A Review on Surface-Enhanced Raman Scattering

Ag-modified CuO cavity arrays as a SERS-electrochemical dual signal platform for thiram detection

Rapid and sensitive determination of pesticide residues in fruits and vegetables is critical for human health and ecosystems. This paper used an Ag-modified CuO sphere-cavity array (CuO@Ag) electrode as a thiram SERS/electrochemical dual readout detection platform. Numerous Raman "hotspots" generated by uniformly distributed silver nanoparticles, charge transfer at the CuO@Ag interface, and the formation of Ag-thiram complexes contribute to the significant enhancement of this SERS substrate, which results in excellent SERS performance with an enhancement factor up to 1.42 × 106. When using SERS as the readout technique, the linear range of the substrate for thiram detection was 0.05-20 nM with a detection limit (LOD) of up to 0.0067 nM. Meanwhile, a correlation between the value of change in current density and thiram concentration was established due to the formation of stable complexes of thiram with Cu2+ generated at specific potentials. The linear range of electrochemical detection was 0.05-20.0 μM, and the detection limit was 0.0167 μM. The newly devised dual-readout sensor offers notable sensitivity and stability. The two signal readout methods complement each other in terms of linear range and detection limit, making it a convenient tool for assessing thiram residue levels in agro-food. At the same time, the combination of commercially available portable equipment makes on-site monitoring possible.

Extralong hot-spots sensor for SERS sensitive detection of phthalate plasticizers in biological tear and serum fluids

Phthalate plasticizers (PAEs) illegally used in food pose a great threat to human health. A new and efficient sensing platform for the sensitive detection of the PAE residues in biological fluids needs to be designed and developed. Here, we report a simple and reliable surface-enhanced Raman spectroscopy (SERS) active platform with extralong hot spots of Au nanobipyramids@Ag nanorods (Au NBPs@Ag NRs) for the rapid and sensitive detection of PAEs in biological fluids. To achieve high activity, Au NBPs@Ag NRs with different shell lengths were fabricated by controlling the synthesis conditions, and the corresponding SERS properties were investigated by using crystal violet (CryV) and butyl benzyl phthalate (BBP). The experimental results showed that a longer shell length correlated to greater Raman activity, which was confirmed by finite-difference time-domain (FDTD) electromagnetic simulation. More importantly, the extralong hot spots of the Au NBPs@Ag NR SERS-active substrate showed excellent homogeneity and reproducibility for the CryV probe molecules (6.21%), and the detection limit was 10-9 M for both BBP and diethylhexyl phthalate (DEHP). Furthermore, through the standard addition method, an extralong hot spots SERS substrate could achieve highly sensitive detection of BBP and DEHP in serum and tears fluids, and the detection limit was as low as 3.52 × 10-8 M and 2.82 × 10-8 M. Therefore, the Au NBPs@Ag NR substrate with an extraordinarily long surface is efficient and versatile, and can potentially be used for high-efficiency sensing analysis in complex biological fluids.

Beyond the Charge Transfer Mechanism for 2D Materials-Assisted Surface Enhanced Raman Scattering

Two-dimensional (2D) materials have been extensively implemented as surface-enhanced Raman scattering (SERS) substrates, enabling trace-molecule detection for broad applications. However, the accurate understanding of the mechanism remains elusive because most theoretical explanations are still phenomenological or qualitative based on simplified models and rough assumptions. To advance the development of 2D material-assisted SERS, it is vital to attain a comprehensive understanding of the enhancement mechanism and a quantitative assessment of the enhancement performance. Here, the microscopic chemical mechanism of 2D material-assisted SERS is quantitatively investigated. The frequency-dependent Raman scattering cross sections suggest that the 2D materials' SERS performance is strongly dependent on the excitation wavelengths and the molecule types. By analysis of the microscopic Raman scattering processes, the comprehensive contributions of SERS can be revealed. Beyond the widely postulated charge transfer mechanisms, the quantitative results conclusively demonstrate that the resonant transitions within 2D materials alone are also capable of enhancing the molecular Raman scattering through the diffusive scattering of phonons. Furthermore, all of these scattering routines will interfere with each other and determine the final SERS performance. Our results not only provide a complete picture of the SERS mechanisms but also demonstrate a systematic and quantitative approach to theoretically understand, predict, and promote the 2D materials SERS toward analytical applications.

A novel SERS-lateral flow assay (LFA) tray for monitoring of miR-155-5p during pyroptosis in breast cancer cells

In the study, a novel surface-enhanced Raman scattering (SERS)-lateral flow assay (LFA) tray for the real-time detection of pyroptosis-associated miR-155-5p in breast cancer cells was established and validated. The SERS probe modified with monoclonal antibodies and functionalized HP1@5-FAM was first synthesized. When miR-155-5p was present, HP1@5-FAM on the SERS probe specifically recognized target miRNAs and hybridized with them, resulting in HP2 on the T line only capturing some SERS probes that were not bound to miR-155-5p. The T line appeared as a light orange band or there was no color change, and the corresponding Raman detection result showed a weak or insignificant Raman signal. The SERS probe showed high selectivity, satisfactory stability, and excellent reproducibility, and the limit of detection (LOD) for miR-155-5p was 7.26 aM. Finally, the proposed SERS-LFA tray was applied to detect miR-155-5p in MBA-MD-468 cells that underwent varying degrees of pyroptosis, and the detection results of SERS were consistent with those of the conventional real-time quantitative reverse transcription polymerase chain reaction (qRT-PCR) assay. The study demonstrated that the SERS-LFA tray was a convenient and ultrasensitive method for miR-155-5p real-time detection, which could provide more detailed information for pyroptosis and be of potential value in guiding the treatment of breast cancer.

Pesticide Identification Using Surface-Enhanced Raman Spectroscopy and Density Functional Theory Calculations: From Structural Insights to On-Site Detection

Pesticides play an important role in conventional agriculture. Yet, their harmful effects on the environment are becoming increasingly apparent. The occurrence of pesticides is hence being monitored worldwide. For fast, easy, yet sensitive identification, surface-enhanced Raman spectroscopy (SERS) is a powerful tool. In this study, a method is introduced that may be amended to in-field detection of pesticides. Gold and silver nanoparticles were synthesized, size-tailored, and characterized. The herbicide paraquat and the fungicide thiram served as model compounds. The preparation yielded reproducible SERS spectra. Using quantum chemical computation, Raman and SERS spectra were calculated and analyzed. The interpretation of vibrational modes in combination with SERS enhancement and attenuation allowed us to identify compound-specific bands. The assignment was interpreted in terms of the orientation of paraquat and thiram on the gold and silver nanoparticle surfaces. Paraquat preferred a co-planar arrangement parallel to the gold nanoparticle surface and a head-on orientation on the silver nanoparticle. For thiram, breaking of the disulfide bond was recognized, such that interaction with the surface occurred via the sulfur atoms. Successful detection of the pesticides after recollection from vegetable leaves demonstrated the method's applicability for pesticide identification.

Nanoplastics Detected in Commercial Sea Salt

People of all ages consume salt every day, but is it really just salt? Plastic nanoparticles [nanoplastics (NPs)] pose an increasing environmental threat and have begun to contaminate everyday salt in consumer goods. Herein, we developed a combined surface enhanced Raman scattering (SERS) and stimulated Raman scattering (SRS) approach that can realize the filtration, enrichment, and detection of NPs in commercial salt. The Au-loaded (50 nm) anodic alumina oxide substrate was used as the SERS substrate to explore the potential types of NP contaminants in salts. SRS was used to conduct imaging and quantify the presence of the NPs. SRS detection was successfully established through standard plastics, and NPs were identified through the match of the hydrocarbon group of the nanoparticles. Simultaneously, the NPs were quantified based on the high spatial resolution and rapid imaging of the SRS imaging platform. NPs in sea salts produced in Asia, Australasia, Europe, and the Atlantic were studied. We estimate that, depending on the location, an average person could be ingesting as many as 6 million NPs per year through the consumption of sea salt alone. The potential health hazards associated with NP ingestion should not be underestimated.

Raman Scattering Enhancement through Pseudo-Cavity Modes

Raman spectroscopy plays a pivotal role in spectroscopic investigations. The small Raman scattering cross-section of numerous analytes, however, requires enhancement of the signal through specific structuring of the electromagnetic and morphological properties of the underlying surface. This enhancement technique is known as surface-enhanced Raman spectroscopy (SERS). Despite the existence of various proposed alternatives, the approach involving Fabry-Pérot cavities, which constitutes a straightforward method to enhance the electromagnetic field around the analyte, has not been extensively utilized. This is because, for the analyte to experience the maximum electric field, it needs to be embedded within the cavity. Consequently, the top mirror of the cavity will eventually shield it from the external laser source. Recently, an open-cavity configuration has been demonstrated to exhibit properties similar to the classic Fabry-Pérot configuration, with the added advantage of maintaining direct accessibility for the laser source. This paper showcases how such a simple yet innovative configuration can be effectively utilized to achieve remarkable Raman enhancement. The simple structure, coupled with its inexpensive nature and versatility in material selection and scalability, makes it an ideal choice for various analytes and integration into diverse Raman apparatus setups.

An in-situ method for SERS substrate preparation and optimization based on galvanic replacement reaction

BACKGROUND

Various surface-enhanced Raman spectroscopy (SERS) substrate preparation methods have been reported, however, how to tune the "gap" between nanostructures to make more "hot spots" is still a barrier that restricts their application. The gap between nanostructures is usually fixed when the substrates are prepared. In other words, it is hard to tune interparticle distances for maximum electromagnetic coupling during substrate preparation process. Therefore, an in-situ substrate optimization method that could monitor the SERS signal intensity changes, i.e., to find the optimum gap width and particle size, during substrate preparation process is needed.

RESULTS

A method based on the galvanic replacement reaction (GRR) is proposed for the in-situ gap width tuning between nanostructures as well as for the optimization of SERS substrates. Noble metal nanoparticles (NPs) form and grow on the sacrificial templates' surface while noble metal ions are reduced by sacrificial metal (oxides) in GRR. Along with the fresh and clean NPs' surface generated, the gap between two noble metal NPs decreases with the growth of the NPs. To demonstrate this strategy, cuprous oxide/Ti (Cu2O/Ti) sacrificial templates were prepared, and then a GRR was carried out with HAuCl4. The real-time SERS detection during GRR show that the optimum reaction time (ORT) is 300 ± 30 s. Furthermore, SERS performance testing was conducted on the optimized substrate, revealing that the detection limit for crystal violet can reach 1.96 × 10-11 M, confirming the feasibility of this method.

SIGNIFICANCE AND NOVELTY

By monitoring the in-situ SERS signal of probes during GRR will obtain an "optimal state" of the SERS substrate with optimal gap width and particle size. The SERS substrate preparation and optimization strategy proposed in this article not only provides a simple, efficient, and low-cost method to fabricate surface-clean noble NPs but also paves the way for the in-situ optimization of NPs size and gap width between NPs which could achieve wider applications of SERS.

Detection of surface enhanced Raman scattering active hotspot using near field scanning optical microscopy

Hotspots are high-intensity electromagnetic zones that form, for example, at the interstitials of plasmonic nanoaggregates, resulting in a considerable rise in the enhancement factor. However, it is inevitable to achieve specific nanometric geometry as well as a suitable technique to capture the details of hotspots. We report near-field surface-enhanced Raman scattering (SERS) spectroscopy of a well-defined gold nanoaggregate of a few nanoparticles adsorbed with a small number of target analytes. A spectrally and spatially resolved SERS measurement setup using an aperture near-field scanning optical microscope (a-NSOM) facilitated the direct observation of localized electromagnetic (EM) fields at the interstitials through SERS. Correlated optical image and corresponding nanometric geometry were captured through the home-built a-NSOM setup. Near-field SERS spectra were recorded at different sites of interest. It was evident that the interstitial positioned at the center of the tetramer provided the most intense Raman scattering, implying the possibility of a SERS-active hotspot therein. SERS bands of the spectrum of the Raman-active dye Rhodamine 6G recorded at the same hotspot coincided well with those reported so far. It was noteworthy that most of the SERS bands in such scenery got enhanced. Such direct observation with high spatial resolution is indispensable to understanding the origin of localized EM fields at "hotspots" and the EM enhancement factor in the SERS process. A finite-difference time-domain (FDTD) analysis was carried out to validate the results.

Pixelated High-Q Metasurfaces for in Situ Biospectroscopy and Artificial Intelligence-Enabled Classification of Lipid Membrane Photoswitching Dynamics

Nanophotonic devices excel at confining light into intense hot spots of electromagnetic near fields, creating exceptional opportunities for light-matter coupling and surface-enhanced sensing. Recently, all-dielectric metasurfaces with ultrasharp resonances enabled by photonic bound states in the continuum (BICs) have unlocked additional functionalities for surface-enhanced biospectroscopy by precisely targeting and reading out the molecular absorption signatures of diverse molecular systems. However, BIC-driven molecular spectroscopy has so far focused on end point measurements in dry conditions, neglecting the crucial interaction dynamics of biological systems. Here, we combine the advantages of pixelated all-dielectric metasurfaces with deep learning-enabled feature extraction and prediction to realize an integrated optofluidic platform for time-resolved in situ biospectroscopy. Our approach harnesses high-Q metasurfaces specifically designed for operation in a lossy aqueous environment together with advanced spectral sampling techniques to temporally resolve the dynamic behavior of photoswitchable lipid membranes. Enabled by a software convolutional neural network, we further demonstrate the real-time classification of the characteristic cis and trans membrane conformations with 98% accuracy. Our synergistic sensing platform incorporating metasurfaces, optofluidics, and deep learning reveals exciting possibilities for studying multimolecular biological systems, ranging from the behavior of transmembrane proteins to the dynamic processes associated with cellular communication.

TriFNet: A triple-branch feature fusion network for pH determination by surface-enhanced Raman spectroscopy

Due to the acidic tumor microenvironment caused by metabolic changes in tumor cells, the accurate pH detection of extracellular fluid is helpful for doctors in precise tumor resection. The combination of Raman spectroscopy and deep learning provides a solution for pH detection. However, most existing studies use one-dimensional convolutional neural networks (1D-CNNs) for spectral analysis, which limits the performance due to insufficient feature extraction. In this work, we propose a 2D triple-branch feature fusion network (TriFNet) for accurate pH determination using surface-enhanced Raman spectra (SERS). Specifically, we design a triple-branch network structure by converting Raman spectra into three types of images to extensively extract complex patterns in spectra. In addition, an attention fusion module, which leverages the complementarity among features in both space and channel, is designed to obtain the valuable information, achieving further accurate pH determination. On our Raman spectral dataset containing 14,137 samples, we achieved mean absolute error (MAE) of 0.059, standard deviation of the absolute error (SD) of 0.07, root mean squared error (RMSE) of 0.092, and coefficient of determination (R2) of 0.991 on the test set. Compared with other published methods, the four metrics showed an average improvement of 47%, 39%, 43%, and 6%, respectively. In addition, visualization validates the diagnostic capability of our model to correlate with biomolecular signatures. Meanwhile, our model has robustness to different SERS chips. These results prove the potential of our method to develop an effective technology based on Raman spectroscopy for accurate pH determination to guide surgery.

Implementation of multiomic mass spectrometry approaches for the evaluation of human health following environmental exposure

Omics analyses collectively refer to the possibility of profiling genetic variants, RNA, epigenetic markers, proteins, lipids, and metabolites. The most common analytical approaches used for detecting molecules present within biofluids related to metabolism are vibrational spectroscopy techniques, represented by infrared, Raman, and nuclear magnetic resonance (NMR) spectroscopies and mass spectrometry (MS). Omics-based assessments utilizing MS are rapidly expanding and being applied to various scientific disciplines and clinical settings. Most of the omics instruments are operated by specialists in dedicated laboratories; however, the development of miniature portable omics has made the technology more available to users for field applications. Variations in molecular information gained from omics approaches are useful for evaluating human health following environmental exposure and the development and progression of numerous diseases. As MS technology develops so do statistical and machine learning methods for the detection of molecular deviations from personalized metabolism, which are correlated to altered health conditions, and they are intended to provide a multi-disciplinary overview for researchers interested in adding multiomic analysis to their current efforts. This includes an introduction to mass spectrometry-based omics technologies, current state-of-the-art capabilities and their respective strengths and limitations for surveying molecular information. Furthermore, we describe how knowledge gained from these assessments can be applied to personalized medicine and diagnostic strategies.

A new copper nanocluster surface molecular imprinted polymethacrylic acid probe for ultratrace trichlorophenol based on in situ-generated nanogold SPR effects

A new coinage metal nanocluster surface molecularly imprinted polymethacrylic acid nanoprobe (NC@MIP) for the selective determination of 2,4,6-trichlorophenol (TCP) was prepared via microwave synthesis using 2,4,6-trichlorophenol as a template molecule, copper nanoclusters (CuNC) as a nanosubstrate, and methacrylic acid as a polymer monomer. It was found that the copper nanocluster MIP (CuNC@MIP) shows the strongest catalytic performance for the reduction of HAuCl4 by hydrazine hydrate for the on-site generation of gold nanoparticles (AuNPs) with the surface plasmon resonance (SPR) effects of resonance surface-enhanced Raman scattering (SERS) and resonance Rayleigh scattering (RRS) as well as absorption (Abs). When TCP was added, the CuNC@MIP nanoprobe and TCP-formed CuNC@MIP-TCP nanoenzyme with stronger catalytic activity generated more AuNPs, and the trimodal analytical signal was enhanced linearly. Therefore, a new SERS/RRS/Abs trimodal sensing platform for TCP was constructed, which was simple, rapid, sensitive, and selective. For each mode, the linear ranges were 0.0075-0.075, 0.010-0.10, and 0.010-0.10 nmol L-1, and the detection limits were 0.0010, 0.021, and 0.043 nmol L-1, respectively. The relative deviation of TCP in different water quality was 0.47%-2.5% and the recovery rate was 94.6%-108.6%.

Surface ligand modified silver nanoparticles-based SERS sensing platform for ultrasensitive detection of the pesticide thiram in green tea leaves: roles of coating agents in sensing performance

Silver nanoparticles (AgNPs) have been regarded as a highly promising substrate for surface-enhanced Raman scattering (SERS) sensors. In this study, we focused on the electrochemical synthesis method by developing three kinds of AgNPs using three different electrolytes: citrate (e-Ag-C), oleic acid (e-Ag-O) and fish mint (Houttuynia cordata Thunb.) extract (e-Ag-bio). The as-prepared AgNPs were characterized and then employed as SERS substrates to detect the pesticide thiram. The obtained results show that e-Ag-O exhibits the best SERS performance. The effect of the coating agent was explained by chemical and electromagnetic enhancements (CM and EM). Although thiram could absorb onto e-Ag-C at the highest level, allowing its Raman signal to be best enhanced via the CM, the smallest interparticle distance of e-Ag-O could have resulted in the largest improvement in the EM. Using e-Ag-O to develop SERS-based sensors for thiram, we obtain the impressive detection limit of 1.04 × 10-10 M in standard samples and 10-9 M in tea leaves. The linear ranges are from 10-4 M to 10-7 M and from 10-7 M to 10-9 M, covering the maximum residue levels for plant commodities established by the United States Environment Protection Agency and European Food Safety Authority (2-13 ppm ∼7.7 × 10-6 M to 5 × 10-5 M).

Highly flexible copper tape decorated with Ag nanoarrays as ultrasensitive SERS platforms for multi-hazardous pollutant sensing

A highly flexible and cost-effective copper tape decorated with silver nanoparticles (Cu-TAg) has been developed for surface-enhanced Raman spectroscopy (SERS) sensing of multi-hazardous environmental pollutants. Highly ordered and spherical-shaped silver nanoarrays have been fabricated using a low-cost thermal evaporation method. The structural, morphological, and optical properties of Cu-TAg sensors have been studied and correlated to the corresponding SERS performances. The size of nanoparticles has been successively tuned by varying the deposition time from 5 to 25 s. The nanoparticle sizes were enhanced with an increase in the evaporation time. SERS investigations have revealed that the sensing potential is subsequently improved with an increase in deposition time up to 10 s and then deteriorates with further increase in Ag deposition. The highest SERS activity was acquired for an optimum size of ~ 37 nm; further simulation studies confirmed this observation. Moreover, Cu-TAg sensors exhibited high sensitivity, reproducibility, and recycling characteristics to be used as excellent chemo-sensors. The lower detection limit estimation revealed that it can sense even in the pico-molar range for sensing of rhodamine 6G and methylene blue. The estimated enhancement factor of the sensor is found to be 9.4 × 107. Molecular-specific sensing of a wide range of pollutants such as rhodamine 6G, alizarin red, methylene blue, butylated hydroxy anisole, and penicillin-streptomycin is demonstrated with high efficiencies for micromolar spiked samples. Copper tape functionalized with Ag arrays thus demonstrated to be a promising candidate for low-cost and reusable chemo-sensors for environmental remediation applications.

Layer-by-Layer Biopolymer Assembly for the In Situ Fabrication of AuNP Plasmonic Paper-A SERS Substrate for Food Adulteration Detection

Here, we introduce an environmentally friendly approach to fabricate a simple and cost-effective plasmonic paper for detecting food additives using surface-enhanced Raman spectroscopy (SERS). The plasmonic paper is fabricated by in situ growth of gold nanoparticles (AuNPs) on filter paper (FP). To facilitate this green fabrication process, we applied a double-layered coating of biopolymers, chitosan (CS) and alginate (ALG), onto the FP using a layer-by-layer (LbL) assembly through electrostatic interactions. Compared to single-layer biopolymer coatings, double-layered biopolymer-coated paper, ALG/CS/FP, significantly improves the reduction properties. Consequently, effective in situ growth of AuNPs can be achieved as seen in high density of AuNP formation on the substrate. The resulting plasmonic paper provides high SERS performance with an enhancement factor (EF) of 5.7 × 1010 and a low limit of detection (LOD) as low as 1.37 × 10-12 M 4-mercaptobenzoic acid (4-MBA). Furthermore, it exhibits spot-to-spot reproducibility with a relative standard deviation (RSD) of 8.2% for SERS analysis and long-term stability over 50 days. This paper-based SERS substrate is applied for melamine (MEL) detection with a low detection limit of 0.2 ppb, which is sufficient for monitoring MEL contamination in milk based on food regulations. Additionally, we demonstrate a simultaneous detection of β-agonists, including ractopamine (RAC) and salbutamol (SAL), exhibiting the multiplexing capability and versatility of the plasmonic paper in food contaminant analysis. The development of this simple plasmonic paper through the LbL biopolymer assembly not only paves the way for novel SERS substrate fabrication but also broadens the application of SERS technology in food contaminant monitoring.

Gold nanoparticles for surface-enhanced Raman scattering detection of benzyldimethyldodecylammonium chloride at low concentration

The oil and gas industry plays a vital role in the global economy. The production process has several critical conditions and can expose metals to corrosion. Surfactants like the quaternary ammonium salt Benzyldimethyldodecylammonium Bromide (BDAC) are currently used to prevent corrosions; classical methods for determining these surfactants have problems in saline samples and usually present high costs. In this context, spectroscopic techniques become an excellent alternative for quaternary ammonium salts detection. Here, a SERS (surface-enhanced Raman scattering) sensor based on gold nanoparticles (AuNPs) synthesized through chemical reduction was used as an alternative method for BDAC detection. We detected BDAC at low concentrations in water solutions: at 5 to 30 ppm (1.47 × 10-5 mol L-1 to 8.82 × 10-5 mol L-1); and had the vibration attempt attribute analyzed. A new study of quaternary ammonium compounds using AuNPs and SERS with a different, easy, and repeatable approach to spectra acquisition is presented and shows to be a promising method applied in quaternary ammonium salt compounds detection for the oil and gas industry.

Bleach Cleaning of Commercially Available Gold Nanopillar Arrays for Surface-Enhanced Raman Spectroscopy (SERS)

Surface-enhanced Raman spectroscopy (SERS) is a highly sensitive technique that can assist in trace analysis for biomedical, diagnostic, and environmental applications. However, a major limitation of SERS is surface contamination of the substrates used, which can complicate the spectral reproducibility, limits of detection, and detection of unknown analytes. This is especially prevalent with commercially available substrates as shipping under a controlled and clean environment is difficult. Here we report a method using dilute bleach solutions to remove surface contamination from commercially available substrates consisting of gold-coated nanopillar arrays that maintains functionality. The results show that this method can be used to remove background signals associated with typical surface contamination in commercially available substrates as well as remove thiolated self-assembled monolayers (SAMs). Results indicate the bleach oxidizes the surface contaminants, which can then be easily washed away. Although the metallic surface also becomes oxidized in this process, the surface can be reduced without loss of SERS activity. The SERS intensity of SAMs improved following bleach treatment across all concentrations studied.

Pen direct writing of SERRS-based lateral flow assays for detection of penicillin G in milk

Direct pen writing offers versatile opportunities for development of low-cost tests for point-of-care applications. In this work a lateral flow immunoassay (LFIA) test was fabricated by hand "writing" immunoprobes onto hand-cut nitrocellulose strips with a commercial fountain pen. The qualitative capabilities of the test were extended by addition of a Raman reporter and consequent design and fabrication of a Surface Enhanced Resonant Raman Scattering (SERRS)-LFIA test. As proof-of-concept, dual detection of penicillin G was achieved in milk with a visual LOD of 20 ppm and a dynamic range of 0.03-97.5 ppm. Evaluation against equivalent tests performed with conventionally prepared LFIA strips showed comparable results, thus demonstrating the validity of the test. These results demonstrate the potential for further decrease in cost and consequent broader use of LFIA tests in remote regions and resource-limited environments.

Highly sensitive SERS platform for pathogen analysis by cyclic DNA nanostructure@AuNP tags and cascade primer exchange reaction

The capacity to identify small amounts of pathogens in real samples is extremely useful. Herein, we proposed a sensitive platform for detecting pathogens using cyclic DNA nanostructure@AuNP tags (CDNA) and a cascade primer exchange reaction (cPER). This platform employs wheat germ agglutinin-modified Fe3O4@Au magnetic nanoparticles (WMRs) to bind the E. coli O157:H7, and then triggers the cPER to generate branched DNA products for CDNA tag hybridization with high stability and amplified SERS signals. It can identify target pathogens as low as 1.91 CFU/mL and discriminate E. coli O157:H7 in complex samples such as water, milk, and serum, demonstrating comparable or greater sensitivity and accuracy than traditional qPCR. Moreover, the developed platform can detect low levels of E. coli O157:H7 in mouse serum, allowing the discrimination of mice with early-stage infection. Thus, this platform holds promise for food analysis and early infection diagnosis.

Deep Learning Enabled SERS Identification of Gaseous Molecules on Flexible Plasmonic MOF Nanowire Films

Through the capture of a target molecule at the metal surface with a highly confined electromagnetic field induced by surface plasmon, surface enhanced Raman spectroscopy (SERS) emerges as a spectral analysis technology with high sensitivity. However, accurate SERS identification of a gaseous molecule with low density and high velocity is still a challenge due to its difficulty in capture. In this work, a flexible paper-based plasmonic metal-organic framework (MOF) film consisting of Ag nanowires@ZIF-8 (AgNWs@ZIF-8) is fabricated for SERS detection of gaseous molecules. Benefiting from its micronanopores generated by the nanowire network and ZIF-8 shell, the effective capture of the gaseous molecule is achieved, and its SERS spectrum is obtained in this paper-based flexible plasmonic MOF nanowire film. With optimal structure parameters, spectra of gaseous 4-aminothiophenol, 4-mercaptophenol, and dithiohydroquinone demonstrate that this film has good SERS performance, which could maintain obvious Raman signals within 30 days during reproducible detection. To realize SERS identification of gaseous molecules, deep learning is performed based on the SERS spectra of the mixed gaseous analyte obtained in this flexible porous film. The results point out that an artificial neural network algorithm could identify gaseous aldehydes (gaseous biomarker of colorectal cancer) in simulated exhaled breath with high accuracy at 93.7%. The integration of the flexible paper-based film sensors with deep learning offers a promising new approach for noninvasive colorectal cancer screening. Our work explores SERS applications in gaseous analyte detection and has broad potential in clinical medicine, food safety, environmental monitoring, etc.

'When is a hotspot a good nanospot' - review of analytical and hotspot-dominated surface enhanced Raman spectroscopy nanoplatforms

Substrate development in surface-enhanced Raman spectroscopy (SERS) continues to attract research interest. In order to determine performance metrics, researchers in foundational SERS studies use a variety of experimental means to characterize the nature of substrates. However, often this process would appear to be performed indiscriminately without consideration for the physical scale of the enhancement phenomena. Herein, we differentiate between SERS substrates whose primary enhancing structures are on the hundreds of nanometer scale (analytical SERS nanosubstrates) and those whose main mechanism derives from nanometric-sized gaps (hot-spot dominated SERS substrates), assessing the utility of various characterization methods for each substrate class. In this context, characterization approaches in white-light spectroscopy, electron beam methods, and scanning probe spectroscopies are reviewed. Tip-enhanced Raman spectroscopy, wavelength-scanned SERS studies, and the impact of surface hydrophobicity are also discussed. Conclusions are thus drawn on the applicability of each characterization technique regarding amenability for SERS experiments that have features at different length scales. For instance, while white light spectroscopy can provide an indication of the plasmon resonances associated with 10 s-100 s nm-scale structures, it may not reveal information about finer surface texturing on the true nm-scale, critical for SERS' sensitivity, and in need of investigation via scanning probe techniques.

Nanogap engineering of 3D nanoraspberries into 2D plasmonic nanoclusters toward improved SERS performance

3D raspberry-like core/satellite nanostructures were prepared by controlled surface functionalization of silica spheres using crosslinked poly(4-vinylpyridine) (P4VP) chains with known binding affinity for gold nanoparticles (AuNPs). The 3D SiO2-g-P(4VP-co-DVB)/AuNP nanoraspberries can be further transformed into 2D plasmonic nanoclusters by etching the silica core with hydrofluoric acid (HF). After the transformation, the interparticle distance between the AuNPs dramatically reduced from a 10 nm scale to sub 2 nm. Owing to the strong electromagnetic field generated by the plasmonic coupling between AuNPs in very close proximity, the established P(4VP-co-DVB)/AuNP nanoclusters provided strong and undisturbed Raman signals as a SERS substrate. In addition, benefiting from the stabilizing effect of the crosslinked P(4VP-co-DVB) network, the prepared SERS substrate has the advantages of good uniformity, stability and reproducibility, as well as strong SERS enhancement, endowing it with great potential for rapid and efficient SERS detection.

Silver nanosheets self-assembled on polystyrene microspheres to form "hot spots" with different nanogap distances for high sensitive SERS detection

Herein, we reported a facile method to control the nanogap distance of silver (Ag) nanosheets to obtain high sensitive plasmonic Surface-enhanced Raman scattering (SERS) substrates. The sulfonated polystyrene (SPS) microspheres with different diameters were first fabricated using micro-emulsion synthesis, and then the SPS microspheres were coated with Ag nanosheets through chemical synthesis with citric acid/ascorbic acid to form Ag nanosheets@SPS (Ag@SPS) substrates with different nanogap distances. The results showed that the nanogap distance of Ag nanosheets self-assembled on SPS microspheres reduced from 80-150 nm to 28-68 nm when the diameter of SPS microspheres increased from 0.9 to 3.5 μm, and the enhancement factor (EF) increased from 105 to 107, the limit of detection of rhodamine 6G (R6G) for the Ag@SPS microspheres reduced from 10-10 to 10-13 mol/L. It confirmed that the Ag nanosheets coated on the surface of SPS microspheres could achieve ultra trace detection of analyte. Furthermore, the low concentration detection limit for melamine with the Ag@SPS microspheres substrate was about 10-8 mg/L, which is lower than the standard legislated by the European Union and the Food & Drug Administration. In addition, the SERS spectrum of 3-mercaptopropionic acid (3-MPA) could be also detected when its concentration was 10-8 mol/L. The prepared substrate offered a promising opportunity for SERS practical applications.

Machine learning enabled multiplex detection of periodontal pathogens by surface-enhanced Raman spectroscopy

Periodontitis is a chronic inflammation of the periodontium caused by a persistent bacterial infection, resulting in destruction of the supporting structures of teeth. Analysis of microbial composition in saliva can inform periodontal status. Actinobacillus actinomycetemcomitans (Aa), Porphyromonas gingivalis (Pg), and Streptococcus mutans (Sm) are among reported periodontal pathogens, and were used as model systems in this study. Our atomic force microscopic (AFM) study revealed that these pathogens are biological nanorods with dimensions of 0.6-1.1 μm in length and 500-700 nm in width. Current bacterial detection methods often involve complex preparation steps and require labeled reporting motifs. Employing surface-enhanced Raman spectroscopy (SERS), we revealed cell-type specific Raman signatures of these pathogens for label-free detection. It overcame the complexity associated with spectral overlaps among different bacterial species, relying on high signal-to-noise ratio (SNR) spectra carefully collected from pure species samples. To enable simple, rapid, and multiplexed detection, we harnessed advanced machine learning techniques to establish predictive models based on a large set of raw spectra of each bacterial species and their mixtures. Using these models, given a raw spectrum collected from a bacterial suspension, simultaneous identification of all three species in the test sample was achieved at 95.6 % accuracy. This sensing modality can be applied to multiplex detection of a broader range and a larger set of periodontal pathogens, paving the way for hassle-free detection of oral bacteria in saliva with little to no sample preparation.

Spectral effects and enhancement quantification in healthy human saliva with surface-enhanced Raman spectroscopy using silver nanopillar substrates

OBJECTIVES

Raman spectroscopy as a diagnostic tool for biofluid applications is limited by low inelastic scattering contributions compared to the fluorescence background from biomolecules. Surface-enhanced Raman spectroscopy (SERS) can increase Raman scattering signals, thereby offering the potential to reduce imaging times. We aimed to evaluate the enhancement related to the plasmonic effect and quantify the improvements in terms of spectral quality associated with SERS measurements in human saliva.

METHODS

Dried human saliva was characterized using spontaneous Raman spectroscopy and SERS. A fabrication protocol was implemented leading to the production of silver (Ag) nanopillar substrates by glancing angle deposition. Two different imaging systems were used to interrogate saliva from 161 healthy donors: a custom single-point macroscopic system and a Raman micro-spectroscopy instrument. Quantitative metrics were established to compare spontaneous RS and SERS measurements: the Raman spectroscopy quality factor (QF), the photonic count rate (PR), the signal-to-background ratio (SBR).

RESULTS

SERS measurements acquired with an excitation energy four times smaller than with spontaneous RS resulted in improved QF, PR values an order of magnitude larger and a SBR twice as large. The SERS enhancement reached 100×, depending on which Raman bands were considered.

CONCLUSIONS

Single-point measurement of dried saliva with silver nanopillars substrates led to reproducible SERS measurements, paving the way to real-time tools of diagnosis in human biofluids.

CA19-9 and CEA biosensors in pancreatic cancer

Cancer is a complex pathophysiological condition causing millions of deaths each year. Early diagnosis is essential especially for pancreatic cancer. Existing diagnostic tools rely on circulating biomarkers such as Carbohydrate Antigen 19-9 (CA19-9) and Carcinoembryonic Antigen (CEA). Unfortunately, these markers are nonspecific and may be increased in a variety of disorders. Accordingly, diagnosis of pancreatic cancer generally involves more invasive approaches such as biopsy as well as imaging studies. Recent advances in biosensor technology have allowed the development of precise diagnostic tools having enhanced analytical sensitivity and specificity. Herein we examine these advances in the detection of cancer in general and in pancreatic cancer specifically. Furthermore, we highlight novel technologies in the measurement of CA19-9 and CEA and explore their future application in the early detection of pancreatic cancer.

Highly efficient, label free, ultrafast plasmonic SERS biosensor (silver nanoarrays/Si) to detect GJB2 gene expressed deafness mutations in real time validated with PCR studies

Rapid diagnostics of any gene mutations related to organ loss is highly demanded now-a days to consume time as well to reduce cost. Currently, Surface enhanced Raman spectroscopy (SERS) is evolved to be a rapid investigating tool to screen gene mutations down to single molecule sensing with regard to the design and development of substrates used for sensing. The current research focuses on particular towards direct detection of deafness mutations associated with single and dual sites related to GJB2 gene. SERS Sensor construction is achieved with plasmonic silver nanoarrays on Si (SNA/Si) substrate by effortless wet chemical methods (Reaction time: 35 s; Concentration: 20 mM). The fabricated SNA/Si facilitates direct sensing of the deafness mutations of GJB2 gene in single as well dual sites with the enhancement of plasmonic hotspots. Normal DNA DMF-33 (GGGGGG) as well as Mutant DNA at single site DMF-9 (GGGGG) were validated by their guanine fingerprint Raman bands intensity quenching for mutant DNA DMF-9 at 1366 cm-1 and 1595 cm-1 respectively. Likewise, double mutations in DMF-19 are substitutional from G to A, portrayed highly intense fingerprint of Adenine Raman bands at 739 cm-1, 1432 cm-1, 1572 cm-1 in comparison to normal DNA (DMF-33). The findings were well analyzed with Raman mapping data which carries almost 625 scans for each DNA sample. The fabricated sensor exhibited the highest sensitivity towards DNA detection down to 0.1 pg/μL with utmost reproducibility. The current study aims to bring in creation of library files for deafness mutations to facilitate clinical diagnostics in a simple and rapid approach.

Fabrication of an Optoplasmonic Raft with Improved SERS Performance Detecting Methamphetamine through Bubble Enrichment

In this work, a novel raft-like structure that combines noble metal nanoparticles (NPs) with an interconnected layer of hemispherical dielectric shell was fabricated and characterized. It was discovered that this hybrid material can enhance the optoplasmonic interaction between plasmonic and dielectric components, thereby improving the sensing performance in surface-enhanced Raman spectroscopy (SERS). Varied geometric parameters of the fabricated optoplasmonic raft, including the inner diameter and thickness of the dielectric shell, were attempted and analyzed through numerical simulation and experimental SERS measurements. With particular size, thickness, and incident orientation, the silica shell focuses the incident optical flow into the deposited silver NPs, undergoing similar near-field focusing behavior in comparison with other optoplasmonic entities. This optoplasmonic raft floating on the water surface is able to harvest the target molecules effectively through bubble enrichment, which rapidly captures and concentrates analytes from the aqueous phase. With a limited sampling time, the sensing performance of the developed optoplasmonic raft is improved by applying the optimized parameters involved in bubble enrichment. The substrates and corresponding enrichment method were implemented in the detection of methamphetamine (METH), achieving a limit of detection (LOD) down to 0.035 nM. As for practical onsite detection, the developed substrate and bubbling strategy were applied in an assembled set, employing a portable Raman spectrometer and an air pump. This set is able to detect METH dissolved in regular commercial beer, which is quite competent in the investigation of drug abuse.

Peeking into the Femtosecond Hot-Carrier Dynamics Reveals Unexpected Mechanisms in Plasmonic Photocatalysis

Plasmonic-driven photocatalysis may lead to reaction selectivity that cannot be otherwise achieved. A fundamental role is played by hot carriers, i.e., electrons and holes generated upon plasmonic decay within the metal nanostructure interacting with molecular species. Understanding the elusive microscopic mechanism behind such selectivity is a key step in the rational design of hot-carrier reactions. To accomplish that, we present state-of-the-art multiscale simulations, going beyond density functional theory, of hot-carrier injections for the rate-determining step of a photocatalytic reaction. We focus on carbon dioxide reduction, for which it was experimentally shown that the presence of a rhodium nanocube under illumination leads to the selective production of methane against carbon monoxide. We show that selectivity is due to a (predominantly) direct hole injection from rhodium to the reaction intermediate CHO. Unexpectedly, such an injection does not promote the selective reaction path by favoring proper bond breaking but rather by promoting bonding of the proper molecular fragment to the surface.

Molecular Simulation Study of Surface-Enhanced Raman Scattering of Liquid Water

We developed in our previous study [J. Chem. Phys., 2021, 155, 174118, J. Phys. Chem. A, 2022, 126, 4762] a classical electronic and molecular dynamics simulation method to describe the optical response of metal material in solution based on an atomistic model by incorporating the classical equation of motion for free electrons under an applied electric field. To show further usefulness of the method, in the present study, we apply it to surface-enhanced Raman scattering of liquid water to examine the signal enhancement of the solution system caused by plasmon resonance effects of a silver nanoparticle.

Advancing Brain Research through Surface-Enhanced Raman Spectroscopy (SERS): Current Applications and Future Prospects

Surface-enhanced Raman spectroscopy (SERS) has recently emerged as a potent analytical technique with significant potential in the field of brain research. This review explores the applications and innovations of SERS in understanding the pathophysiological basis and diagnosis of brain disorders. SERS holds significant advantages over conventional Raman spectroscopy, particularly in terms of sensitivity and stability. The integration of label-free SERS presents promising opportunities for the rapid, reliable, and non-invasive diagnosis of brain-associated diseases, particularly when combined with advanced computational methods such as machine learning. SERS has potential to deepen our understanding of brain diseases, enhancing diagnosis, monitoring, and therapeutic interventions. Such advancements could significantly enhance the accuracy of clinical diagnosis and further our understanding of brain-related processes and diseases. This review assesses the utility of SERS in diagnosing and understanding the pathophysiological basis of brain disorders such as Alzheimer's and Parkinson's diseases, stroke, and brain cancer. Recent technological advances in SERS instrumentation and techniques are discussed, including innovations in nanoparticle design, substrate materials, and imaging technologies. We also explore prospects and emerging trends, offering insights into new technologies, while also addressing various challenges and limitations associated with SERS in brain research.

Na2Ti3O7@RF@Ag Heterostructures as Efficient Substrates for SERS and Photocatalytic Applications

A multi-step procedure was effectively employed to synthesize innovative three-dimensional (3D) heterostructures encompassing sodium titanate (Na2Ti3O7) nanowire cores, an intermediate resorcinol-formaldehyde (RF) layer, and outer silver (Ag) nanoparticle sheaths, referred to as Na2Ti3O7@RF@Ag heterostructures. Initially, a one-step hydrothermal technique facilitated the direct growth of single-crystal Na2Ti3O7 nanowires onto a flexible Ti foil. Subsequently, a two-step wet chemical process facilitated the sequential deposition of an RF layer and Ag nanoparticles onto the Na2Ti3O7 nanowires at a low reaction temperature. Optimal concentrations of silver nitrate and L-ascorbic acid can lead to the cultivation of Na2Ti3O7@RF@Ag heterostructures exhibiting heightened surface-enhanced Raman scattering (SERS), which is particularly beneficial for the detection of rhodamine B (RhB) molecules. This phenomenon can be ascribed to the distinctive geometry of the Na2Ti3O7@RF@Ag heterostructures, which offer an increased number of hot spots and surface-active sites, thereby showcasing notable SERS enhancement, commendable reproducibility, and enduring stability over the long term. Furthermore, the Na2Ti3O7@RF@Ag heterostructures demonstrate remarkable follow-up as first-order chemical kinetic and recyclable photocatalysts for the photodecomposition of an RhB solution under UV light irradiation. This result can be attributed to the enhanced inhibition of electron-hole pair recombination and increased surface-active sites.

Hybrid cavity-antenna architecture for strong and tunable sideband-selective molecular Raman scattering enhancement

Plasmon resonances at the surface of metallic antennas allow for extreme enhancement of Raman scattering. Intrinsic to plasmonics, however, is that extreme field confinement lacks precise spectral control, which would hold great promise in shaping the optomechanical interaction between light and molecular vibrations. We demonstrate an experimental platform composed of a plasmonic nanocube-on-mirror antenna coupled to an open, tunable Fabry-Perot microcavity for selective addressing of individual vibrational lines of molecules with strong Raman scattering enhancement. Multiple narrow and intense optical resonances arising from the hybridization of the cavity modes and the plasmonic broad resonance are used to simultaneously enhance the laser pump and the local density of optical states, and are characterized using rigorous modal analysis. The versatile bottom-up fabrication approach permits quantitative comparison with the bare nanocube-on-mirror system, both theoretically and experimentally. This shows that the hybrid system allows for similar SERS enhancement ratios with narrow optical modes, paving the way for dynamical backaction effects in molecular optomechanics.

Fabrication of PEDOT:PSS-based solution gated organic electrochemical transistor array for cancer cells detection

Organic electrochemical transistor (OECT) was applied in chemical and biological sensing. In this work, we developed a simple and repeatable method to fabricate OECT array, which had been successfully used to detect cancer cells. PEDPT:PSS conductive film between source and drain electrodes were patterned through photolithography, which can achieve uniform devices with same electrical characterization. When MCF-7 cancer cells are captured on the PEDOT:PSS surface via specifical antibody, the transfer characteristic of OECT shifts to higher gate electrode voltage due to the electrostatic interaction between cancer cells and device. The effective gate voltage shift can reach about 63 mV when the concentration of cancer cells increased to 5000. The shift of effective gate voltage is related to the cancer cell morphology, which is increased in the first 1 h and decreased when the capture time was larger than 1 h. The device of OECT array can increase the sample flux and make the detection result more accurate. It is expected that OECT array will have promising practical applications in single cancer cell detection in the future.

Ag@AuNP-Functionalized Capillary-Based SERS Sensing Platform for Interference-Free Detection of Glucose in Urine Using SERS Tags with Built-In Nitrile Signal

A Ag@AuNP-functionalized capillary-based surface-enhanced Raman scattering (SERS) sensing platform for the interference-free detection of glucose using SERS tags with a built-in nitrile signal has been proposed in this work. Capillary-based SERS capture substrates were prepared by connecting 4-mercaptophenylboronic acid (MBA) to the surface of the Ag@AuNP layer anchored on the inner wall of the capillaries. The SERS tags with a built-in interference-free signal could then be fixed onto the Ag@AuNP layer of the capillary-based capture substrate based on the distinguished feature of glucose, which can form a bidentate glucose-boronic complex. Thus, many "hot spots" were formed, which produced an improved SERS signal. The quantitative analysis of glucose levels was realized using the interference-free SERS intensity of nitrile at 2222 cm-1, with a detection limit of about 0.059 mM. Additionally, the capillary-based disposable SERS sensing platform was successfully employed to detect glucose in artificial urine, and the new strategy has great potential to be further applied in the diagnosis and control of diabetes.

A Calibration-Free Optical Emission Spectroscopic Method to Determine the Composition of a Spark Discharge Plasma Used for AuAg Binary Nanoparticle Synthesis

Spark discharge generators (SDGs) employ controlled gaseous environments to induce spark ablation of non-insulating electrodes, resulting in the formation of various nanostructures in the gas phase. The method offers technological advantages such as continuous particle production, scalable yield, and minimal waste. Additionally, the versatility of the process enables the generation of alloy nanoparticles from various material combinations, including immiscible ones. In order to fully exploit its potential, understanding the atomic mixing process during electrode ablation, particularly in the case of dissimilar electrodes, is crucial. Temporally and spatially resolved optical emission spectroscopy (OES) has been previously demonstrated as an effective characterization tool for spark plasmas in SDGs. However, to gain a deeper insight into the vapor mixing process, it is essential to quantitatively determine the plasma composition in both space and time. This paper introduces a calibration-free OES-based method tailored for spark plasmas utilized in binary nanoparticle generation. The method introduces the so-called multi-element combinatory Boltzmann plots, which use intensity ratios of emission atomic lines from different materials, allowing for the direct estimation of total number concentration ratios. The approach is tested using synthetic spectra and validated with experimental spark spectra obtained near an alloyed gold-silver (AuAg) electrode with a known composition. The study demonstrates the capabilities and robustness of the proposed method, with a focus on the AuAg system due to its significance in plasmonic research and frequent synthesis using spark ablation.

A review on solution- and vapor-responsive sensors for the detection of phthalates

Phthalic acid esters, largely referred to as phthalates, are today acknowledged as important pollutants used in the manufacture of polyvinyl chloride (PVC)-based plastics, whose use extends to almost every aspect of modern life. The risk of exposure to phthalates is particularly relevant as high concentrations are regularly found in drinking water, food-contact materials and medical devices, motivating an immense body of research devoted to methods for their detection in liquid samples. Conversely, phthalate vapors have only recently been acknowledged as potentially important atmospheric pollutants and as early fire indicators; additionally, deposition of these vapors can pose significant problems to the proper functioning of spacecraft and diverse on-board devices, leading to major space agencies recognizing the need of developing vapor-responsive phthalate sensors. In this manuscript we present a literature survey on solution- and vapor-responsive sensors and analytical assays for the detection of phthalates, providing a detailed analysis of a vast array of analytical data to offer a clear idea on the analytical performance (limits of detection and quantification, linear range) and advantages provided by each class of sensor covered in this review (electrochemical, optical and vapor-responsive) in the context of their potential real-life applications; the manuscript also gives detailed fundamental information on the various physicochemical responses exploited by these sensors and assays that could potentially be harnessed by new researchers entering the field.

Bimetallic copper palladium nanorods: plasmonic properties and palladium content effects

Cu is an inexpensive alternative plasmonic metal with optical behaviour comparable to Au but with much poorer environmental stability. Alloying with a more stable metal can improve stability and add functionality, with potential effects on the plasmonic properties. Here we investigate the plasmonic behaviour of Cu nanorods and Cu-CuPd nanorods containing up to 46 mass percent Pd. Monochromated scanning transmission electron microscopy electron energy-loss spectroscopy first reveals the strong length dependence of multiple plasmonic modes in Cu nanorods, where the plasmon peaks redshift and narrow with increasing length. Next, we observe an increased damping (and increased linewidth) with increasing Pd content, accompanied by minimal frequency shift. These results are corroborated by and expanded upon with numerical simulations using the electron-driven discrete dipole approximation. This study indicates that adding Pd to nanostructures of Cu is a promising method to expand the scope of their plasmonic applications.

Exploring Reliable and Efficient Plasmonic Nanopatterning for Surface- and Tip-Enhanced Raman Spectroscopies

Surface-enhanced Raman scattering (SERS) is of growing interest for a wide range of applications, especially for biomedical analysis, thanks to its sensitivity, specificity, and multiplexing capabilities. A crucial role for successful applications of SERS is played by the development of reproducible, efficient, and facile procedures for the fabrication of metal nanostructures (SERS substrates). Even more challenging is to extend the fabrication techniques of plasmonic nano-textures to atomic force microscope (AFM) probes to carry out tip-enhanced Raman spectroscopy (TERS) experiments, in which spatial resolution below the diffraction limit is added to the peculiarities of SERS. In this short review, we describe recent studies performed by our group during the last ten years in which novel nanofabrication techniques have been successfully applied to SERS and TERS experiments for studying bio-systems and molecular species of environmental interest.

Food Safety Issues in the Oltrepò Pavese Area: A SERS Sensing Perspective

Handly and easy-to-use optical instrumentation is very important for food safety monitoring, as it provides the possibility to assess law and health compliances at every stage of the food chain. In particular, the Surface-enhanced Raman Scattering (SERS) method appears highly promising because the intrinsic drawback of Raman spectroscopy, i.e., the natural weakness of the effect and, in turn, of the signal, is overcome thanks to the peculiar interaction between laser light and plasmonic excitations at the SERS substrate. This fact paved the way for the widespread use of SERS sensing not only for food safety but also for biomedicine, pharmaceutical process analysis, forensic science, cultural heritage and more. However, the current technological maturity of the SERS technique does not find a counterpart in the recognition of SERS as a routine method in compliance protocols. This is mainly due to the very scattered landscape of SERS substrates designed and tailored specifically for the targeted analyte. In fact, a very large variety of SERS substrates were proposed for molecular sensing in different environments and matrices. This review presents the advantages and perspectives of SERS sensing in food safety. The focus of the survey is limited to specific analytes of interest for producers, consumers and stakeholders in Oltrepò Pavese, a definite regional area that is located within the district of Pavia in the northern part of Italy. Our attention has been addressed to (i) glyphosate in rice fields, (ii) histamine in a world-famous local product (wine), (iii) tetracycline, an antibiotic often detected in waste sludges that can be dangerous, for instance in maize crops and (iv) Sudan dyes-used as adulterants-in the production of saffron and other spices, which represent niche crops for Oltrepò. The review aims to highlight the SERS performance for each analyte, with a discussion of the different methods used to prepare SERS substrates and the different reported limits of detection.

Recent trends in aptamer-based nanobiosensors for detection of vascular endothelial growth factors (VEGFs) biomarker: A review

Vascular endothelial growth factor (VEGF) is a remarkable cytokine that plays an important role in regulating vascular formation during the angiogenesis process. Therefore, real-time detection and quantification of VEGF is essential for clinical diagnosis and treatment due to its overexpression in various tumors. Among various sensing strategies, the aptamer-based sensors in combination with biological molecules improve the detection ability VEGFs. Aptamers are suitable biological recognition agents for the preparation of sensitive and reproducible aptasensors (Apt-sensors) due to their low immunogenicity, simple and straightforward chemical modification, and high resistance to denaturation. Here, a summary of the strategies for immobilization of aptamers (e.g., direct or self-assembled monolayer (SAM) attachment, etc.) on different types of electrodes was provided. Moreover, we discussed nanoparticle deposition techniques and surface modification methods used for signal amplification in the detection of VEGF. Furthermore, we are investigating various types of optical and electrochemical Apt-sensors used to improve sensor characterization in the detection of VEGF biomarkers.

Advances in surface-enhanced Raman spectroscopy-based sensors for detection of various biomarkers

Surface enhanced Raman spectroscopy (SERS) allows the ultrasensitive detection of analytes present in traces or even single molecule levels by the generation of electromagnetic fields. It is a powerful vibrational spectroscopic method that is capable to detect traces of chemical and biological analytes. SERS technique is involved in the extremely sophisticated studies of molecules with high specificity and sensitivity. In the vicinity of nanomaterials decorated surfaces, SERS can monitor extremely low concentrations of analytes in a non-destructive manner with narrow line widths. This review article is focused on some recently developed SERS-based sensors for distinct types of analytes like disease-related biomarkers, organic and inorganic molecules, various toxins, dyes, pesticides, bacteria as well as single molecules. This study aims to enlighten the arising sensing approaches based on the SERS technique. Apart from this, some basics of the SERS technique like their mechanism, detection strategy, and involvement of some specific nanomaterials are also highlighted herein. Finally, the study concluded with some discussion of applications of SERS in various fields like food and environmental analysis.

Peculiarities of the Structure of Au-TiO2 and Au-WO3 Plasmonic Nanocomposites

As nanotechnology continues to advance, the study of nanocomposites and their unique properties is at the forefront of research. There are still various blank spots in understanding the behavior of such composite materials, especially regarding plasmonic effects like localized surface plasmon resonance (LSPR) which is essential for developing advanced nanotechnologies. In this work, we explore the structural properties of composite thin films consisting of oxide matrices and gold nanoparticles (Au NPs), which were prepared by radio-frequency magnetron sputtering. Titanium dioxide (TiO2) and tungsten trioxide (WO3) were chosen as the host matrices of the composites. Such composite thin films owing to the presence of Au NPs demonstrate the LSPR phenomenon in the visible region. It is shown, that spectroscopic study, in particular, Raman spectroscopy can reveal peculiar features of structures of such composite systems due to LSPR and photoluminescence (PL) of Au NPs in the visible spectrum. In particular, defect peaks of TiO2 (700-720 cm-1) or WO3 (935 cm-1) in Raman spectra can be clearly observed when the samples are illuminated with a 633 nm excitation laser. Excitation with 532 nm leads to a decrease in the intensity of the defect peak, which totally disappears at 473 nm excitation. Such dependences of the defect peaks on excitation laser wavelength are probably related to the polarization of the matrix's defective regions close to the interface with gold NPs.

Investigation of high-order resonant modes for aluminium nanoparticles (arrays) using the finite-difference time-domain method

The optical properties of aluminum nanoparticles are simulated and calculated using the finite-difference time-domain (FDTD) method. Our research has given a comprehensive explanation of how the substrate's dielectric coefficients impact the surface plasmon resonance effect. Furthermore, it offers valuable insights into the role of substrate materials with different dielectric coefficients in modulating the surface plasmon resonance effect of aluminum nanoparticles. The simulation demonstrates the high sensitivity of the structure's surface plasmon resonance (SPR) to the particle size of aluminum nanoparticles. Primarily due to the short-wavelength resonance characteristics, as the particle size increases in the presence of a substrate, there is an overall red shift in the peak position compared to the case without a substrate. A non-metallic kind of substance, which is weakly coupled to the aluminum nanoparticles, has weak electric field enhancement; nevertheless the metal substrates confer significant electrically powered field enhancement to the system, and the height of the particles placed on the substrate also affects the SPR properties of the structure. For various specific needs or possible applications requiring different characteristic peaks, the SPR properties of the aluminum nanoparticle-substrate structure can be tuned by particle size and height.

RamanCMP: A Raman spectral classification acceleration method based on lightweight model and model compression techniques

In recent years, Raman spectroscopy combined with deep learning techniques has been widely used in various fields such as medical, chemical, and geological. However, there is still room for optimization of deep learning techniques and model compression algorithms for processing Raman spectral data. To further optimize deep learning models applied to Raman spectroscopy, in this study time, accuracy, sensitivity, specificity and floating point operations numbers(FLOPs) are used as evaluation metrics to optimize the model, which is named RamanCompact(RamanCMP). The experimental data used in this research are selected from the RRUFF public dataset, which consists of 723 Raman spectroscopy data samples from 10 different mineral categories. In this paper, 1D-EfficientNet adapted to the spectral data as well as 1D-DRSN are proposed to improve the model classification accuracy. To achieve better classification accuracy while optimizing the time parameters, three model compression methods are designed: knowledge distillation using 1D-EfficientNet model as a teacher model to train convolutional neural networks(CNN), proposing a channel conversion method to optimize 1D-DRSN model, and using 1D-DRSN model as a feature extractor in combination with linear discriminant analysis(LDA) model for classification. Compared with the traditional LDA and CNN models, the accuracy of 1D-EfficientNet and 1D-DRSN is improved by more than 20%. The time of the distilled model is reduced by 9680.9s compared with the teacher model 1D-EfficientNet under the condition of losing 2.07% accuracy. The accuracy of the distilled model is improved by 20% compared to the CNN student model while keeping inference efficiency constant. The 1D-DRSN optimized with channel conversion method saves 60% inference time of the original 1D-DRSN model. Feature extraction reduces the inference time of 1D-DRSN model by 93% with 94.48% accuracy. This study innovatively combines lightweight models and model compression algorithms to improve the classification speed of deep learning models in the field of Raman spectroscopy, forming a complete set of analysis methods and laying the foundation for future research.

Ultrasensitive detection of crystal violet using a molybdenum sulfide-silver nanostructure-based sensing platform: roles of the adsorbing semiconductor in SERS signal enhancement

Crystal violet (CV) is an organic dye that is stabilized by the extensive resonance delocalization of electrons over three electron-donating amine groups. This prevents the molecule from being linked to a metal surface, and therefore, reduces the sensitivity of surface-enhanced Raman scattering (SERS) sensors for this toxic dye. In this work, we improved the sensing performance of a silver-based SERS sensor for CV detection by modifying the active substrate. Molybdenum sulfide (MoS2) nanosheets were employed as a scaffold for anchoring electrochemically synthesized silver nanoparticles (e-AgNPs) through a single step of ultrasonication, leading to the formation of MoS2/Ag nanocomposites. As an excellent adsorbent, MoS2 promoted the adsorption of CV onto the surface of the substrate, allowing more CV molecules to be able to experience the SERS effect originating from the e-AgNPs. Hence, the SERS signal of CV was significantly enhanced. In addition, the effects of the MoS2 content of the nanocomposites on their SERS performance were also taken into account. Using MoS2/Ag with the most optimal MoS2 content of 10%, the SERS sensor exhibited the best enhancement of the SERS signal of CV with an impressive detection limit of 1.17 × 10-11 M in standard water and 10-9 M in tap water thanks to an enhancement factor of 2.9 × 106, which was 11.2 times higher than that using pure e-AgNPs.

Multiplex SERS detection of polycyclic aromatic hydrocarbon (PAH) pollutants in water samples using gold nanostars and machine learning analysis

Polycyclic aromatic hydrocarbons (PAHs) have attracted a lot of environmental concern because of their carcinogenic and mutagenic properties, and the fact they can easily contaminate natural resources such as drinking water and river water. This study presents a simple and sensitive point-of-care SERS detection of PAHs combined with machine learning algorithms to predict the PAH content more precisely and accurately in real-life samples such as drinking water and river water. We first synthesized multibranched sharp-spiked surfactant-free gold nanostars (GNSs) that can generate strong surface-enhanced Raman scattering (SERS) signals, which were further coated with cetyltrimethylammonium bromide (CTAB) for long-term stability of the GNSs as well as to trap PAHs. We utilized CTAB-capped GNSs for solution-based 'mix and detect' SERS sensing of various PAHs including pyrene (PY), nitro-pyrene (NP), anthracene (ANT), benzo[a]pyrene (BAP), and triphenylene (TP) spiked in drinking water and river water using a portable Raman module. Very low limits of detection (LOD) were achieved in the nanomolar range for the PAHs investigated. More importantly, the detected SERS signal was reproducible for over 90 days after synthesis. Furthermore, we analyzed the SERS data using artificial intelligence (AI) with machine learning algorithms based on the convolutional neural network (CNN) model in order to discriminate the PAHs in samples more precisely and accurately. Using a CNN classification model, we achieved a high prediction accuracy of 90% in the nanomolar detection range and an f1 score (harmonic mean of precision and recall) of 94%, and using a CNN regression model, achieved an RMSEconc = 1.07 × 10-1 μM. Overall, our SERS platform can be effectively and efficiently used for the accurate detection of PAHs in real-life samples, thus opening up a new, sensitive, selective, and practical approach for point-of-need SERS diagnosis of small molecules in complex practical environments.

Plasmonic Nanostructure Biosensors: A Review

Plasmonic nanostructure biosensors based on metal are a powerful tool in the biosensing field. Surface plasmon resonance (SPR) can be classified into localized surface plasmon resonance (LSPR) and propagating surface plasmon polariton (PSPP), based on the transmission mode. Initially, the physical principles of LSPR and PSPP are elaborated. In what follows, the recent development of the biosensors related to SPR principle is summarized. For clarity, they are categorized into three groups according to the sensing principle: (i) inherent resonance-based biosensors, which are sensitive to the refractive index changes of the surroundings; (ii) plasmon nanoruler biosensors in which the distances of the nanostructure can be changed by biomolecules at the nanoscale; and (iii) surface-enhanced Raman scattering biosensors in which the nanostructure serves as an amplifier for Raman scattering signals. Moreover, the advanced application of single-molecule detection is discussed in terms of metal nanoparticle and nanopore structures. The review concludes by providing perspectives on the future development of plasmonic nanostructure biosensors.

Evaluation and classification of fentanyl-related compounds using EC-SERS and machine learning

Multiple analytical techniques for the screening of fentanyl-related compounds exist. High discriminatory methods such as GC-MS and LC-MS are expensive, time-consuming, and less amenable to onsite analysis. Raman spectroscopy provides a rapid, inexpensive alternative. Raman variants such as electrochemical surface-enhanced Raman scattering (EC-SERS) can provide signal enhancements with 1010 magnitudes, allowing for the detection of low-concentration analytes, otherwise undetected using conventional Raman. Library search algorithms embedded in instruments utilizing SERS may suffer from accuracy when multicomponent mixtures involving fentanyl derivatives are analyzed. The complexing of machine learning techniques to Raman spectra demonstrates an improvement in the discrimination of drugs even when present in multicomponent mixtures of various ratios. Additionally, these algorithms are capable of identifying spectral features difficult to detect by manual comparisons. Therefore, the goal of this study was to evaluate fentanyl-related compounds and other drugs of abuse using EC-SERS and to process the acquired data using machine learning-convolutional neural networks (CNN). The CNN was created using Keras v 2.4.0 with Tensorflow v 2.9.1 backend. In-house binary mixtures and authentic adjudicated case samples were used to evaluate the created machine-learning models. The overall accuracy of the model was 98.4 ± 0.1% after 10-fold cross-validation. The correct identification for the in-house binary mixtures was 92%, while the authentic case samples were 85%. The high accuracies achieved in this study demonstrate the advantage of using machine learning to process spectral data when screening seized drug materials comprised of multiple components.