The aim of this study was to assess the potential of FTIR spectroscopy for monitoring biochemical changes in serum samples of individuals with carotid atherosclerosis following surgical intervention. Principal Component Analysis (PCA) of FTIR spectra from serum samples reveals distinct biochemical patterns at different time points: pre-surgery, 24 h post-surgery, and 48 h post-surgery. Two spectral ranges, 800–1800 cm−1 and 2800–3000 cm−1, were analyzed. PCA demonstrated that pre-surgery samples can be clearly differentiated from those taken 24 and 48 h post-surgery. However, no significant distinction was found between the 24-hour and 48-hour post-surgery samples. For the 800–1800 cm−1 range, the first principal component (PC1) explained 77.49% of the variance, highlighting the molecular vibrations of lipids, proteins, and carbohydrates. In the 2800–3000 cm−1 range, PC1 accounted for 94.89% of the variance, primarily reflecting lipid-related vibrations. These findings indicate a clear separation between pre-surgery and post-surgery samples, with the most significant variance explained by PC1. Additionally, the Boruta algorithm identified a key spectral range between 1506 cm−1 and 1673 cm−1, critical for distinguishing the samples. Classification models, including k-Nearest Neighbors, Gradient Boosting, Support Vector Machine, and Neural Network, demonstrated excellent performance in differentiating pre-surgery and post-surgery samples. However, the models struggled to distinguish between the 24-hour and 48-hour post-surgery time points. This suggests that FTIR spectroscopy may be useful for monitoring post-surgery recovery in carotid artery atherosclerosis, although subtle changes in the biochemical profile are challenging to detect between 24 and 48 h post-surgery.
Attenuated total reflectance-Fourier transform infrared spectroscopy (ATR-FTIR) provides information on the molecular composition and structure of samples. The use of ATR-FTIR was evaluated for biochemical analysis and taxonomic differentiation of entomopathogenic nematodes (EPNs). Spectra were obtained from a small sample (pellet) of a nematode population recovered from commercial EPN packages, which was placed directly on the ATR plate. Differences in signal intensity at multiple peaks associated with biomolecules critical to the survival of EPN (trehalose, glycogen, and triglyceride) were measured and visualized using Non-Metric Multidimensional Scaling (nMDS) and Principal Component Analysis (PCA). Statistically significant differences in peak signal intensity were observed between EPN species for each biochemical parameter, providing a basis for assessing the likelihood of their performance success in the field conditions. The present study also evaluated FTIR analysis of EPN for taxonomic differentiation. Results demonstrate that FTIR can be used to identify and differentiate Steinernema and Heterorhabditis genera/species, offering a potentially faster, less expensive alternative to molecular identification techniques. Ultimately, this study demonstrates the efficacy of ATR-FTIR as a reliable method for assessing the biochemical suitability of EPN products for field applications and differentiating between EPNs.
Endometrial cancer (EC) is increasingly prevalent worldwide, highlighting the need for non-invasive blood-based diagnostic triage tools. ATR-FTIR spectroscopy enables rapid, label-free biochemical profiling of plasma or serum for experimental cancer detection. To date, no systematic review or meta-analysis has evaluated the experimental performance of infrared spectroscopy for discriminating EC from non-cancer in blood-based samples. This study synthesizes available evidence to characterize the strength, consistency, and heterogeneity of the underlying spectroscopic signal across preclinical and proof-of-concept studies. MEDLINE, Web of Science, EMBASE, Scopus, Google Scholar, and CENTRAL were searched without language restrictions. Eligible studies evaluated ATR-FTIR spectroscopy of plasma or serum using histopathology as the reference standard. Pooled sensitivity, specificity, likelihood ratios, and diagnostic odds ratios were estimated using a bivariate random-effects model, with assessment of heterogeneity, threshold effects, and publication bias. Five case–control studies comprising 1376 participants were included. For plasma-based analyses, pooled sensitivity was 0.61 (95% CI: 0.59–0.68) and specificity was 0.73 (95% CI: 0.69–0.76), with a diagnostic odds ratio of 4.23 (95% CI: 3.33–5.37). For serum-based analyses, pooled sensitivity and specificity were both 0.62 (95% CI: 0.59–0.65), with a diagnostic odds ratio of 2.65 (95% CI: 2.16–3.25). Substantial heterogeneity and significant threshold effects were observed. Current evidence supports reproducible spectroscopic differences between EC and non-cancer blood samples under experimental conditions. However, methodological heterogeneity and retrospective case–control study designs limit clinical interpretability. These findings provide a benchmark for future prospective validation rather than immediate clinical application.
Cancer diagnostic methods based on Raman spectroscopy are being actively investigated, and there is a strong need for simple approaches to amplify the intensity of Raman scattered light from biological fluids such as serum and urine, which contain only trace amounts of nucleic acids, proteins, amino acids, and other analytes together with highly autofluorescent background components. We evaluated two measurement methods. One was the needle method (NM), in which a laser irradiates a droplet of liquid sample held at the tip of a fine-diameter stainless-steel needle. The other was the quartz glass fiber sheet method (QSM), in which a quartz glass fiber sheet is imbued with a liquid sample, allowed to dry, and then irradiated at the sheet surface. Raman spectra of sodium benzoate, sodium sulfate, human serum, and human urine were recorded. For the model compounds, spectra obtained by QSM reproduced the Raman shifts of the solid state, whereas spectra of aqueous solutions measured by NM showed clear peak shifts, and the scattered-light intensity increased monotonically with the number of drops on the sheet. Based on these findings, we infer that the samples crystallize and become concentrated within the quartz glass fiber sheet, enabling acquisition of spectra with high scattered-light intensity even from low-concentration solutions. For human serum and urine, QSM increased the intensity of characteristic bands by up to about seven-fold compared with NM while preserving the spectral fingerprints. Our results indicate that a quartz glass fiber sheet is a practical low-background substrate for obtaining FT-Raman spectra of liquid biological samples whose components are present at low concentrations.
Surface-enhanced Raman spectroscopy (SERS) enables rapid, label-free bacterial fingerprinting, yet many machine-learning studies lack transparent evaluation protocols and interpretable feature analysis. This study presents a SERS-machine-learning evaluation framework for bacterial classification using a fixed-concentration ten-class Staphylococcus aureus dataset (100 spectra) and leak-free 60/20/20 train/validation/test partitions repeated across 15 random seeds. After Savitzky-Golay smoothing and standard normal variate normalization, spectra were analyzed in both full (384 variables) and cropped (400–1200 cm−1; 298 variables) representations. Linear discriminant analysis (LDA) served as the primary dense classifier, while stability-selection-based feature selection combined with LDA (StabSel-LDA) was evaluated as a sparse companion model with validation-tuned feature count K. LDA achieved near-ceiling clean performance (mean accuracy 0.970–0.973; macro-F1 0.966–0.969) and remained stable under additive noise, drift, and ±5 cm−1 spectral shifts (accuracy 0.953). StabSel-LDA reduced the feature set to 124–228 bands and produced reproducible band-selection profiles across seeds, but showed lower clean accuracy (0.940–0.943) and pronounced degradation under ±5 cm−1 shifts (0.760–0.783). These results support a role-separated deployment strategy in which LDA functions as the primary decision model, while StabSel-LDA provides a sparse companion for feature compression and interpretable spectral-band identification. The proposed framework offers a transparent approach for balancing classification performance, robustness assessment, and spectral interpretability in small SERS datasets.
Metabolic syndrome (MetS) is characterized by central adiposity, hypertension, dyslipidemia, insulin resistance, and chronic inflammation, conditions that alter lipid regulation in multiple tissues, including skin. Fatty acidbinding proteins, particularly epidermal fatty acid-binding protein (E-FABP, FABP5), have been proposed as potential biomarkers of these metabolic and cardiovascular alterations; however, their contribution in skin has not been studied using vibrational spectroscopy. This work aimed to evaluate whether Raman spectroscopy can detect epidermal FABP-associated molecular changes in skin during the development of MetS. Male Wistar rats were fed a high-fat diet for up to 52 weeks, and in vivo Raman spectra of abdominal skin were acquired at several time points. Raman band-intensity analysis, principal component analysis, and logistic regression classification were applied to identify biochemical differences between the control and metabolic syndrome groups. The Raman bands at 1002 cm 1 , 1050 cm-1 , and 1377 cm 1 , associated with E-FABP, showed time-dependent differences, with the most significant increase at 18 weeks and convergence between groups by 52 weeks. Based on this unique spectral feature, we developed a noninvasive Raman workflow capable of detecting E-FAPB directly in living tissue, as a potential Raman biomarker of MetS. The classification model achieved 79% accuracy with an area under the curve of 0.85. In summary, our findings indicate that Raman spectroscopy can detect E-FABP alterations in skin associated with MetS, supporting its potential as a non-invasive tool for early metabolic assessment.
Aqueous thin layers on germanium surface have been investigated by infrared spectroscopy. Two doublets of water librations and one of atmospheric CO2 bound in the thin layer imply the aqueous thin layer structuring, namely formation of quasi-crystal structure. The layer structuring and theoretical estimations based on polaronic exciton concept allow to assign a broad band at 1124 cm-1 to longitudinal optical phonons observed in the IR spectrum. The building up of the broad band is interpreted in terms of Fermi resonance that involves two frequencies at 1556 and 1664 cm-1. Fermi coupling coefficient with a slight (36 cm-1) or strong perturbation of 220 cm-1 partakes in the mixing with the energy levels. The mixing results in unusual features of the IR spectra of the thin aqueous layers observed on germanium plates. It seems that the appearance of Fermi resonance in the structure of aqueous layer arises because of a brake of spin-orbit coupling in polaronic exciton. Namely, it arises because the repulsion between the spins of the opposite charges.\
To achieve accurate quality assessment of B. dorsalis-infested apples, this study proposes an early detection and firmness prediction method based on hyperspectral imaging (HSI) integrated with a one-dimensional convolutional neural network (1D-CNN). First, a hyperspectral imaging system (400-1000 nm) was used to acquire images over two consecutive years for three categories of apple samples: healthy, B. dorsalis-infested, and superficially similar scarred apples, while the firmness of each fruit was measured simultaneously. Subsequently, the successive projections algorithm (SPA) and competitive adaptive reweighted sampling (CARS) were applied to select characteristic bands related to fruit categories and firmness, respectively. Finally, early detection and firmness prediction models were developed by integrating partial least squares-discriminant analysis (PLS-DA), back-propagation neural network (BP), 1D-CNN, random forest (RF), support vector machine (SVM), and 1DCNN. The results showed that the SPA-1D-CNN detection model with 10 selected characteristic bands achieved satisfactory performance, with an overall classification accuracy of 94%, precision of 0.94, recall of 0.94, and an F1-score of 0.94. For firmness prediction, the SPA-1D-CNN model also performed well, For the calibration set, R2 c was 0.87, RMSEC was 0.76. For the prediction set, R2p was 0.86, RMSEP was 0.79. Moreover, pixel-wise firmness prediction was implemented on the spectral images, enabling the visualization of apple firmness distribution. This study demonstrates that HSI combined with 1D-CNN provides an effective approach for early quality assessment of B. dorsalis-infested apples, providing theoretical support for the development of relevant detection devices.
The optical path length of a transmission flow cell is a critical parameter that influences the quantitative accuracy of infrared (IR) spectroscopy. This study investigates how path length affects the accuracy of IR spectroscopy for measuring bulk properties using a generalizable machine learning (ML)-based framework. The approach was demonstrated using an onboard fuel sensing application aimed at predicting the derived cetane number (DCN) of jet fuels and fuel blends from mid-IR spectra in the wavelength range of 5.8-7.2 & micro;m. A range of path lengths, from 1 & times; 10-5 to 500 & micro;m, was examined by simulating spectra through scaling of experimentally measured data. The results showed that extremely short path lengths led to poor prediction accuracy due to insufficient absorption and low signal-to-noise ratio (SNR), while excessively long path lengths caused strong absorption saturation and amplified spectral artifacts, degrading model performance. A broad near-optimal range between 10 and 58.7 & micro;m was identified within the simulation framework, where spectra exhibited adequate absorption, moderate saturation, and minimal distortion. These findings demonstrate that allowing moderate absorption saturation can enhance predictive accuracy by improving SNR at wavelengths most correlated with the target property and emphasize that optimal measurement conditions should be determined based on predictive performance rather than solely maximizing SNR while maintaining absorbance within traditional limits.
The growing electronic cigarette market faces significant quality control issues regarding e-liquid compositions. Fast analytical methods are required to verify the proportions of the primary solvents, vegetable glycerin and propylene glycol. This study used Attenuated Total Reflectance Fourier Transform Infrared spectroscopy with partial least squares regression to quantify glycerin and propylene glycol in commercial e-liquids. Gas Chromatography with Flame Ionization Detection served as the reference method. A matrix-matched calibration model, built with real commercial samples, was developed to effectively account for spectral interferences. The optimized PLS model achieved high predictive accuracy (R-2 = 0.9995), strongly correlating with the chromatography results. Crucially, both techniques exposed severe discrepancies between the measured glycerin/propylene glycol ratios and the manufacturers declared labels. These findings validate this spectroscopic approach as a robust, non-destructive, and low-cost tool for the routine quality control and rapid screening of mislabeled formulations. Future work should expand the calibration dataset with a larger set of commercial samples to further enhance the model's universal applicability.
Closed-form solutions of isolated spheres have been used extensively in modeling and understanding spherical biological scattering systems in infrared microscopy. However, since samples are deposited onto infrared transparent slides, they are not isolated, and the slide may affect the measured spectra. While the effect of the slides used in infrared microscopy is well understood for thin film samples, it is not understood to what extent the slide affects the scattering signatures from spherical systems. To get a more accurate representation of the measurements, the sample and the slide should be considered as a combined scattering system. In the present study, we investigate the effect of microscope slides of different materials on the measured absorbance spectra. The primary effect is dampening of the ripples when the slide is index matched to the spherical sample, while the wiggles are not affected by the slide. Since spectra of spherical biological samples most often contain wiggles but are devoid of ripples, the effect of the slide is in many cases not significant. Since forward models in use for spectral correction only consider the wiggles, one can conclude that the forward models are valid regardless of the slide used in the experiment.
To address the issues of non-real-time data acquisition and sample consistency in liquid-phase near-infrared spectroscopy, this study designed and developed an online liquid-phase near-infrared spectroscopy acquisition system based on an STM32 microcontroller. The system innovatively employs a dual-buffer bottle liquid-level closed-loop control strategy, aiming to eliminate pipeline cavitation at the hardware level and enhance sample delivery efficiency. Comparative experiments using distilled water samples demonstrated that the dual-buffer bottle liquid level control system achieved 0% cavitation occurrence, a relative standard deviation (RSD) of spectral repeatability as low as 1.2%, and a 14.5% improvement in sample delivery efficiency, validating the physical stability of the hardware system. Building upon this platform, systematic data quality evaluation was conducted. Spectral data were collected from ammonia nitrogen solutions and beer samples. Competitive Adaptive Re-weighted Sampling algorithm was employed to identify key feature wavelengths for each sample type. Multiple Extreme Learning Machines regression models were established to assess the usability of chemical information within the data. Results showed that the ammonia nitrogen optimal model achieved a coefficient of determination exceeding 0.99 for both calibration and validation sets, with a relative root mean square error below 4.8%. Similarly, the ethanol optimal model achieved a coefficient of determination exceeding 0.99 for both sets, with a relative root mean square error below 1.75%. These results validate the effectiveness of the dual-buffer-bottle liquid level closed-loop control strategy within this system and confirm the usability of the spectral data collected by the system.
Traumatic brain injury (TBI) is a common case type in forensic medicine. Accurately distinguishing injured from non-injured tissues and estimating the time elapsed since injury are crucial aspects of forensic practice. This study aimed to investigate the feasibility of using Attenuated Total Reflection Fourier Transform Infrared (ATR-FTIR) spectroscopy to discriminate between injured and non-injured tissues, as well as to differentiate various post-injury intervals following moderate TBI. A mouse model of moderate TBI was established using the Feeney weight-drop method, and individuals with moderate injury were strictly selected based on the modified Neurological Severity Score (mNSS). Firstly, Principal Component Analysis (PCA) was employed for dimensionality reduction and visualization of the spectral data. Secondly, classification models including Partial Least Squares Discriminant Analysis (PLS-DA) and Support Vector Machine (SVM) were constructed to distinguish injured from non-injured tissues across different time points, both of which demonstrated excellent classification performance. Subsequently, regression models, including Partial Least Squares Regression (PLS-R), Constrained Linear Regression (CLR), Principal Component Regression (PCR), Multiple Linear Regression (MLR), Support Vector Regression (SVR), and Artificial Neural Network (ANN), were developed based on the spectral data to predict the survival time post-TBI. Among these, the SVR model delivered the best prediction performance with R2 of 0.936 and RMSE of 1.485 days. This preliminary study demonstrates that FTIR spectroscopy combined with chemometrics enables rapid and accurate identification of trauma occurrence and estimation of the post-injury interval, thereby providing a scientific basis for case investigation and judicial proceedings.
Gunshot residue (GSR) is a key form of trace evidence in firearm-related investigations It comprises of two main components namely inorganic GSR (IGSR) and organic GSR (OGSR). IGSR is traditionally classified based on particle morphology and elemental composition. However, this approach becomes challenging for residues from non-traditional ammunition, including heavy metal-free primers. Consequently, there is increasing interest in incorporating OGSR into forensic protocols and developing standardized approaches for its interpretation. As indicated in several studies, OGSR detection can be affected by environmental variables; however, a systematic study on its stability under defined temperature and humidity conditions remains limited. In this study, we evaluated the chemical stability of OGSR under controlled laboratory simulations of temperature and relative humidity. The conditions were selected to represent ambient scenarios relevant to typical Indian weather, with temperatures ranging from 10 to 60 degrees C, relative humidity from 45% to 95% RH and exposure time varying between 0 and 8 h. A total of 51 OGSR samples were collected under controlled indoor firing-range conditions using 9 & times; 19 mm ammunition. These samples were subjected to artificial ageing in environmental chambers and exposed for different durations within this time window to study the degradation pathway of OGSR. To interrogate these potential degradation pathways, OGSR stability was monitored using Raman spectroscopy to detect time-dependent vibrational spectral changes. The results demonstrate no measurable changes in OGSR profiles under the tested temperature-humidity-exposure time combinations and, highlighting factors that influence OGSR detectability when evidence collection is delayed within hours after discharge. These findings provide condition-specific data to support interpretation of OGSR in forensic casework, while noting that longer-term persistence and complex outdoor scene effects were not evaluated in the present study.
Lung cancer, as one of the most prevalent malignant tumors globally with high incidence and mortality rates, involves cumbersome and complex and time-consuming pathological diagnostic processes that severely hinder early intervention and treatment opportunities for patients. Developing an efficient and accurate diagnostic method for lung cancer is of great significance. Raman spectroscopy, leveraging its exceptional sensitivity and flexibility, can capture molecular vibration information of cells, demonstrating significant potential in biomedical detection. This study utilized Raman spectroscopy to collect spectral data from cytological smears of 81 patients with radiologically detected pulmonary abnormalities and 14 confirmed lung cancer patients, thereby constructing a lung cancer diagnostic model. Principal Component Analysis (PCA), characteristic peak correlation analysis, and peak-fitting techniques were employed to explore latent spectral information, providing more precise data support for model construction. Furthermore, a parallel neural network model combining Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) was developed (CNN-LSTM model), integrating CNN's feature extraction capability with LSTM's handling of long-sequence dependencies to achieve rapid and accurate diagnosis of lung cancer. Experimental results showed that the proposed diagnostic model achieved an accuracy of 95.26%, exhibiting excellent classification performance and practical applicability. The study demonstrates that the deep integration of Raman spectroscopy and neural networks offers an effective, simple, and rapid new approach for lung cancer diagnosis.
Building on evidence that plasma-treated water solutions (PTWS) exhibit antitumor activity, this study investigated their selective effects on healthy keratinocytes versus head and neck cancer (HNC) cells. PTWS were generated from clinically approved rehydrating solutions supplemented with tyrosine (SIII-Tyr) and treated with oxygen or air plasma at different treatment times. Human keratinocytes (HaCaT) and HNC (FaDu and SAS) cells were exposed to different PTWS formulations for 30min followed by 24-hours incubation in culture medium. Cells were analysed using Fourier Transform Infrared (FTIR) and Raman spectroscopy, with principal components analysis (PCA) to identify biochemical changes related to cytotoxicity. Results were correlated with cell viability (MTT assay) and intracellular reactive oxygen species (ROS) levels (flow cytometry). HaCaT cells showed minimal sensitivity, while FaDu and SAS cells were significantly affected. Notably, SAS cells exhibited over 90% mortality after exposure to PTWS oxy 20’ sample. ROS levels increased in all cell lines following exposure to PTWS, but for HaCaT cells remained below the baseline ROS of untreated HNC. The highest ROS accumulation was observed in SAS cells treated with PTWS oxy 20’, aligning with cytotoxicity data. PCA of FTIR and Raman spectra revealed distinct biochemical signatures in HNC cells, particularly under PTWS oxy 20’ treatment exposure. Even under milder conditions (air 10’), significant spectral deviations between HNC and HaCaT cells suggested a potential window for selective action. These findings support PTWS selectivity against HNC cells. From a vibrational spectroscopy perspective, this study provides a novel, rapid, label-free tool combining FTIR with Raman to assess selective biochemical responses in HNC models. These results support future preclinical and translational applications.
Breast cancer is the most prevalent malignancy among women worldwide, highlighting the urgent need for real-time and accurate intraoperative diagnostic techniques. Fiber-optic micro-Raman spectroscopy enables non-invasive, label-free, and real-time detection of biomolecular features in tissues, offering promising potential in clinical breast cancer applications. However, raw Raman spectra are often affected by strong autofluorescence baselines, which obscure critical spectral features and compromise subsequent modeling accuracy. To address these challenges, a dynamic threshold adaptive gradient weighted (DT-AGW) baseline correction algorithm is proposed. This method adaptively adjusts penalization weights across wavenumbers based on spectral gradient features, eliminating the need for prior information and enhancing generalizability. A dynamic threshold mechanism improves peak recognition tolerance, increasing robustness to low signal-to-noise ratio spectra. Furthermore, the weighting function incorporates both spectral gradient and fitting residuals to improve iterative correction accuracy. Experimental results demonstrate that DT-AGW achieves lower Root Mean Square Error and higher classification accuracy compared to conventional baseline correction methods. The proposed algorithm shows strong potential for integration into portable intraoperative diagnostic devices and offers improved performance for biomedical spectral data analysis.
The rising global cocaine trafficking and consumption, coupled with the proliferation of harmful adulterants, highlights the urgent need for rapid, reliable, and on-site analytical methods. Unlike traditional chromatographymass spectrometry, which requires extensive sample preparation and long analysis times, near-infrared (NIR) spectroscopy offers a non-destructive, portable, and efficient alternative. A comprehensive, portable method is needed to quantify cocaine and its common, hazardous adulterants in seized samples, providing crucial information for public health and police intelligence. A portable NIR spectroscopy method combined with chemometrics for the simultaneous quantification of cocaine, phenacetin, and levamisole in 155 real seized samples was developed and evaluated. PLS1 and PLS2 models were built and validated. PLS1 models showed prediction errors (RMSEP) below 6.40% for all compounds (6.40% for cocaine, 2.93% for levamisole, and 3.38% for phenacetin), with high R2 values. The streamlined PLS2 model, allowing simultaneous quantification, yielded RMSEP values below 6.55% for all compounds (6.55% for cocaine, 3.64% for levamisole, and 3.11% for phenacetin). A reproducibility study confirmed the robustness, showing relative standard deviations (RSD) below 10% in most cases for all three compounds. This work demonstrates portable NIR spectroscopy as a fast, reliable, and nondestructive screening tool for forensic analysis. This method addresses the novelty of a multi-analyte response, as existing literature focuses on individual compounds. Simultaneously quantifying cocaine and critical adulterants, provides richer insights vital for public health, informing users of risks, and enhancing police intelligence in drug trafficking investigations, thus reducing reliance on time-consuming chromatographic analyses.
Whenever the dipole moment derivatives of out-of-plane bending vibrations in planar molecules are described in terms of atomic charges and their changes (either called charge transfer, CT, or charge fluxes, CF), a constraint arises from the symmetry of the normal coordinate, requesting the charge transfer term to vanish, i.e., CT=0. This was first reported in 1989 by Dinur & Hagler and no exception has been found to this date. In this work, we show that the aforementioned symmetry constraints in planar molecules are, in fact, a special case within a more general one in which a given vibration is symmetric with respect to a given molecular plane of symmetry. This general case does not depend on the vibration being out-of-plane or in-plane bendings, nor on the molecule being planar or not. Nonetheless, whereas in the general case only the atoms lying on a symmetry plane will show CT=0, for out-of-plane vibrations of planar molecules all atoms will show CT=0 simultaneously because all atoms belong to that plane, making the overall CT=0 too. The argument is grounded on symmetry elements and their mathematical properties, while numerical examples are also presented and discussed.
Research on non-noble metal SERS platforms has drawn widespread focus in the past decade. Molybdenum disulfide (MoS2) has emerged as a highly potential candidate for non-noble metal SERS platforms, owing to its tunable bandgap and abundant active sites. Nevertheless, pristine MoS2 exhibits weak SERS activity, limiting its practical applications. In this study, tungsten atoms were doped into MoS2 by a one-step hydrothermal approach to significantly boost MoS2's capability for SERS applications. The following factors account for the enhanced SERS activity of tungsten-doped MoS2 (W-MoS2): W doping can enhance the adsorption between MoS2 and analyte molecules, and facilitate charge transfer between them. Additionally, W doping is capable of regulating the band structure of MoS2, thereby enhancing the charge transfer efficiency. Experimental results show that the W-MoS2 SERS substrate has better detection performance compared with the pure MoS2 SERS substrate. For methylene blue (MB), the SERS enhancement factor (EF) of the W-MoS2 substrate was calculated as 1.095 x 106, and its detection limit was achieved at 10(-7) M. Additionally, the W-MoS2 platform demonstrates high homogeneity and stability. This work offers a novel perspective on the rational synthesis of transition-metal dichalcogenide materials exhibiting high SERS performance.
