Spectroscopy Substrates Research Articles

Qualitative and quantitative analysis of four benzimidazole residues in food by surface-enhanced Raman spectroscopy combined with chemometrics.

In this study, surface-enhanced Raman spectroscopy (SERS) combined with chemometric methods were developed for qualitative and quantitative analysis of four benzimidazole (BMZs) residues in corn. Sulfhydryl functionalized Fe3O4@SiO2@Ag-SH magnetic SERS substrates were prepared to obtain the SERS spectra of four BMZs for chemometric analysis. The partial least squares regression discrimination analysis (PLS-DA) model performed best, with a recall rate upwards 99.17%, and could successfully distinguish four BMZs. Under the support vector machine regression (SVR) model, the detection limits of carbendazim, benomyl, thiophanate-methyl and thiabendazole were 0.055mg/L, 0.056mg/L, 0.067mg/L and 0.093mg/L, respectively; the average recovery was in the range of 85.6%-107.5%. Furthermore, the method verified by HPLC, and the results showed that there was no significant difference between two methods (p>0.05). Therefore, the strategy based on SERS coupling chemometrics can be served as a promising tool for rapid determination of BMZs residues in food.

Superhydrophobic Surface Modification of Polymer Microneedles Enables Fabrication of Multimodal Surface-Enhanced Raman Spectroscopy and Mass Spectrometry Substrates for Synthetic Drug Detection in Blood Plasma.

Microneedles are widely used substrates for various chemical and biological sensing applications utilizing surface-enhanced Raman spectroscopy (SERS), which is indeed a highly sensitive and specific analytical approach. This article reports the fabrication of a nanoparticle (NP)-decorated microneedle substrate that is both a SERS substrate and a substrate-supported electrospray ionization (ssESI) mass spectrometry (MS) sample ionization platform. Polymeric ligand-functionalized gold nanorods (Au NRs) are adsorbed onto superhydrophobic surface-modified polydimethylsiloxane (PDMS) microneedles through the control of various interfacial interactions. We show that the chain length of the polymer ligands dictates the NR adsorption process. Importantly, assembling Au NRs onto the micrometer-diameter needle tips allows the formation of highly concentrated electromagnetic hot spots, which provide the SERS enhancement factor as high as 1.0 × 106. The micrometer-sized area of the microneedle top and high electromagnetic field enhancement of our system can be loosely compared with tip-enhanced Raman spectroscopy, where the apex of a plasmonic NP-functionalized sharp probe produces high-intensity plasmonic hot spots. Utilizing our NR-decorated microneedle substrates, the synthetic drugs fentanyl and alprazolam are analyzed with a subpicomolar limit of detection. Further analysis of drug-molecule interactions on the NR surface utilizing the Langmuir adsorption model suggests that the higher polarizability of fentanyl allows for a stronger interaction with hydrophilic polymer layers on the NR surface. We further demonstrate the translational aspect of the microneedle substrate for both SERS- and ssESI-MS-based detection of these two potent drugs in 10 drug-of-abuse (DOA) patient plasma samples with minimal preanalysis sample preparation steps. Chemometric analysis for the SERS-based detection shows a very good classification between fentanyl, alprazolam, or a mixture thereof in our selected 10 samples. Most importantly, ssESI-MS analysis also successfully identifies fentanyl or alprazolam in these same 10 DOA plasma samples. We believe that our multimodal detection approach presented herein is a highly versatile detection technology that can be applicable to the detection of any analyte type without performing any complicated sample preparation.

Reusability, Long‐Life Storage and Highly Sensitive Zirconium Nitride (ZrN) Surface‐Enhanced Raman Spectroscopy (SERS) Substrate Fabricated by Reactive Gas‐Timing Rf Magnetron Sputtering

AbstractTransition metal nitrides (TMN) are promising material alternative to replace noble metals in the field of plasmonic applications, especially surface‐enhanced Raman spectroscopy (SERS). Here we demonstrate a practical surface enhanced Raman spectroscopy (SERS) substrate using zirconium nitride (ZrN) thin films grown by reactive gas‐timing (RGT) rf magnetron sputtering. The tailored properties of ZrN thin film exploited for SERS activity could be achieved to obtain a highly sensitive ZrN thin film SERS substrate with the enhancement factor (EF) of 1.24 × 106 and 4.8 %RSD at 1626 cm‐1 toward methylene blue (MB) analyte which are comparable to the optimized Au sputtered thin films (EF=1.18 × 106 and with 5.1%RSD). We find that the spatial plasmonic hotspots on the surface of ZrN SERS substrate controlled by the turn‐on timing of Ar:N2 sputtered gas sequence, leading to the discrete conductive surface profile, strongly relates to non‐stoichiometric composition and the degree of (200)‐oriented texture at the surface of ZrN thin film. Furthermore, ZrN thin film SERS substrates exhibit an excellent recyclability more than 30 cycles with simple cleaning process and a storage time longer than 6 months. The detection and reusability of ZrN SERS substrate on the low concentration of trinitrotoluene (TNT) for homeland security are also performed.

Application of serum SERS technology combined with deep learning algorithm in the rapid diagnosis of immune diseases and chronic kidney disease

Surface-enhanced Raman spectroscopy (SERS), as a rapid, non-invasive and reliable spectroscopic detection technique, has promising applications in disease screening and diagnosis. In this paper, an annealed silver nanoparticles/porous silicon Bragg reflector (AgNPs/PSB) composite SERS substrate with high sensitivity and strong stability was prepared by immersion plating and heat treatment using porous silicon Bragg reflector (PSB) as the substrate. The substrate combines the five deep learning algorithms of the improved AlexNet, ResNet, SqueezeNet, temporal convolutional network (TCN) and multiscale fusion convolutional neural network (MCNN). We constructed rapid screening models for patients with primary Sjögren’s syndrome (pSS) and healthy controls (HC), diabetic nephropathy patients (DN) and healthy controls (HC), respectively. The results showed that the annealed AgNPs/PSB composite SERS substrates performed well in diagnosing. Among them, the MCNN model had the best classification effect in the two groups of experiments, with an accuracy rate of 94.7% and 92.0%, respectively. Previous studies have indicated that the AgNPs/PSB composite SERS substrate, combined with machine learning algorithms, has achieved promising classification results in disease diagnosis. This study shows that SERS technology based on annealed AgNPs/PSB composite substrate combined with deep learning algorithm has a greater developmental prospect and research value in the early identification and screening of immune diseases and chronic kidney disease, providing reference ideas for non-invasive and rapid clinical medical diagnosis of patients.

Construct high performance SERS sensing platform assisted by machine learning

Surface-Enhanced Raman Spectroscopy (SERS) is a powerful analytical technique for detecting trace analytes using noble metal nanoparticles. In this paper, we present a novel approach to construct a high-performance SERS sensing platform using self-assembled gold nanoparticles on aminated glass capillaries. The surface self-assembly technology ensures uniformity and repeatability of the SERS substrate, addressing the challenges of poor reproducibility observed in conventional methods. The 30 nm gold nanoparticles exhibit excellent plasmonic properties and biocompatibility, making them ideal candidates for SERS applications. We conducted SERS detection using Rhodamine 6G (R6G) as probe molecules, achieving a minimum detectable concentration of 0.1 nM for the AuNPs/GS substrate and 0.1 pM for the S-AuNPs/GC substrate. The S-AuNPs/GC substrate demonstrated commendable uniformity and repeatability, with a relative standard deviation of 12.1 %. Machine learning techniques, including baseline correction, normalization, and smoothing, were employed for data processing, enhancing the accuracy and reliability of the SERS analysis. By employing the K-means clustering algorithm, we identified three distinct groups of spectral characteristics. Additionally, Principal Component Analysis (PCA) allowed visualization and understanding of the clustering results in a two-dimensional space, capturing approximately 86.74 % of the data's variance. The successful construction of a high-performance SERS sensing platform with enhanced sensitivity, accuracy, and reliability, assisted by machine learning, holds great potential for various applications in chemical sensing, environmental monitoring, and biomedical diagnostics.

Tuning LSPR of Thermal Spike-Induced Shape-Engineered Au Nanoparticles Embedded in Si3N4 Thin-Film Matrix for SERS Applications.

While gold nanoparticles (Au NPs) are widely used as surface-enhanced Raman spectroscopy (SERS) substrates, their agglomeration and dynamic movement under laser irradiation result in the major drawback in SERS applications, viz., the repeatability of SERS signals. We tune the optical and structural properties of size- and shape-modified Au NPs embedded in a thin silicon nitride (Si3N4) matrix by intense electronic excitation with swift heavy ion (SHI) irradiation with the aim of overcoming this classical SERS disadvantage. We demonstrate the shape evolution of a single layer of Au NPs inserted between amorphous Si3N4 thin films under fluences of 120 MeV Au9+ ions ranging between 1 × 1011 and 1 × 1013 ions cm-2. This shape modification results in the gradual blue shift of the localized surface plasmon resonance (LSPR) dip until 1 × 1012 ions/cm2 and then a sudden diminishment at 1 × 1013 ions/cm2. Finite domain time difference (FDTD) simulations further justify our experimental optical spectra. The dynamical NP aggregation and dissolution, in addition to NP elongation and deformation at different fluences, are noted from 2D grazing incidence small-angle X-ray scattering (GISAXS) profiles, as well as cross-sectional transmission electron microscopy (X-TEM). The systematic shape evolution of metal NPs embedded in the insulating matrix is shown to be due to thermal spike-induced localized melting and a localized pressure hike upon SHI irradiation. Utilizing this specific control over the characteristics of Au NPs, viz., shape, size, interparticle gap, and corresponding optical response via SHI irradiation, we demonstrate their applications as very stable SERS substrates, where the separation between NPs and analyte does not alter under laser illumination. Thus, these irradiated SERS active substrates with controlled NP size and gap provide the optimal conditions for creating localized electromagnetic hotspots that amplify the SERS signals, which do not alter with time or laser exposure. We found that the film irradiated with 1 × 1011 exhibits the highest SERS intensity due to its optimal NP size distribution and shape. Thus, not only our study provides a SERS substrate for stable and repeatable signals but also the understanding depicted here opens new research avenues in designing SERS substrates, photovoltaics, optoelectronic devices, etc. with ion beam irradiation.

Surface-enhanced Raman spectroscopy for characterization of supernatant samples of biofilm forming bacterial strains

Staphylococcus epidermidis is considered major cause of nosocomial infections. Its pathogenicity is mainly due to the ability to form biofilms on different surfaces, particularly indwelling medical devices. This bacterium consists of different strains consisting of non, medium and strong biofilm forming ones. Surface-enhanced Raman spectroscopy (SERS) is a powerful analytical technique that can be used to detect and analyze biochemical composition of the supernatant samples of different strains of bacteria including non, medium and strong biofilm forming bacterial strains. SERS is a powerful technique for the robust, reliable, rapid detection and discrimination of bacteria in the form of characteristic SERS spectral features which can be used for detection and classification. SERS is used to differentiate three classes of bacteria with respect to their biofilm forming ability. Silver nanoparticles (Ag NPs) are used as SERS substrate and synthesized with chemical reduction method. Principal component analysis (PCA) and partial least square discriminant analysis (PLS-DA) are used to discriminate SERS spectral data sets of non, medium and strong biofilm forming bacteria. PLS-DA analysis is a multivariate statistical technique that can be used to analyze data from bacterial sets.

Facile electrochemical surface-alloying and etching of Au wires to enable high-performance substrates for surface enhanced Raman scattering

Surface-enhanced Raman Spectroscopy (SERS) is a nondestructive technique for rapid detection of analytes even at the single-molecule level. However, highly sensitive and reliable SERS substrates are mostly fabricated with complex nanofabrication techniques, greatly restricting their practical applications. A convenient electrochemical method for transforming the surface of commercial gold wires/foils into silver-alloyed nanostructures is demonstrated in this report. Au substrates are treated with repetitive anodic and cathodic bias in an electrolyte of thiourea, in a one-pot one-step manner. X-rays absorption fine structure (XAFS) spectroscopy confirms that the AuAg alloy is induced at the surface. The unique AuAg alloyed surface nanostructures are particularly advantageous when served as SERS substrates, enabling a remarkably sensitive detection of Rhodamine B (a detection limit of 10−14 ​M, and uniform strong response throughout the substrates at 10−12 ​M).

Deep Learning Assisted Surface-Enhanced Raman Spectroscopy (SERS) for Rapid and Direct Nucleic Acid Amplification and Detection: Toward Enhanced Molecular Diagnostics.

Surface-enhanced Raman scattering (SERS) has evolved into a robust analytical technique capable of detecting a variety of biomolecules despite challenges in securing a reliable Raman signal. Conventional SERS-based nucleic acid detection relies on hybridization assays, but reproducibility and signal strength issues have hindered research on directly amplifying nucleic acids on SERS surfaces. This study introduces a deep learning assisted ZnO-Au-SERS-based direct amplification (ZADA) system for rapid, sensitive molecular diagnostics. The system employs a SERS substrate fabricated by depositing gold on uniformly grown ZnO nanorods. These nanorods create hot spots for the amplification of the target nucleic acids directly on the SERS surface, eliminating the need for postamplification hybridization and Raman reporters. The limit of detection of the ZADA system was superior to those of the conventional amplification methods. Clinical validation of the ZADA system with coronavirus disease 2019 (COVID-19) samples from human patients yielded a sensitivity and specificity of 92.31% and 81.25%, respectively. The integration of a deep learning program further enhanced sensitivity and specificity to 100% and reduced SERS analysis time, showcasing the potential of the ZADA system for rapid, label-free disease diagnosis via direct nucleic acid amplification and detection within 20 min.

Atomic layer deposition assisted fabrication of insertable silver dendrites-based SERS substrates with high adhesion

Considerable efforts have been dedicated to the development of low-cost and highly sensitive surface-enhanced Raman spectroscopy (SERS) substrates, which have found applications in numerous fields. However, the critical factors of adhesion and stability have been insufficiently addressed. Such as Ag dendrites (Ag D) have been recognized for their cost-effectiveness and superior efficiency as SERS substrates, nonetheless, the issue of weak adhesion to the substrate has remained a challenge. Herein, a novel strategy to fabricate a Planar Ag dendrites-based SERS substrate with high adhesion is reported, that employs a combination of atomic layer deposition, roll milling, and chemical etching. The resulting Planar Ag dendrites show excellent adhesion, with a surface stripping force 18 times greater than that of pristine Ag dendrites, allowing for prolonged ultrasonic cleaning. Moreover, the substrate can be used as an insertable SERS sensor to obtain spectral information inside apples due to its excellent adhesion. The proposed design achieves high SERS sensitivity, with a limit of detection of 10-9 M and an analytical enhancement factor of 1.5 × 108 for R6G. Overall, this work provides a valuable reference for improving the adhesion of noble metal nanostructures on supporting substrates, offering a reliable SERS substrate for practical applications.

Enhanced phase transfer of Ag-nanospheres using amine-containing amphiphiles for sensitive chemical detection in nonaqueous environments

Effective phase transfer of Ag-nanospheres (AgNSs) was achieved through molecular interactions, rendering them interfacially compatible with various organic chemicals. This transfer was confirmed by distinct color changes and scanning electron microscope images, and the transferred AgNSs were employed as substrates for surface-enhanced Raman spectroscopy (SERS), enabling the sensitive detection of rhodamine 6G molecules dispersed in nonaqueous solutions. Notably, this detection capability extended to concentrations as low as 1 × 10−14 M. These findings underscore the potential of phase transferred AgNSs as versatile SERS substrates for sensitive chemical sensing in nonaqueous environments.

Super-Radiant SERS Enhancement by Plasmonic Particle Gratings.

Despite recent developments, surface-enhanced Raman spectroscopy (SERS) applications face challenges in achieving both high sensitivity and uniform Raman signals over a large area. Using the directional self-assembly of plasmonic nanoparticles in lattice structures, we show how one can increase the SERS signal 43-fold over randomly aligned gold nanoparticles without relying on the photoluminescence of Rhodamine 6G. For this study, we have chosen the lattice constant for an off-resonant case that matches the lattice resonance and super-radiant plasmon mode along the particle chain. Supported by electromagnetic simulations, we systematically analyze the radiative components of the plasmon modes by varying the particle size while keeping the lattice periodicity constant. We perform polarization-dependent SERS measurements and compare them with other standard SERS excitation wavelengths. Using the self-assembled plasmonic particle lattice, we have developed an effective SERS substrate that provides a significantly higher signal with 73% less surface coverage. This colloidal approach enables the cost-effective and scalable fabrication of highly sensitive, uniform, and polarization-dependent SERS substrates.

Highly sensitive detection of cationic pollutants on molybdenum carbide (MXene)/Fe2O3/Ag as a SERS substrate

Recent studies reported that the two-dimensional transition-metal carbides (MXenes) present promising results for surface-enhanced Raman spectroscopy (SERS) application, permitting sensitive detection of analyte molecules thanks to rich surface chemistries, surface plasmon resonances, and electrical features. However, Mxene materials, especially Mo-based ones, still suffer from low concentrated sensing activity and lack specific sensing suited to surface chemistries. To overcome those issues, for the first time, we propose a simple and scalable approach to fabricate Mo2CTx/Fe2O3/Ag (MFA) hybrid nanostructure via the hydrothermal treatment of MXene and Fe2O3, and subsequent in-situ decorated of Ag NPs via seed-mediated growth processes. As-prepared MFA presents a 1 × 10−9 M detection limit with a 1.46 × 106 enhancement factor for cationic MB molecules. Here, in addition to the contribution of the electromagnetic enhancement mechanism due to the presence of Ag, the chemical enhancement mechanism also comes into play as a result of the specific binding of the cationic molecule, MB, to the negatively charged MXene surface. This result is also observed for harmful cationic molecules found in wastewater such as CTAC and arsenic. MFA exhibits a low detection limit for CTAB, where a very low concentration of 0.075 M was achieved probably owing to its cationic character. Last but not least, the MFA hybrid nanostructure shows promising results for arsenic detection in tap water with 10 μg/L. Overall, an as-prepared MFA is a promising option for creating a SERS-active surface that can be customized for particular uses, due to its adjustable surface chemistries and elevated charge carrier densities, while also being low-cost and simple to manufacture.

2D MXenes as a Promising Candidate for Surface Enhanced Raman Spectroscopy: State of the Art, Recent Trends, and Future Prospects

AbstractSurface‐enhanced enhanced Raman spectroscopy (SERS) has emerged as a powerful analytical technique for ultrasensitive and label‐free detection of chemical species, with numerous applications in various fields. Recently, 2D MXenes, have evoked substantial intrigue as promising substrates for SERS. Hence, a comprehensive understanding of the developments in the Raman effect and the mechanisms involved in SERS is highly crucial. The review reflects the advances, working principle, and dual mechanisms, including SERS's electromagnetic and chemical mechanisms. Noble metal nanostructures are highly prioritized as SERS substrates owing to their excellent sensitivity. However, due to certain disadvantages that they pose, metal‐free SERS substrates with exceptional tunable properties are extensively researched in the current days. The combination of 2D MXenes and nanostructures can be effective in producing enhanced SERS signals. SERS performance of different MXene‐based materials is emphasized. The performance of this combination is credited to their large surface‐to‐volume ratio, good electrical conductivity, and surface‐terminated functionalities. The recent advancements in MXenes and MXenes‐based heterostructures driven SERS sensing concerning the structural design of the material, its performance, and the mechanisms are studied. Finally, a detailed conclusion is provided with the challenges and future perspectives for designing 2D materials for efficient SERS sensors.

Wafer-Scale Nanocracks Enable Single-Molecule Detection and on-Site Analysis

Large-area surface-enhanced Raman spectroscopy (SERS) sensing platforms displaying ultrahigh sensitivity and signal uniformity have potentially enormous sensing applicability, but they are still challenging to prepare in a scalable manner. In this study, silver nanopaste (AgNPA) was employed to prepare a wafer-scale, ultrasensitive SERS substrate. The self-generated, high-density Ag nanocracks (NCKs) with small gaps could be created on Si wafers via a spin-coating process, and provided extremely abundant hotspots for SERS analyses with ultrahigh sensitivity—down to the level of single molecules (enhancement factor: ca. 1010; detection limit: ca. 10–18 M)—and great signal reproducibility (variation: ca. 3.6%). Moreover, the Ag NCK arrays demonstrated broad applicability and practicability for on-site detection when combined with a portable 785 Raman spectrometer. This method allowed the highly sensitive detection of a diverse range of analytes (benzo[a]pyrene, di-2-ethylhexyl phthalate, aflatoxins B1, zearalenone, ractopamine, salbutamol, sildenafil, thiram, dimethoate, and methamidophos). In particular, pesticides are used extensively in agricultural production. Unfortunately, they can affect the environment and human health as a result of acute toxicity. Therefore, the simultaneous label-free detection of three different pesticides was demonstrated. Finally, the SERS substrates are fabricated through a simple, efficient, and scalable process that offers new opportunities for mass production. Figure 1

Qualitative and quantitative studies of phthalates in extra virgin olive oil (EVOO) by surface-enhanced Raman spectroscopy (SERS) combined with long short term memory (LSTM) neural network

Phthalates are commonly used plasticizers in the plastics industry, and have received extensive attention due to their reproductive toxicity. Since phthalates are lipophilic solutions, phthalates can easily migrate from packaging to edible oils. This study synthesized stable and sensitive Gold Nanostars as SERS substrates to conduct qualitative and quantitative analysis of two common phthalates, dibutyl phthalate and di(2-ethylhexyl) phthalate. Two ethanol standard solutions and actual oil solutions of phthalates at different concentrations (10, 5, 1, 0.5, 0.1, 0.02 mg/kg) were prepared. After dimension reduction, LSTM achieved the accuracy of 98% for pure EVOO and EVOO adulterated with different types of phthalates. In terms of quantification, LSTM demonstrates great predictive performance with Rp2 greater than 0.97 and the ratio of performance to deviation greater than 5. These results have certain guiding significance for the analysis of plasticizers in edible oil.

Amorphous/Crystal Heterostructure Coupled Oxygen Vacancies‐Sensitized TiO2 with Conspicuous Charge‐Transfer Resonance for Efficient SERS Detection of Chloramphenicol

AbstractAntibiotic environmental pollutants, such as chloramphenicol (CAP), have attracted global attention. They usually show a complex background, low concentration, and weak chemical activity in water bodies, making their rapid and on‐site detection a scientific challenge. Surface‐enhanced Raman spectroscopy (SERS) technology can facilitate the practical application of CAP detection; however, it is important to match substrate materials with excellent SERS properties. The most challenging aspect of SERS substrate design is the realization of energy level matching and charge‐transfer resonance. Herein, a crystal‐amorphous TiO2 substrate material rich in oxygen vacancies, CA‐V‐TiO2, is proposed. Through the cooperative regulation of vacancies and amorphous state, the bandgap structure can be optimized, the charge‐transfer efficiency can be improved, and finally the energy level matching between CA‐V‐TiO2 substrate and molecules can be realized and the conspicuous charge‐transfer resonance phenomenon can be generated, effectively optimizing the performance of SERS substrate. In addition, CA‐V‐TiO2 substrate is used for sensitive detection of CAP, showing multiple advantages such as low detection limit, good reproducibility, and high stability, which can realize trace CAP detection in actual water environments. This work provides a reliable platform for SERS on‐site detection of CAP in the field of food and environmental monitoring.

A Practical Nanoplasmonic SERS Substrate for Differential Diagnosis of Lung Normal and Cancer Cells through Multivariate Statistical Analysis

AbstractLung cancer ranks first for cancer‐related mortalities primarily due to late diagnosis. Though Surface‐Enhanced Raman spectroscopy (SERS) is a popular bioanalytical technique, its direct application to diagnosis is impeded by low data reproducibility. Colloidal nanoparticles suffer from SERS intensity fluctuations due to unavoidable aggregation, and Brownian and diffusion motions in biological samples. The processes for solid‐state SERS substrates are either sophisticated or difficult to reproduce. Herein, we revisit the well‐established thermal evaporation process for the easy and reproducible preparation of silver nanoparticles loaded SERS glass substrates. The static mode of thermal evaporation yielded closely packed and uniformly distributed silver nanoparticles. The properties of these nanoparticles are tuned for the best performance by controlling the thermal evaporation process. And SERS substrate exhibited a reasonably good enhancement factor of ~105 with uniformity and reproducibility <6 % RSD over a large area. It was utilized for label‐free SERS fingerprinting of lung adenocarcinoma cells A549 and normal lung fibroblast cells, WI‐38. The obtained data shows a slight distinction of Raman fingerprints in terms of certain biomolecules like nucleic acids, proteins, and lipids. Further multivariate statistical tools have been utilized which ensures a clear divergence between the cancerous cells and normal cells.

Using Machine Learning and Silver Nanoparticle-Based Surface-Enhanced Raman Spectroscopy for Classification of Cardiovascular Disease Biomarkers.

Characterizing complex biofluids using surface-enhanced Raman spectroscopy (SERS) coupled with machine learning (ML) has been proposed as a powerful tool for point-of-care detection of clinical disease. ML is well-suited to categorizing otherwise uninterpretable, patient-derived SERS spectra that contain a multitude of low concentration, disease-specific molecular biomarkers among a dense spectral background of biological molecules. However, ML can generate false, non-generalizable models when data sets used for model training are inadequate. It is thus critical to determine how different SERS experimental methodologies and workflow parameters can potentially impact ML disease classification of clinical samples. In this study, a label-free, broadband, Ag nanoparticle-based SERS platform was coupled with ML to assess simulated clinical samples for cardiovascular disease (CVD), containing randomized combinations of five key CVD biomarkers at clinically relevant concentrations in serum. Raman spectra obtained at 532, 633, and 785 nm from up to 300 unique samples were classified into physiological and pathological categories using two standard ML models. Label-free SERS and ML could correctly classify randomized CVD samples with high accuracies of up to 90.0% at 532 nm using as few as 200 training samples. Spectra obtained at 532 nm produced the highest accuracies with no significant increase achieved using multiwavelength SERS. Sample preparation and measurement methodologies (e.g., different SERS substrate lots, sample volumes, sample sizes, and known variations in randomization and experimental handling) were shown to strongly influence the ML classification and could artificially increase classification accuracies by as much as 27%. This detailed investigation into the proper application of ML techniques for CVD classification can lead to improved data set acquisition required for the SERS community, such that ML on labeled and robust SERS data sets can be practically applied for future point-of-care testing in patients.

Application of Anodic Titanium Oxide Modified with Silver Nanoparticles as a Substrate for Surface-Enhanced Raman Spectroscopy.

The formation of nanostructured anodic titanium oxide (ATO) layers was explored on pure titanium by conventional anodizing under two different operating conditions to form nanotube and nanopore morphologies. The ATO layers were successfully developed and showed optimal structural integrity after the annealing process conducted in the air atmosphere at 450 °C. The ATO nanopore film was thinner (1.2 +/- 0.3 μm) than the ATO nanotube layer (3.3 +/- 0.6 μm). Differences in internal pore diameter were also noticeable, i.e., 88 +/- 9 nm and 64 +/- 7 nm for ATO nanopore and nanotube morphology, respectively. The silver deposition on ATO was successfully carried out on both ATO morphologies by silver electrodeposition and Ag colloid deposition. The most homogeneous silver deposit was prepared by Ag electrodeposition on the ATO nanopores. Therefore, these samples were selected as potential surface-enhanced Raman spectroscopy (SERS) substrate, and evaluation using pyridine (aq.) as a testing analyte was conducted. The results revealed that the most intense SERS signal was registered for nanopore ATO/Ag substrate obtained by electrodeposition of silver on ATO by 2.5 min at 1 V from 0.05M AgNO3 (aq.) (analytical enhancement factor, AEF ~5.3 × 104) and 0.025 M AgNO3 (aq.) (AEF ~2.7 × 102). The current findings reveal a low-complexity and inexpensive synthesis of efficient SERS substrates, which allows modification of the substrate morphology by selecting the parameters of the synthesis process.