of antibiotic contamination in the environment. This study reports the testing of two gold nanostructures as SERS substrates for the label-free detection of quinoline, a small-molecular-weight antibiotic that is commonly found in wastewater. The results showed that the self-assembled SERS substrate was able to quantify quinoline spiked in wastewater with a lower limit of detection (LoD) of 5.01 ppb. The SERStrate (commercially available SERS substrate with gold nanopillars) had a similar sensitivity for quinoline quantification in pure water (LoD of 1.15 ppb) but did not perform well for quinoline quantification in wastewater (LoD of 97.5 ppm) due to interferences from non-target molecules in the wastewater. Models constructed based on machine learning algorithms could improve the separation and identification of quinoline Raman spectra from those of interference molecules to some degree, but the selectivity of SERS intensification was more critical to achieve the identification and quantification of the target analyte. The results of this study are a proof-of-concept for SERS applications in label-free sensing of environmental contaminants. Further research is warranted to transform the concept into a practical technology for environmental monitoring.

Surface-enhanced Raman scattering integrating with machine learning for green tea storage time identification

The rapid and accurate identification of complex samples still remains a great challenge, especially for those with similar compositions. In this work, we report an integration strategy consisting of surface-enhanced Raman scattering (SERS) and machine learning to discriminate complex and similar analytes, in this case green tea products with different storage times. Surface-functionalized Ag nanoparticles (NPs) were used as a SERS substrate to reveal the changes in the sensory components of green tea with variable storage time. Principal components analysis (PCA)-based support vector machine (SVM) classification was used to extract the key spectral features and identify green tea with different storage times. The results showed that such an integration strategy achieved high predictive accuracy on time tag discrimination for green tea. The multiclass SVM classifier successfully recognized green tea with different storage times at a prediction accuracy of 95.9%, sensitivity of 96.6%, and specificity of 98.8%. Therefore, this work illustrates that the SERS-based PCA-SVM platform might be a facile and reliable tool for the identification of complex matrices with subtle differentiations.

Nanosupernova: a new anisotropic nanostructure for SERS

Gold and/or silver nanostars are interesting anisotropic nanoparticles that have been used in surface-enhanced Raman scattering (SERS). In particular SERS nanotags consisting of gold nanostars and Raman reporter molecules have been widely utilised in biosensing and bioimaging. To improve the SERS activity of gold/silver nanostars, this paper details the development of a simple synthesis method that results in the formation of quasi-spherical SERS nanotags and larger highly anisotropic nanoparticles with a novel structure, which we have designated nanosupernova. The resulting SERS nanotags and nanosupernova contain gold/silver nanostars at their core, a self-assembled monolayer of Raman reporter molecules, and a final silver coating. The silver coating is the essential step responsible for the formation of the two types of particles, with incubation time, and type of Raman reporter molecule, the defining factor as to which forms. We discovered that the Raman reporter molecule, 5,5'-dithiobis-(2-nitrobenzoic acid) (DTNB), plays a crucial role in controlling the morphology of nanosupernova. We believe the larger highly anisotropic nanoparticles will open new applications in material sciences and in optical and electronic devices in the future.

Overview of Optical Biosensors for Early Cancer Detection: Fundamentals, Applications and Future Perspectives

Conventional cancer detection and treatment methodologies are based on surgical, chemical and radiational processes, which are expensive, time consuming and painful. Therefore, great interest has been directed toward developing sensitive, inexpensive and rapid techniques for early cancer detection. Optical biosensors have advantages in terms of high sensitivity and being label free with a compact size. In this review paper, the state of the art of optical biosensors for early cancer detection is presented in detail. The basic idea, sensitivity analysis, advantages and limitations of the optical biosensors are discussed. This includes optical biosensors based on plasmonic waveguides, photonic crystal fibers, slot waveguides and metamaterials. Further, the traditional optical methods, such as the colorimetric technique, optical coherence tomography, surface-enhanced Raman spectroscopy and reflectometric interference spectroscopy, are addressed.

Computational chromatography: A machine learning strategy for demixing individual chemical components in complex mixtures

Surface-enhanced Raman spectroscopy (SERS) holds exceptional promise as a streamlined chemical detection strategy for biological and environmental contaminants compared with current laboratory methods. Priority pollutants such as polycyclic aromatic hydrocarbons (PAHs), detectable in water and soil worldwide and known to induce multiple adverse health effects upon human exposure, are typically found in multicomponent mixtures. By combining the molecular fingerprinting capabilities of SERS with the signal separation and detection capabilities of machine learning (ML), we examine whether individual PAHs can be identified through an analysis of the SERS spectra of multicomponent PAH mixtures. We have developed an unsupervised ML method we call Characteristic Peak Extraction, a dimensionality reduction algorithm that extracts characteristic SERS peaks based on counts of detected peaks of the mixture. By analyzing the SERS spectra of two-component and four-component PAH mixtures where the concentration ratios of the various components vary, this algorithm is able to extract the spectra of each unknown component in the mixture of unknowns, which is then subsequently identified against a SERS spectral library of PAHs. Combining the molecular fingerprinting capabilities of SERS with the signal separation and detection capabilities of ML, this effort is a step toward the computational demixing of unknown chemical components occurring in complex multicomponent mixtures.

Toward Quantitative Surface-Enhanced Raman Scattering with Plasmonic Nanoparticles: Multiscale View on Heterogeneities in Particle Morphology, Surface Modification, Interface, and Analytical Protocols

Surface-enhanced Raman scattering (SERS) provides significantly enhanced Raman scattering signals from molecules adsorbed on plasmonic nanostructures, as well as the molecules' vibrational fingerprints. Plasmonic nanoparticle systems are particularly powerful for SERS substrates as they provide a wide range of structural features and plasmonic couplings to boost the enhancement, often up to >10⁸-10¹⁰. Nevertheless, nanoparticle-based SERS is not widely utilized as a means for reliable quantitative measurement of molecules largely due to limited controllability, uniformity, and scalability of plasmonic nanoparticles, poor molecular modification chemistry, and a lack of widely used analytical protocols for SERS. Furthermore, multiscale issues with plasmonic nanoparticle systems that range from atomic and molecular scales to assembled nanostructure scale are difficult to simultaneously control, analyze, and address. In this perspective, we introduce and discuss the design principles and key issues in preparing SERS nanoparticle substrates and the recent studies on the uniform and controllable synthesis and newly emerging machine learning-based analysis of plasmonic nanoparticle systems for quantitative SERS. Specifically, the multiscale point of view with plasmonic nanoparticle systems toward quantitative SERS is provided throughout this perspective. Furthermore, issues with correctly estimating and comparing SERS enhancement factors are discussed, and newly emerging statistical and artificial intelligence approaches for analyzing complex SERS systems are introduced and scrutinized to address challenges that cannot be fully resolved through synthetic improvements.

Recent Progress on Solid Substrates for Surface-Enhanced Raman Spectroscopy Analysis

Surface-enhanced Raman spectroscopy (SERS) is a powerful vibrational spectroscopy technique with distinguished features of non-destructivity, ultra-sensitivity, rapidity, and fingerprint characteristics for analysis and sensors. The SERS signals are mainly dependent on the engineering of high-quality substrates. Recently, solid SERS substrates with diverse forms have been attracting increasing attention due to their promising features, including dense hot spot, high stability, controllable morphology, and convenient portability. Here, we comprehensively review the recent advances made in the field of solid SERS substrates, including their common fabrication methods, basic categories, main features, and representative applications, respectively. Firstly, the main categories of solid SERS substrates, mainly including membrane substrate, self-assembled substrate, chip substrate, magnetic solid substrate, and other solid substrate, are introduced in detail, as well as corresponding construction strategies and main features. Secondly, the typical applications of solid SERS substrates in bio-analysis, food safety analysis, environment analysis, and other analyses are briefly reviewed. Finally, the challenges and perspectives of solid SERS substrates, including analytical performance improvement and largescale production level enhancement, are proposed.

Emerging nanosensor platforms and machine learning strategies toward rapid, point-of-need small-molecule metabolite detection and monitoring

Speedy, point-of-need detection and monitoring of small-molecule metabolites are vital across diverse applications ranging from biomedicine to agri-food and environmental surveillance. Nanomaterial-based sensor (nanosensor) platforms are rapidly emerging as excellent candidates for versatile and ultrasensitive detection owing to their highly configurable optical, electrical and electrochemical properties, fast readout, as well as portability and ease of use. To translate nanosensor technologies for real-world applications, key challenges to overcome include ultralow analyte concentration down to ppb or nM levels, complex sample matrices with numerous interfering species, difficulty in differentiating isomers and structural analogues, as well as complex, multidimensional datasets of high sample variability. In this Perspective, we focus on contemporary and emerging strategies to address the aforementioned challenges and enhance nanosensor detection performance in terms of sensitivity, selectivity and multiplexing capability. We outline 3 main concepts: (1) customization of designer nanosensor platform configurations via chemical- and physical-based modification strategies, (2) development of hybrid techniques including multimodal and hyphenated techniques, and (3) synergistic use of machine learning such as clustering, classification and regression algorithms for data exploration and predictions. These concepts can be further integrated as multifaceted strategies to further boost nanosensor performances. Finally, we present a critical outlook that explores future opportunities toward the design of next-generation nanosensor platforms for rapid, point-of-need detection of various small-molecule metabolites.

Classification of Preeclamptic Placental Extracellular Vesicles Using Femtosecond Laser Fabricated Nanoplasmonic Sensors

Placental extracellular vesicles (EVs) play an essential role in pregnancy by protecting and transporting diverse biomolecules that aid in fetomaternal communication. However, in preeclampsia, they have also been implicated in contributing to disease progression. Despite their potential clinical value, current technologies cannot provide a rapid and effective means of differentiating between healthy and diseased placental EVs. To address this, a fabrication process called laser-induced nanostructuring of SERS-active thin films (LINST) was developed to produce scalable nanoplasmonic substrates that provide exceptional Raman signal enhancement and allow the biochemical fingerprinting of EVs. After validating the performance of LINST substrates with chemical standards, placental EVs from tissue explant cultures were characterized, demonstrating that preeclamptic and normotensive placental EVs have classifiably distinct Raman spectra following the application of advanced machine learning algorithms. Given the abundance of placental EVs in maternal circulation, these findings encourage immediate exploration of surface-enhanced Raman spectroscopy (SERS) of EVs as a promising method for preeclampsia liquid biopsies, while this novel fabrication process will provide a versatile and scalable substrate for many other SERS applications.

A Polymer-Based Multichannel Sensor for Rapid Cell-Based Screening of Antibiotic Mechanisms and Resistance Development

Antibiotic resistance presents a critical threat to public health, necessitating the rapid development of novel antibiotics and an appropriate choice of therapeutics to combat refractory bacterial infections. Here, we report a high-throughput polymer-based sensor platform that rapidly (30 min) profiles mechanisms of antibiotic activity. The sensor array features three fluorophore-conjugated polymers that can detect subtle antibiotic-induced phenotypic changes on bacterial surfaces, generating distinct mechanism-based fluorescence patterns. Notably, discrimination of different generations of antibiotic resistance was achieved with high efficiency. This sensor platform combines trainability, simplicity, and rapid screening into a readily translatable platform.

A gold nanoparticle doped flexible substrate for microplastics SERS detection

Due to overuse of plastic products, decomposed microplastics (MPs) are widely spread in aquatic ecosystems, and will cause irreparable harm to the human body through the food chain. Traditional MP detection methods require cumbersome sample pre-processing procedures and complex instruments, so there is an urgent demand to develop methods to achieve simple on-site detection. Herein, a simple, sensitive, accurate, and stable MP detection method based on surface-enhanced Raman scattering (SERS) is investigated. Considering the hydrophobic problems of MPs, gold nanoparticle (AuNP) doped filter paper as a flexible SERS substrate is applied to capture MPs in the fiber pores. Benefitting from the electromagnetic (EM) hot spots generated by AuNPs, the Raman signal of MPs can be effectively enhanced. Meanwhile, the flexible SERS substrate has good sensitivity to a minimum detectable concentration of 0.1 g L^-1 for polyethylene terephthalate (PET) in water, and the maximum enhancement factor (EF) can reach 360.5. Furthermore, the practicability of the developed method has been proved by the successful detection of MPs in tap water and pond water. This research provides an easy process, high sensitivity, and good reproducibility method for MP detection.

Dielectric Walls/Layers Modulated 3D Periodically Structured SERS Chips: Design, Batch Fabrication, and Applications

As an indispensable constituent of plasmonic materials/dielectrics for surface enhanced Raman scattering (SERS) effects, dielectrics play a key role in excitation and transmission of surface plasmons which however remain more elusive relative to plasmonic materials. Herein, different roles of vertical dielectric walls, and horizontal and vertical dielectric layers in SERS via 3D periodic plasmonic materials/dielectrics structures are studied. Surface plasmon polariton (SPP) interferences can be maximized within dielectric walls besieged by plasmonic layers at the wall thicknesses of integral multiple half-SPPplasmonic material-dielectric -wavelength which effectively excites localized surface plasmon resonance to improve SERS effects by one order of magnitude compared to roughness and/or nanogaps only. The introduction of extra Au nanoparticles on thin dielectric layers can further enhance SERS effects only slightly. Thus, the designed Au/SiO2 based SERS chips show an enhancement factor of 8.9 × 1010 , 265 times higher relative to the chips with far thinner SiO2 walls. As many as 1200 chips are batch fabricated for a 4 in wafer using cost-effective nanoimprint lithography which can detect trace Hg ions as low as 1 ppt. This study demonstrates a complete generalized platform from design to low-cost batch-fabrication to applications for novel high performance SERS chips of any plasmonic materials/dielectrics.

Boosting bacteria differentiation efficiency with multidimensional surface-enhanced Raman scattering: the example of Bacillus cereus

Surface-enhanced Raman scattering (SERS) is a powerful tool for constructing biomolecular fingerprints, which play a vital role in differentiation of bacteria. Due to the rather subtle differences in the SERS spectra among different bacteria, artificial intelligence is usually adopted and enormous amounts of spectral data are required to improve the differentiation efficiency. However, in many cases, large volume data acquisition on bacteria is not only technical difficult but labour intensive. It is known that surface modification of SERS nanomaterials can bring additional dimensionality (difference) of the SERS fingerprints. Here in this work, we show that the concept could be used to improve the bacteria differentiation efficiency. Ag NPs were modified with 11-mercaptoundecanoic acid, 11-mercapto-1-undecanol, and 1-dodecanethiol to provide additional dimensionality. The modified NPs then were mixed with cell lysate from different strains of Bacillus cereus (B. cereus). Even by applying a simple PCA process to the resulting SERS spectra data, all the three modified Ag NPs showed superior differentiation results compared with bare Ag NPs, which could only separate Staphylococcus aureus (S. aureus) and B. cereus. It is believed that the multidimensional SERS could find great potential in bacteria differentiation.

Phenotyping Bacteria through a Black-Box Approach: Amplifying Surface-Enhanced Raman Spectroscopy Spectral Differences among Bacteria by Inputting Appropriate Environmental Stress

Surface-enhanced Raman spectroscopy (SERS) stands out in the field of microbial analysis due to its rich molecular information, fast analysis speed, and high sensitivity. However, achieving strain-level differentiation is still challenging because numerous bacterial species inevitably have very similar SERS profiles. Here, a method inspired by the black-box theory was proposed to boost the spectral differences, where the undifferentiated bacteria was considered as a type of black-box, external environmental stress was used as the input, and the SERS spectra of bacteria exposed to the same stress was output. For proof of the concept, three types of environmental stress were explored, i.e., ethanol, ultraviolet light (UV), and ultrasound. Enterococcus faecalis (E. faecalis) and three types of Escherichia coli (E. coli) were all subjected to the stimuli (stress) before SERS measurement. Then the collected spectra were processed only by simple principal component analysis (PCA) to achieve differentiation. The results showed that appropriate stress was beneficial to increase the differences in bacterial SERS spectra. When sonication at 490 W for 60 s was used as the input, the optimal differentiation of bacteria at the species (E. faecalis and E. coli) and strain-level (three E. coli) can be achieved.

Smart Contact Lens with Dual-Sensing Platform for Monitoring Intraocular Pressure and Matrix Metalloproteinase-9

Contact lenses have become a popular health-monitoring wearable device due to their direct contact with the eyes. By integrating biosensors into contact lenses, real-time and noninvasive diagnoses of various diseases can be realized. However, current contact lens sensors often require complex electronics, which may obstruct the user's vision or even damage the cornea. Moreover, most of the reported contact lens sensors can only detect one analyte. Therefore, an optical-based dual-functional smart contact lens sensor has been introduced to monitor intraocular pressure (IOP) and detect matrix metalloproteinase-9 (MMP-9), both of which are key biomarkers in many eye-related diseases such as glaucoma. Specifically, the elevated IOP is continuously monitored by applying an antiopal structure through color changes, without any complex electronics. Together with the peptide modified gold nanobowls (AuNBs) surface-enhanced Raman scattering (SERS) substrate, the quantitative analysis of MMP-9 at a low nanomolar range is achieved in real tear samples. The dual-sensing functions are thus demonstrated, providing a convenient, noninvasive, and potentially multifunctional sensing platform for monitoring health and diagnostic biomarkers in human tears.

Single-atom sites on perovskite chips for record-high sensitivity and quantification in SERS

Surface enhanced Raman scattering (SERS) is a rapid and nondestructive technique that is capable of detecting and identifying chemical or biological compounds. Sensitive SERS quantification is vital for practical applications, particularly for portable detection of biomolecules such as amino acids and nucleotides. However, few approaches can achieve sensitive and quantitative Raman detection of these most fundamental components in biology. Herein, a noble-metal-free single-atom site on a chip strategy was applied to modify single tungsten atom oxide on a lead halide perovskite, which provides sensitive SERS quantification for various analytes, including rhodamine, tyrosine and cytosine. The single-atom site on a chip can enable quantitative linear SERS responses of rhodamine (10^-6-1 mmol L^-1), tyrosine (0.06-1 mmol L^-1) and cytosine (0.2-45 mmol L^-1), respectively, which all achieve record-high enhancement factors among plasmonic-free semiconductors. The experimental test and theoretical simulation both reveal that the enhanced mechanism can be ascribed to the controllable single-atom site, which can not only trap photoinduced electrons from the perovskite substrate but also enhance the highly efficient and quantitative charge transfer to analytes. Furthermore, the label-free strategy of single-atom sites on a chip can be applied in a portable Raman platform to obtain a sensitivity similar to that on a benchtop instrument, which can be readily extended to various biomolecules for low-cost, widely demanded and more precise point-of-care testing or in-vitro detection.

ELECTRONIC SUPPLEMENTARY MATERIAL

Supplementary material is available for this article at 10.1007/s40843-022-1968-5 and is accessible for authorized users.

Noble Metal Nanoparticle Biosensors: From Fundamental Studies toward Point-of-Care Diagnostics

Noble metal nanoparticles (NMNPs) have become firmly established as effective agents to detect various biomolecules with extremely high sensitivity. This ability stems from the collective oscillation of free electrons and extremely large electric field enhancement under exposure to light, leading to various light-matter interactions such as localized surface plasmon resonance (LSPR) and surface-enhanced Raman scattering. A remarkable feature of NMNPs is their customizability by mechanisms such as particle etching, growth, and aggregation/dispersion, yielding distinct color changes and excellent opportunities for colorimetric biosensing in user-friendly assays and devices. They are readily functionalized with a large variety of capping agents and biomolecules, with resultant bioconjugates often possessing excellent biocompatibility, which can be used to quantitatively detect analytes from physiological fluids. Furthermore, they can possess excellent catalytic properties that can achieve significant signal amplification through mechanisms such as the catalytic transformation of colorless substrates to colored reporters. The various excellent attributes of NMNP biosensors have put them in the spotlight for developing high-performance in vitro diagnostic (IVD) devices that are particularly well-suited to mitigate the societal threat that infectious diseases pose. This threat continues to dominate the global health care landscape, claiming millions of lives annually. NMNP IVDs possess the potential to sensitively detect infections even at very early stages with affordable and field-deployable devices, which will be key to strengthening infectious disease management. This has been the major focal point of current research, with a view to new avenues for early multiplexed detection of infectious diseases with portable devices such as smartphones, especially in resource-limited settings.In this Account, we provide an overview of our original inspiration and efforts in NMNP-based assay development, together with some more sophisticated IVD assays by ourselves and many others. Our work in the area has led to our recent efforts in developing IVDs for high-profile infectious diseases, including Ebola and HIV. We emphasize that integration with digital platforms represents an opportunity to establish and efficiently manage widespread testing, tracking, epidemiological intelligence, and data sharing backed by community participation. We highlight how digital technologies can address the limitations of conventional diagnostic technologies at the point of care (POC) and how they may be used to abate and contain the spread of infectious diseases. Finally, we focus on more recent integrations of noble metal nanoparticles with Raman spectroscopy for accurate, noninvasive POC diagnostics with improved sensitivity and specificity.

Prospects of Surface-Enhanced Raman Spectroscopy for Biomarker Monitoring toward Precision Medicine

Future precision medicine will be undoubtedly sustained by the detection of validated biomarkers that enable a precise classification of patients based on their predicted disease risk, prognosis, and response to a specific treatment. Up to now, genomics, transcriptomics, and immunohistochemistry have been the main clinically amenable tools at hand for identifying key diagnostic, prognostic, and predictive biomarkers. However, other molecular strategies, including metabolomics, are still in their infancy and require the development of new biomarker detection technologies, toward routine implementation into clinical diagnosis. In this context, surface-enhanced Raman scattering (SERS) spectroscopy has been recognized as a promising technology for clinical monitoring thanks to its high sensitivity and label-free operation, which should help accelerate the discovery of biomarkers and their corresponding screening in a simpler, faster, and less-expensive manner. Many studies have demonstrated the excellent performance of SERS in biomedical applications. However, such studies have also revealed several variables that should be considered for accurate SERS monitoring, in particular, when the signal is collected from biological sources (tissues, cells or biofluids). This Perspective is aimed at piecing together the puzzle of SERS in biomarker monitoring, with a view on future challenges and implications. We address the most relevant requirements of plasmonic substrates for biomedical applications, as well as the implementation of tools from artificial intelligence or biotechnology to guide the development of highly versatile sensors.

Overcoming Acidic H2O2/Fe(II/III) Redox-Induced Low H2O2 Utilization Efficiency by Carbon Quantum Dots Fenton-like Catalysis

Fenton reaction has important implications in biology- and environment-related remediation. Hydroxyl radicals (^•OH) and hydroxide (OH^-) were formed by a reaction between Fe(II) and hydrogen peroxide (H₂O₂). The acidic H₂O₂/Fe(II/III) redox-induced low H₂O₂ utilization efficiency is the bottleneck of Fenton reaction. Electron paramagnetic resonance, surface-enhanced Raman scattering, and density functional theory calculation indicate that the unpaired electrons in the defects of carbon quantum dots (CQDs) and the carboxylic groups at the edge have a synergistic effect on CQDs Fenton-like catalysis. This leads to a 33-fold higher H₂O₂ utilization efficiency in comparison with Fe(II)/H₂O₂ Fenton reaction, and the pseudo-first-order reaction rate constant (k_obs) increases 38-fold that of Fe(III)/H₂O₂ under equivalent conditions. The replacement of acidic H₂O₂/Fe(II/III) redox with CQD-mediated Fe(II/III) redox improves the sluggish Fe(II) generation. Highly effective production of ^•OH in CQDs-Fe(III)/H₂O₂ dramatically decreases the selectivity of toxic intermediate benzoquinone. The inorganic ions and dissolved organic matter (DOM) in real groundwater show negligible effects on the CQDs Fenton-like catalysis process. This work presents a process with a higher efficiency of utilization of H₂O₂in situ chemical oxidation (ISCO) to remove persistent organic pollutants.

Synthesis of gold nanoparticles@reduced porous graphene-modified ITO electrode for spectroelectrochemical detection of SARS-CoV-2 spike protein

Here, we reported the synthesis of reduced porous graphene oxide (rPGO) decorated with gold nanoparticles (Au NPs) to modify the ITO electrode. Then we used this highly uniform Au NPs@rPGO modified ITO electrode as a surface-enhanced Raman spectroscopy-active surface and a working electrode. The uses of the Au nanoparticles and porous graphene enhance the Raman signals and the electrochemical conductivity. COVID-19 protein-based biosensor was developed based on immobilization of anti-COVID-19 antibodies onto the modified electrode and its uses as a probe for capturing the COVID-19 protein. The developed biosensor showed the capability of monitoring the COVID-19 protein within a concentration range from 100 nmol/L to 1 pmol/L with a limit of detection (LOD) of 75 fmol/L. Furthermore, COVID-19 protein was detected based on electrochemical techniques within a concentration range from 100 nmol/L to 500 fmol/L that showed a LOD of 39.5 fmol/L. Finally, three concentrations of COVID-19 protein spiked in human serum were investigated. Thus, the present sensor showed high efficiency towards the detection of COVID-19.

Facile synthesis of Au@palladium oxide nano-sunflowers for ultrasensitive surface-enhanced Raman scattering analysis

Metal-semiconductor nanocomposites have gain more and more attention as novel surface-enhanced Raman scattering (SERS) substrates due to the coupling Raman enhancement mechanism between the nanocomponents. Herein, Au@palladium oxide (Au@PdOx) nanohybrids...

Flexible Plasmonic Biosensors for Healthcare Monitoring: Progress and Prospects

The noble metal nanoparticle has been widely utilized as a plasmonic unit to enhance biosensors, by leveraging its electric and/or optical properties. Integrated with the "flexible" feature, it further enables opportunities in developing healthcare products in a conformal and adaptive fashion, such as wrist pulse tracers, body temperature trackers, blood glucose monitors, etc. In this work, we present a holistic review of the recent advance of flexible plasmonic biosensors for the healthcare sector. The technical spectrum broadly covers the design and selection of a flexible substrate, the process to integrate flexible and plasmonic units, the exploration of different types of flexible plasmonic biosensors to monitor human temperature, blood glucose, ions, gas, and motion indicators, as well as their applications for surface-enhanced Raman scattering (SERS) and colorimetric detections. Their fundamental working principles and structural innovations are scoped and summarized. The challenges and prospects are articulated regarding the critical importance for continued progress of flexible plasmonic biosensors to improve living quality.

Raman Scattering-Based Biosensing: New Prospects and Opportunities

The growing interest in the development of new platforms for the application of Raman spectroscopy techniques in biosensor technologies is driven by the potential of these techniques in identifying chemical compounds, as well as structural and functional features of biomolecules. The effect of Raman scattering is a result of inelastic light scattering processes, which lead to the emission of scattered light with a different frequency associated with molecular vibrations of the identified molecule. Spontaneous Raman scattering is usually weak, resulting in complexities with the separation of weak inelastically scattered light and intense Rayleigh scattering. These limitations have led to the development of various techniques for enhancing Raman scattering, including resonance Raman spectroscopy (RRS) and nonlinear Raman spectroscopy (coherent anti-Stokes Raman spectroscopy and stimulated Raman spectroscopy). Furthermore, the discovery of the phenomenon of enhanced Raman scattering near metallic nanostructures gave impetus to the development of the surface-enhanced Raman spectroscopy (SERS) as well as its combination with resonance Raman spectroscopy and nonlinear Raman spectroscopic techniques. The combination of nonlinear and resonant optical effects with metal substrates or nanoparticles can be used to increase speed, spatial resolution, and signal amplification in Raman spectroscopy, making these techniques promising for the analysis and characterization of biological samples. This review provides the main provisions of the listed Raman techniques and the advantages and limitations present when applied to life sciences research. The recent advances in SERS and SERS-combined techniques are summarized, such as SERRS, SE-CARS, and SE-SRS for bioimaging and the biosensing of molecules, which form the basis for potential future applications of these techniques in biosensor technology. In addition, an overview is given of the main tools for success in the development of biosensors based on Raman spectroscopy techniques, which can be achieved by choosing one or a combination of the following approaches: (i) fabrication of a reproducible SERS substrate, (ii) synthesis of the SERS nanotag, and (iii) implementation of new platforms for on-site testing.