Search Results (232)

In this study, we perform density functional theory (DFT) simulations to investigate the Raman spectra of the bulk and surface phases of β-Li₃PS₄ (LPS) and Li₂S, as well as their interfaces at varying compositional ratios. This analysis is relevant given the widespread application of these materials in Li–S solid-state batteries, where Li₂S functions not only as a cathode material but also as a protective layer for the lithium anode. Understanding the interfacial structure and how compositional variations influence its chemical and mechanical stability is therefore crucial. Our results demonstrate that the LPS/Li₂S interface remains stable regardless of the compositional ratio. However, when the content of both materials is low, the Raman-active vibrational mode associated with the [PS₄]³⁻ tetrahedral cluster dominates the interface spectrum, effectively obscuring the characteristic peaks of Li₂S and other interfacial features. Only when sufficient amounts of both LPS and Li₂S are present does the coupling between their vibrational modes become sufficiently pronounced to alter the Raman profile and reveal distinct interfacial fingerprints. Full article

Raman spectroscopy has become an indispensable analytical technique in pharmaceutical research, offering non-invasive, rapid, and chemically specific insights into pharmaceutical compounds. In this study, we present a comprehensive benchmark of machine learning models for classifying 32 pharmaceutical compounds based on their Raman spectral signatures. A diverse array of algorithms—including Support Vector Machines (SVMs), Random Forests, k-Nearest Neighbors (k-NN), Gradient Boosting (XGBoost, LightGBM), and 1D Convolutional Neural Networks (CNNs)—were evaluated on a publicly available dataset. The results demonstrate outstanding classification performance across models, with linear SVM achieving the highest accuracy of 99.88%, followed closely by CNN (99.26%). Ensemble methods such as Random Forest and XGBoost also yielded high accuracies above 98.3%. In addition to strong predictive performance, SHAP (SHapley Additive exPlanations) analysis was employed to interpret model decisions. CNN models, in particular, revealed well-localized and chemically meaningful spectral regions critical to classification. This combination of high accuracy and interpretability highlights the promise of explainable AI in pharmaceutical analysis and quality control, offering robust, transparent, and scalable solutions for real-world applications. Full article

Food contaminants, including harmful microbes, pesticide residues, heavy metals and illegal additives, pose significant public health risks. While traditional detection methods are effective, they are often slow and require complex equipment, which limits their application in real-time monitoring and rapid response. Surface-enhanced Raman scattering (SERS) technology has gained widespread use in related research due to its hypersensitivity, non-destructibility and molecular fingerprinting capabilities. In recent years, Au-Ag bimetallic nanoparticles (Au-Ag BNPs) have emerged as novel SERS substrates, accelerating advancements in SERS detection technology. Au-Ag BNPs can be classified into Au-Ag alloys, Au-Ag core–shells and Au-Ag aggregates, among which the Au-Ag core–shell structure is more widely applied. This review discusses the types, synthesis methods and practical applications of Au-Ag BNPs in food contaminants. The study aims to provide valuable insights into the development of new Au-Ag BNPs and their effective use in detecting common food contaminants. Additionally, this paper explores the challenges and future prospects of SERS technology based on Au-Ag BNPs for pollutant detection, including the development of functional integrated substrates, advancements in intelligent algorithms and the creation of portable on-site detection platforms. These innovations are designed to streamline the detection process and offer guidance in selecting optimal sensing methods for the on-site detection of specific pollutants. Full article

Surface-enhanced Raman scattering (SERS) spectroscopy is a straightforward analytical technique capable of providing detailed information about metabolites in biological samples. The objective of this study was to perform a SERS analysis of ergothioneine (EGT), an amino acid synthesized by microbes and fungi, across a range of pH values (acidic to alkaline) and concentrations (2 × 10⁻⁵ M to 2 × 10⁻⁷ M), to understand the dynamic interactions between EGT and silver (Ag) nanoparticles. Furthermore, SERS was applied in situ on mushroom fruiting bodies to detect the presence of EGT. The SERS spectra revealed that the interaction of EGT with Ag nanoparticles underwent significant alterations at varying pH levels, primarily due to isomerization. These changes were associated with modifications in the aromaticity and ionization of the imidazole ring, driven by both metal adsorption and alkaline conditions. Our results indicated the formation of distinct tautomeric forms of the imidazole group, namely the thione and thiol forms, in aqueous solution and on the Ag surface, respectively. Furthermore, the EGT spectra at different concentrations suggested that ionization occurred at lower concentrations. Notably, the SERS spectra of the mushroom fruiting bodies were dominated by prominent bands attributable to EGT, as corroborated by the comparison with the EGT fungal extract and EGT standard. These findings underscore the utility of SERS spectroscopy as a rapid and effective tool for obtaining comprehensive molecular fingerprints, even directly from complex biological matrices such as mushroom fruiting bodies. Full article

Sub-wavelength metallic nanostructures allow the squeezing of light within nanoscale regions, called plasmonic hotspots. Squeezed near-field light has been demonstrated to detect, modulate, and generate light in more effective ways. The enhanced electric field in the plasmonic hotspots are also utilized for identifying molecular fingerprints in a more sensitive manner, i.e., surface-enhanced Raman spectroscopy (SERS). SERS is a versatile tool used to characterize chemicals and biomolecules with the advantages of label-free detection, specificity, and high sensitivity compared to fluorescence and colorimetric sensing methods. With its practical and diverse applications such as biomedical sensing, the evaluation of SERS on diverse nano-structure platforms and materials is highly in demand. Nanogap structures are promising SERS platforms which can be fabricated over a large area with uniform nanoscale gap size. Here, we demonstrate the fabrication of large-area metal–insulator–metal nanogap structures with different metals (i.e., Au and Ag) and analyze material dependence on SERS. While both nanometer-sized gap structures exhibit a large enhancement factor for Raman spectroscopy, Ag-based structures exhibit 58- and 15-times-larger enhancement factors for bottom and top plasmonic hotspots, respectively. The enhanced detection on a silver nanogap platform is attributed to enhanced electric field in the gap, as confirmed by simulation. Our findings provide not only a way to better understand SERS in different metallic nano platforms but also insights for designing highly sensitive nanoscale chemical and biomedical sensors. Full article

To address issues of food authenticity, such as fraud and origin tracing, it is essential to employ methods in food fingerprinting that are efficient, economical, and easy to use. This review highlights the capabilities of vibrational spectroscopy techniques, including mid-infrared (MIR), near-infrared (NIR), and Raman spectroscopy, as non-invasive tools for food authentication. These methods offer rapid, cost-effective, and environmentally friendly analysis across diverse food matrices. This review further discusses recent advances such as hyperspectral imaging, portable devices, and data fusion strategies that integrate chemometrics and artificial intelligence. Despite their promise, challenges remain, including limited sensitivity for certain compounds, spectral overlaps, fluorescence interference in Raman spectroscopy, and the need for standardized validation protocols. Looking forward, trends such as the miniaturization of devices, real-time monitoring, and AI-enhanced spectral interpretation are expected to significantly advance the field of food authentication. Full article

Due to its unique molecular fingerprinting capability and multiplex detection advantages, surface-enhanced Raman scattering (SERS) has shown great application potential in the field of biological analysis. However, the weak signal intensity and large background interference significantly limited the application of SERS in biosensing and bioimaging. Loading a large amount of Raman molecules with signal in the silent region on the hotspots of the electromagnetic field of the SERS substrate can effectively avoid severe background noise signals and significantly improve the signal intensity, making the sensitivity and specificity of SERS detection remarkably improved. To achieve this goal, a new SERS signal-amplification strategy is herein reported for background-free detection of Cu²⁺ by using Raman-silent probes loaded on cabbage-like gold microparticles (AuMPs) with high enhancement capabilities and single-particle detection feasibility. In this work, carboxyl-modified AuMPs were used to enable Cu²⁺ adsorption via electrostatic interactions, followed by ferricyanide coordination with Cu²⁺ to introduce cyano groups, therefore generating a stable SERS signal with nearly zero background signals owing to the Raman-silent fingerprint of cyano at 2137 cm⁻¹. Based on the signal intensity of cyano groups correlated with Cu²⁺ concentration resulting from the specific coordination between Cu²⁺ and cyanide, a novel SERS method for Cu²⁺ detection with high sensitivity and selectivity is proposed. It is noted that benefiting from per ferricyanide possessing six cyano groups, the established method with the advantage of signal amplification can significantly enhance the sensing sensitivity beyond conventional approaches. Experimental results demonstrated this SERS sensor possesses significant merits towards the determination of Cu²⁺ in terms of high selectivity, broad linear range from 1 nM to 1 mM, and low limit of detection (0.1 nM) superior to other reported colorimetric, fluorescence, and electrochemical methods. Moreover, algorithm data processing for optimization of SERS original data was further used to improve the SERS signal reliability. As the proof-of-concept demonstrations, this work paves the way for improving SERS sensing capability through the silent-range fingerprint and signal amplification strategy, and reveals SERS as an effective tool for trace detection in complex biological and environmental matrices. Full article

This investigation presents an overview of various optical biosensors utilized for the detection of cancer cells. It covers a comprehensive range of technologies, including surface plasmon resonance $\left(SPR\right)$ sensors, which exploit changes in refractive index $\left(RI\right)$ at the sensor surface to detect biomolecular interactions. Localized surface plasmon resonance $\left(LSPR\right)$ sensors offer high sensitivity and versatility in detecting cancer biomarkers. Colorimetric sensors, based on color changes induced via specific biochemical reactions, provide a cost-effective and simple approach to cancer detection. Sensors based on fluorescence work using the light emitted from fluorescent molecules detect cancer-specific targets with specificity and high sensitivity. Photonics and waveguide sensors utilize optical waveguides to detect changes in light propagation, offering real-time and label-free detection of cancer biomarkers. Raman spectroscopy-based sensors utilize surface-enhanced Raman scattering $\left(SERS\right)$to provide molecular fingerprint information for cancer diagnosis. Lastly, fiber optic sensors offer flexibility and miniaturization, making them suitable for in vivo and point-of-care applications in cancer detection. This study provides insights into the principles, applications, and advancements of these optical biosensors in cancer diagnostics, highlighting their potential in improving early detection and patient outcomes. Full article

Because of the involvement of π-electron cyclic constituents in their side chains, the so-called aromatic residues give rise to a number of strong, narrow, and well-resolved lines spread over the middle wavenumber (1800–600 cm⁻¹) region of the Raman spectra of peptides and proteins. The number of characteristic aromatic markers increases with the structural complexity (Phe → Tyr → Trp), herein referred to as (F_i = 1, …, 6) in Phe, (Y_i = 1, …, 7) in Tyr, and (W_i = 1, …, 8) in Trp. Herein, we undertake an overview of these markers through the analysis of a representative data base gathered from the most structurally simple tripeptides, Gly-Xxx-Gly (where Xxx = Phe, Tyr, Trp). In this framework, off-resonance Raman spectra obtained from the aqueous samples of these tripeptides were jointly used with the structural and vibrational data collected from the density functional theory (DFT) calculations using the M062X hybrid functional and 6-311++G(d,p) atomic basis set. The conformation dependence of aromatic Raman markers was explored upon a representative set of 75 conformers, having five different backbone secondary structures (i.e., β-strand, polyproline-II, helix, classic, and inverse γ-turn), and plausible side chain rotamers. The hydration effects were considered upon using both implicit (polarizable solvent continuum) and explicit (minimal number of 5–7 water molecules) models. Raman spectra were calculated through a multiconformational approach based on the thermal (Boltzmann) average of the spectra arising from all calculated conformers. A subsequent discussion highlights the conformational landscape of conformers and the wavenumber dispersion of aromatic Raman markers. In particular, a new interpretation was proposed for the characteristic Raman doublets arising from Tyr (~850–830 cm⁻¹) and Trp (~1360–1340 cm⁻¹), definitely excluding the previously suggested Fermi-resonance-based assignment of these markers through the consideration of the interactions between the aromatic side chain and its adjacent peptide bonds. Full article

Due to rising concerns regarding the adulteration and mislabeling of honey, new directives at the European level encourage researchers to develop reliable honey authentication models based on rapid and cost-effective analytical techniques, such as vibrational spectroscopies. The present study discusses the identification of the main vibrational bands of the FT-Raman and ATR-IR spectra of the most consumed honey varieties in Transylvania: acacia, honeydew, and rapeseed, exposing the ways the spectral fingerprint differs based on the honey’s varietal-dependent composition. Additionally, a pilot study on honey authentication describes a new methodology of processing the combined vibrational data with the most efficient machine learning algorithms. By employing the proposed methodology, the developed model was capable of distinguishing honey produced in a narrow geographical region (Transylvania) with an accuracy of 85.2% and 93.8% on training and testing datasets when the Trilayered Neural Network algorithm was applied to the combined IR and Raman data. Moreover, acacia honey was differentiated against fifteen other sources with a 87% accuracy on training and testing datasets. The proposed methodology proved efficiency and can be further employed for label control and food safety enhancement. Full article

Surface-enhanced Raman spectroscopy (SERS) is widely employed due to its high sensitivity and distinctive fingerprinting capabilities. Colloidal nanoaggregates are commonly used as SERS substrates because of their mobility and the abundance of “hotspots”. Although the reagent-free “freeze-thaw-ultrasonication” method for preparing Ag nanoaggregates (AgNAs) does not introduce additional background interference and maintains the original interfacial properties of AgNAs, their unstable physical nanostructure limits SERS detection to just 7 days. Herein, we demonstrate mesoporous silica-encapsulated colloidal Ag nanoaggregates (AgNAs@m-SiO₂) by combining a freeze-thaw-ultrasonication method and a cetyltrimethylammonium bromide (CTAB)-assisted silanization reaction, achieving long-term SERS stability of more than two months. The prepared AgNAs@m-SiO₂ serve a dual capability: (1) preserving electromagnetic “hotspots” for ultra-sensitive detection (e.g., malachite green detection limit: 3.60 × 10⁻⁸ M), and (2) maintaining structural stability under harsh conditions. The AgNAs@m-SiO₂ substrate exhibited superior structural stability after 50 min of ultrasonic treatment, with an initial SERS signal retention of 91.8%, which is twice that of the bare AgNAs (retention of 45%). The long-term performance further highlighted its superiority: after 70 days of storage, the composite maintained 84.3% of its original signal strength, outperforming the uncoated controls by over ten times (which retained only 8%). Crucially, the substrate’s robust design enables the direct detection of contaminants in real environmental matrices (river and seawater) for qualitative analyses and water quality assessments, thus validating its suitability for environmental sensing applications in the field. Full article

Accurate identification and characterization of allergenic proteins at the molecular level are essential for pinpointing the specific protein structures responsible for allergic reactions, thus advancing the development of precise diagnostic tests. Significant efforts have been focused on novel experimental techniques aimed at deepening the understanding of the underlying molecular mechanisms of these reactions. In this work, we show, for the first time to our knowledge, the unique Raman fingerprint of three Parietaria judaica (Par j) allergenic proteins. These proteins are typically present in pollen and are known to trigger severe respiratory diseases. In our research, we further exploited the surface-enhanced Raman scattering (SERS) effect from an Ag dendrite substrate. This approach provided better discrimination and a comprehensive analysis of the proteins Par j 1, 2, and 4 in hydration conditions, enabling rapid differentiation between them through a spectroscopic study. Full article

Raman spectroscopy enables fast, label-free, qualitative, and quantitative observation of the physical and chemical properties of various substances. Here, we present a 785 nm custom-built Raman spectroscopy instrument designed for sensing applications in the 400–1700 cm⁻¹ spectral range. We demonstrate the performance of the instrument by fingerprinting 14 pesticide reference samples with over twenty technical repeats per sample. We present molecular Raman fingerprints of the pesticides comprehensively and distinguish similarities and differences among them using multivariate analysis and machine learning techniques. The same pesticides were additionally investigated using a commercial 532 nm Raman instrument to see the potential variations in peak shifts and intensities. We developed a unique Raman fingerprint library for 14 reference pesticides, which is comprehensively documented in this study for the first time. The comparison shows the importance of selecting an appropriate excitation wavelength based on the target analyte. While 532 nm may be advantageous for certain compounds due to resonance enhancement, 785 nm is generally more effective for reducing fluorescence and achieving clearer Raman spectra. By employing machine learning techniques like the Random Forest Classifier, the study automates the classification of 14 different pesticides, streamlining data interpretation for non-experts. Applying such combined techniques to a wider range of agricultural chemicals, clinical biomarkers, or pollutants could provide an impetus to develop monitoring technologies in food safety, diagnostics, and cross-industry quality control applications. Full article

Hepatitis, most importantly hepatitis B and hepatitis C, is a significant global health concern, requiring an accurate and early diagnosis to prevent severe liver damage and ensure effective treatment. The currently employed diagnostic methods, while effective, are often limited in their sensitivity, specificity, and rapidity, and the quest for improved diagnostic tools is ongoing. This review explores the innovative application of Raman spectroscopy combined with a chemometric analysis as a powerful diagnostic tool for hepatitis. Raman spectroscopy offers a non-invasive, rapid, and detailed molecular fingerprint of biological samples, while chemometric techniques enhance the interpretation of complex spectral data, enabling precise differentiation between healthy and diseased states and moreover the severity/stage of disease. This review aims to provide a comprehensive overview of the current research, foster greater understanding, and stimulate further innovations in this burgeoning field. The Raman spectrum of blood plasma or serum provides fingerprints of biochemical changes in the blood profile and the occurrence of disease simultaneously, while Raman analyses of polymerase chain reaction/hybridization chain reaction (PCR/HCR)-amplified nucleic acids and extracted DNA/RNA as the test samples provide more accurate differentiation between healthy and diseased states. Chemometric tools enhance the diagnostic efficiency and allow for quantification of the viral loads, indicating the stage of disease. The incorporation of different methodologies like surface enhancement and centrifugal filtration using membranes provides the ability to target biochemical changes directly linked with the disease. Immunoassays and biosensors based on Raman spectroscopy offer accurate quantitative detection of viral antigens or the immune response in the body (antibodies). Microfluidic devices enhance the speed of detection through the continuous testing of flowing samples. Raman diagnostic studies with massive sample sizes of up to 1000 and multiple reports of achieving a greater than 90% differentiation accuracy, sensitivity, and specificity using advanced multivariate data analysis tools indicate that Raman spectroscopy is a promising tool for hepatitis detection. Its reproducibility and the identification of unique reference spectral features for each hepatic disease are still challenges in the translation of Raman spectroscopy as a clinical tool, however. The development of databases for automated comparison and the incorporation of automated chemometric processors into Raman diagnostic tools could pave the way for their clinical translation in the near future. Full article

Bacterial infection is one of the common infectious diseases in clinical practice, and the research on efficient detection of bacteria has attracted much attention in recent years. Currently, the traditional detection methods of bacteria are mainly based on cell culturing, microscopic examination, and molecular biology techniques, all of which have the disadvantages of complex operation and time-consuming. Surface-enhanced Raman spectroscopy (SERS) technology has shown prominent advantages in bacterial detection and identification because of the merit of high-sensitivity, fast detection and unique molecular fingerprint spectrum. This paper mainly investigates and discusses the application of SERS in bacterial detection, and systematically reviews the progress of SERS applications, including nano-enhanced dielectric materials of SERS, signal amplification of SERS labeled molecules, and the integration of SERS with microfluidic technology. Finally, the paper analyzes the challenges associated with the application of SERS in bacterial detection and offers insights into future development trends. Full article