This study presents a novel immunochromatographic assay (ICA) leveraging surface-enhanced Raman scattering (SERS) for ultrasensitive pesticide detection. The proposed method utilizes core-shell structured gold nanoparticles (Au@Au NPs) functionalized with antibodies as the SERS substrate, coupled with Raman reporter molecules (MPBN) to generate a robust spectroscopic signal. By exploiting the immunorecognition between target antigens and immobilized antibodies, Raman-tagged Au@Au NPs are captured on the ICA test line, enabling quantitative analysis of lambda-cyhalothrin, and the minimum detection limit in actual samples (green peppers) reached 1.33 ppb. Comparative studies demonstrated that the SERS-ICA method achieved a 103-fold increase in sensitivity compared to the conventional LFA. Validation in complex matrices (grapes, eggplants, and green peppers) yielded recoveries of 92.85-118.25% and relative standard deviations (RSD) of 4.02-16.17%, confirming the method's exceptional specificity, accuracy, and applicability in real-world scenarios. This platform holds significant promise for rapid, on-site monitoring of trace contaminants in food safety and environmental surveillance.

Raman spectroscopy enables accurate substance identification by leveraging its inherent fingerprint characteristics, yet edge-side Raman spectral qualitative unmixing analysis faces two critical challenges: traditional methods suffer from low inference efficiency and high resource consumption, while deep learning-based methods rely heavily on large-scale labeled data. So this paper proposes a Sparse Bayesian Learning (SBL) unfolded network named Edge-RamanSBL-Net (ERSN) to address the pain points. ERSN adopts a cascaded unfolded architecture with 5 SBL layers, converting the iterative optimization of SBL into the forward network to learn the mapping relationship between mxiture spectra and multiple pure components and support high-dimensional dictionary input. Through a network parameter sharing mechanism, the model weight is compressed to 6.13MB. During the training phase, a hierarchical decrement strategy is employed to decouple training sufficiency from testing flexibility, allowing inference complexity to be adjusted on demand. Experiments validated with data collected by portable instruments show that on 16 types of mixture samples (10 spectra per type), both the positive identification rate and recall rate reach 100%, with an average execution time of 40ms per spectrum (75-fold speedup). Moreover, only one 5-component mixture spectrum is required to complete One-Shot training. ERSN is an effective solution for fast and accurate Raman qualitative unmixing on edge devices.

The rapid and accurate detection of chlorpyrifos (CPF), a widely used pesticide in agricultural products, is crucial for food safety assurance. While traditional methods like HPLC and GC-MS are accurate, they remain costly, slow, and lack portability. Herein, an ultrasensitive magnetic biosensing platform was developed for the ratiometric detection of CPF in tea by integrating surface-enhanced Raman spectroscopy (SERS) with an aptamer-based recognition strategy. The platform employed a competitive displacement mechanism, where specific binding between aptamer and CPF triggered the release of signal probes upon magnetic separation. This process altered the Raman intensity ratio of two reporter molecules (4-MPY and 4-MBN). The ratiometric sensing approach, combined with magnetic separation, improved operational convenience and reduced matrix interference from complex samples. Furthermore, the platform exhibited a wide linear detection range (10-9 M to 10-4 M) with a limit of detection (LOD) of 1.4 × 10-6 mg/kg. It demonstrated high sensitivity, stability, reproducibility and specificity across six tea varieties, offering an effective solution for detecting pesticide residues in complex food matrices.

Raman spectroscopy, as a nondestructive testing technique, is often limited by noise interference and feature distortion in portable instruments, which severely affect the accuracy of substance detection. To address this, this paper proposes a fast and effective portable-to-benchtop Raman spectral translation method (ETS) for portable devices. First, ETS employs empirical mode decomposition (EMD) to filter out instrument-specific noise through multiscale decomposition. Then, a simplified and lightweight transformer network is used to realize feature mapping between portable and benchtop Raman instruments. Finally, sparse Bayesian learning (SBL) further strengthens the sparse representation of the intrinsic peaks of substances. In the experiment, ETS demonstrated an excellent instrument transfer performance. The cosine similarity of the transferred spectra exceeds 99%. It also demonstrated a great instrument generalization performance. This allows the transferred data to achieve an accuracy of 100% in pure-substance classification in the database. In addition, ETS has an execution time of approximately 0.254 s per spectrum on an Arm CPU with a model weight of only 9.43 MB. In summary, ETS effectively solves the cross-instrument consistency problem of portable Raman instruments, providing key technical support for on-site, rapid substance detection.

This paper introduces an end-to-end convolutional neural network-Morphology-Gaussian Guided Dual-Branch Convolutional Neural Network (MGD-CNN)-for Raman spectral preprocessing, designed to integrate baseline estimation and signal denoising through a collaborative training mechanism. This approach enhances signal quality while preserving key spectral features. The method employs a dual-module co-training scheme that unifies baseline estimation and denoising into a single convolutional network, utilizing a customized deep convolutional architecture to automatically learn spectral characteristics, enabling fully automated signal processing. In the comparative evaluation of preprocessing performance, the proposed model achieves a spectral signal-to-noise ratio (SSNR) of 562.93, a coefficient of determination (R 2 ) of 0.9572, and a root mean square error (RMSE) of 0.0242, significantly outperforming conventional methods and setting a new benchmark for these core metrics. Furthermore, in downstream classification tasks, the preprocessed spectra improve the classification accuracy to 99.15 %, underscoring the method's exceptional ability to preserve discriminative spectral information. This method provides a high-precision, efficient, and adaptive preprocessing solution for Raman spectral analysis.

A Novel Surface-Enhanced Raman Scattering-Based Immunochromatographic Assay (SERS-ICA) for ultrasensitive detection of lambda-cyhalothrin in food samples development, validation, and comparative analysis with conventional lateral flow assay.

With the global pursuit of clean energy and carbon neutrality goals, water electrolysis technology, as a key method for producing green hydrogen, has gained significant attention due to its high efficiency and low carbon characteristics. However, the oxygen evolution reaction (OER), as a critical step in water electrolysis, remains a major challenge for efficiency improvement due to its slow kinetics and high overpotentials. To address this, we systematically investigated the stability, electronic structure, and OER performance of transition metal-doped ReSe2 monolayers (TM- ReSe2, TM = Hf, Ta, W, Os, Ir, Pt) using first-principles calculations. Our results show that the interlayer bonding energy of ReSe2 is 18.9 meV/& Aring;(2), indicating its suitability for exfoliation into stable monolayers. Among the doped systems, Hf-ReSe2 exhibits exceptional thermodynamic and electrochemical stability, with a remarkably low OER free energy barrier of 0.805 eV in acidic media. This is attributed to its optimized electronic structure, moderate adsorption energies for intermediates, and balanced d-band center position. Furthermore, strain effects and charge transfer analyses reveal that Hf doping induces tensile strain (similar to 2.1 %) and significant electron transfer (1.6 e(-)), synergistically enhancing OER activity. These findings provide a theoretical foundation for designing high-performance, Pt-free OER catalysts for water splitting applications.

Handheld Raman spectroscopy offers a rapid, nondestructive method for on-site analysis. However, Raman spectra collected from different instruments often exhibit significant nonlinear discrepancies due to variations in optical configurations, detector responses, and assembly tolerances. These discrepancies hinder data comparability and limit the widespread integration of handheld Raman systems. In this work, we present a standard-material-based model transfer approach to achieve accurate spectral alignment between handheld and benchtop microscopic Raman instruments. The method integrates wavenumber calibration, multiscale convolution, and an attention-guided segmented transfer strategy to establish a high-fidelity mapping from handheld spectra to microscopic Raman reference profiles. This approach effectively corrects nonlinear wavenumber shifts while preserving key spectral features, including peak positions, relative intensities, and overall spectral shape. Experimental results demonstrate that the proposed framework significantly reduces peak position root-mean-square error (RMSE) across diverse analytes and enhances standard spectral similarity metrics, such as Spectral Angle Mapper (SAM), Dynamic Time Warping (DTW), and Maximum Mean Discrepancy (MMD). Additionally, the transferred spectra exhibit improved performance in downstream classification tasks, confirming enhanced cross-instrument reliability. This work provides a robust solution for achieving spectral consistency across Raman platforms, supporting interoperable analysis across different measurement environments and laying the foundation for multi-instrument spectral harmonization.

In this study, we developed an aptamer-mediated surface-enhanced Raman scattering (SERS)-coupled immunochromatographic assay (ICA) for the quantitative detection of pyrethroids. For this approach, core-shell gold nanoparticles (Au@Au NPs) were used as SERS probes by conjugating with aptamers, and fluorescent Raman reporter molecules (MPBN) were incorporated to yield stable, reproducible spectral signals. Leveraging the aptamers' specific recognition of the target analyte, Raman-labeled Au@Au NPs could be effectively captured by the test line of the immunochromatographic strip, thereby enabling the quantitative analysis of fenvalerate-with a limit of detection (LOD) as low as 0.4 ppb. Comparative experiments revealed that the detection sensitivity of this SERS-ICA method was 476 times higher than that of conventional lateral flow assays (LFA), showcasing a remarkable sensitivity advantage. To validate its practicality, spiked recovery experiments were conducted on three real samples, namely welsh onions, cowpeas, and kidney beans. The results showed that the spiked recoveries ranged from 78.3% to 112.5%, with relative standard deviations (RSD) between 1.7% and 20.1%. This fully confirms that the method possesses favorable specificity and accuracy, making it suitable for real-sample detection. This detection platform offers a new technical route for the rapid on-site monitoring of trace contaminants in food safety and environmental surveillance, holding broad application prospects.

Raman spectroscopy is a powerful nondestructive analytical technique, yet its signals are frequently obscured by fluorescence background and noise, posing significant challenges for subsequent analysis-particularly in training deep learning models. The performance of deep learning models heavily relies on high-quality training labels. However, conventional algorithm-based label generation methods often fail to reflect the physical essence of Raman spectra, thereby limiting model performance. To address this limitation, we present RamanNet Labeler, a graphical user interface tool developed using PyQt5. The core innovation of this tool lies in its physics-guided Voigt profile modeling for spectral unmixing, enabling the generation of high-fidelity "ideal spectra" as training labels for deep learning. Integrated with adaptive preprocessing, interactive Voigt peak fitting, and a versatile synthetic data generator, RamanNet Labeler effectively extracts physically authentic components from complex raw spectra. Experimental results demonstrate its capability to batch-generate standardized datasets for training denoising, peak extraction, and quantitative analysis models. This tool provides a critical solution for advancing reliable deep learning applications in Raman spectral analysis.

Raman spectroscopy is a critical analytical technique across numerous engineering disciplines; however, the reliability of its measurements is often compromised by artifacts stemming from complex instrumental and sample-specific variations. Automating quality assessment poses a significant challenge: conventional physics-based thresholds, though interpretable, lack the flexibility to accommodate diverse anomalies, while data-driven deep learning approaches typically overlook valuable domain knowledge. To address this gap, we propose PAQC—a hybrid-intelligence method that synergistically integrates explicit physical priors with implicit representation learning. Our approach processes unlabeled spectral data through dual parallel streams: an explicit knowledge stream that computes physically-informed quality metrics to produce high-confidence pseudo-labels, and an implicit knowledge stream that uses a peak-attention autoencoder, guided by known peak locations, to extract discriminative deep features. These two streams are cohesively fused through a progressive contrastive learning network, yielding a highly separable feature space tailored for anomaly detection. For practical deployment, a Mahalanobis distance-based classifier enables real-time quality diagnosis of individual spectra. Evaluated on a real-world dataset using a rigorous 5-fold cross-validation protocol, PAQC achieves state-of-the-art performance with an F1-score of 98.98% ± 0.67%, while critically maintaining a perfect 100.00% recall, underscoring its effectiveness as a robust and scalable solution for automated quality control in knowledge-sensitive engineering applications.

The pervasive occurrence of short-chain per- and polyfluoroalkyl substances (PFAS), such as potassium perfluorobutane sulfonate (PFBS-K), in aquatic environments poses severe health risks. However, tracing these contaminants at low levels remains difficult because targeted and reliable sensing platforms are lacking in complex mixtures. While surface-enhanced Raman scattering (SERS) allows rapid and sensitive detection, its practicality is hindered by poor analyte affinity and irreproducible signal generation. Here, we report a general design strategy that integrates molecularly targeted enrichment with plasmonic enhancement by creating a biomimetic SERS substrate. We engineered a fluorine-functionalized metal-organic framework (UiO-66-F4) that mimics PFAS's fluorinated structure, endowing it with exceptional affinity and capacity for PFBS-K, as verified by UPLC-MS/MS. After in-situ incorporation of Au nanoparticles, the resulting UiO-66-F4@Au substrate achieves unprecedented SERS performance for PFAS detection, featuring high reproducibility (RSD = 5.29 %) and remarkable sensitivity, with a low detection limit of 0.926 x 10-7 M for PFBS-K in real lake water. This work presents a practical sensor for environmental monitoring and establishes a flexible, target-oriented design paradigm for developing advanced optical platforms to precisely detect trace pollutants in complex environments.

The carbon dioxide electrocatalytic reduction reaction (CO2RR) can be used to convert CO2 into single carbon products such as methane, which is an effective means of mitigating CO2 levels in the atmosphere. CO2RR was simulated using DFT calculations on model catalysts doped with Cu at different cuts of Ni. The catalytic performance of different surfaces of Ni metal doped with Cu atoms, including formation energy, density of states, differential charge density, and energy of adsorption, were investigated comprehensively to evaluate the performance of these catalysts. The results indicate that all the Cu atoms doped in the first layer of the Ni surface enhance the stability of the catalyst and facilitate the conversion of CO2 to formic acid (HCOOH). During the CO2 adsorption process, the lower the formation energy of the catalyst, the lower the adsorption energy of CO2, and the greater the change in bond angle during CO2 adsorption, the more intense the electron transfer, and the better the catalytic effect, which promotes its reduction reaction. Additionally, a smaller change in bond angle indicates that the CO2 molecule is more uniformly adsorbed on the catalyst surface, which is beneficial for the formation of stable intermediates, thereby improving catalytic efficiency.

The accurate detection of pesticide residues in complex food matrices is significantly hindered by interference from sample components. To address this challenge, we developed a novel surface-enhanced Raman spectroscopy-based immunochromatographic assay (SERS-ICA) for the ultrasensitive and on-site detection of carbofuran. The method employs antibody-conjugated Au@Ag core-shell nanoparticles (Au@Ag NPs) as SERS probes, introducing the Raman reporter molecule 4-mercaptobenzoic acid (4MBA) with stable and easily identifiable characteristic peaks. A competitive immunoassay format was established, leveraging antigen-antibody specificity for selective carbofuran recognition. To maximize detection reliability, we systematically evaluated signal-averaging strategies for SERS-ICA by comparing Raman signals collected at 1, 2, 4, 8, and 16 test-line positions. We identified an eight-point averaging scheme that balances precision (R2 = 0.9934) and practicality (average recovery = 99.39%). The assay demonstrates remarkable sensitivity with a Raman detection limit of 1 pg/kg (10-6 mg/kg) and a visual cutoff of 10 μg/kg (10-5 mg/kg). Validation with three agriculturally relevant vegetables (celery, cowpea, and cucumber) showed excellent performance, with linear correlations (R2 > 0.99) and near-quantitative recoveries (99.09-99.39%). Blind spike-recovery tests (95.47-101.65%) further confirmed the robustness of the method for real-world applications. This work establishes SERS-ICA as a next-generation platform for field-deployable, matrix-resistant pesticide monitoring with unmatched sensitivity.

Organotin compounds (OTCs) are toxic pollutants threatening ecosystems and human health, among which dioctyltin (DOCT), widely used in skin-contact textiles, can induce immune dysfunction and metabolic disorders. Although DOCT levels in textiles are strictly regulated by international standards, traditional GC-MS suffers from cumbersome derivatization, unsatisfactory repeatability, and lengthy analysis, highlighting the urgent demand for a rapid and sensitive detection approach. Herein, we developed a fast SERS-based strategy for DOCT determination using size-optimized Au@Ag core–shell nanoparticles as the substrate, which offers simple pretreatment, high efficiency, good uniformity, and excellent reproducibility. The SERS spectra and functional group vibration modes of DOCT were elucidated by density functional theory (DFT) calculations combined with experimental validation, and the peak at 301 cm−1 was identified as the characteristic peak for quantitative analysis. After extractant optimization, the method achieved a low LOD of 0.1 μg/L in real textile samples, with recoveries ranging from 86% to 108% and good linearity from 0.1 to 1000 μg/L (R2 = 0.9804). This approach provides a reliable, high-sensitivity alternative for rapid monitoring of DOCT residues in textiles.

Tin disulfide (SnS2) is a promising two-dimensional layered semiconductor with a suitable bandgap, a negative conduction band, and abundant, eco-friendly constituents, showing great potential for photocatalytic energy conversion and environmental remediation. However, pristine SnS2 suffers from rapid charge recombination, low conductivity, sluggish surface kinetics, and poor stability, limiting its practical efficiency. This review systematically summarizes recent advances in controlled synthesis, modification strategies, and applications of SnS2-based photocatalysts. We first introduce the crystal and electronic structures of SnS2. Then, we outline major preparation techniques, including hydrothermal/solvothermal, chemical vapor deposition (CVD), microwave-assisted, spray coating, and vacuum evaporation, with emphasis on their regulation mechanisms. Subsequently, we highlight key modification approaches—element doping, heterojunction construction, defect engineering, and surface plasmon resonance (SPR) effect—that enhance light absorption, charge separation, and surface reactivity. We also present advanced characterization tools for elucidating charge dynamics, interfacial band alignment, and reaction pathways. Furthermore, we review the application progress in pollutant degradation and energy conversion. Finally, we discuss remaining challenges, including instability, noble-metal dependence, and poor adaptability in complex systems, and offer perspectives on multi-strategy synergy, combined in-situ/theoretical approaches, and integrated multifunctional systems for future development.

Pesticide residues can persist in food and environmental matrices after pesticide application. Their accumulation and migration may pose potential risks to food safety, environmental quality, and public health. Rapid, sensitive, and field-deployable analytical methods are therefore urgently needed. Magnetic solid-phase extraction coupled with surface-enhanced Raman scattering (MSPE–SERS) combines magnetic enrichment, magnetic separation, and molecular fingerprint detection. It provides a promising strategy for trace pesticide analysis in complex matrices. In this review, recent advances in MSPE–SERS for pesticide residue detection are summarized. Magnetic sorbents, SERS-active substrates, and integrated enrichment–detection platforms are discussed. Applications in food and environmental samples are also reviewed. Key factors affecting analytical performance are analyzed, including sorbent selectivity, hot-spot construction, matrix interference, substrate reproducibility, and quantitative robustness. Finally, emerging directions and remaining challenges are highlighted, including Raman encoding, portable Raman detection, machine-learning-assisted spectral analysis, reusable magnetic substrates, standardization, regulatory validation, green analytical chemistry, and field-ready detection systems.

The electrosynthesis of cyclohexanone oxime from cyclohexanone and nitrogenous feedstock driven by renewable electricity presents a sustainable alternative to energy-intensive and hazardous industrial processes. However, achieving high activity and selectivity is challenged by the over-reduction of key intermediates and the lack of effective sites for C─N coupling. Herein, we report a Fe1Bi single-atom alloy (Fe1Bi SAA) featuring Fe-Bi atomic interfaces that collaborate for the one-pot electrosynthesis of cyclohexanone oxime. The Fe1Bi SAA achieves a remarkable Faradaic efficiency of 70.9% and a yield rate of 0.94 mmol cm-2 h-1 for cyclohexanone oxime. Combined in situ electrochemical spectroscopic measurements and density functional theory calculations reveal an atomic-scale synergistic mechanism: dispersed Fe sites adsorb and activate cyclohexanone, while adjacent Bi sites selectively reduce nitrite to the key hydroxylamine intermediate. The techno-economic analysis based on flow electrolyzer operation confirms the potential economic viability of the electrosynthesis of cyclohexanone oxime. This work provides profound atomic-level insight into cooperative catalysis for C─N coupling reactions toward the electrosynthesis of value-added organonitrogen compounds.

A new hybrid substrate of core-satellite plasma MOF, namely L-Au @ ZIF-67 @ S-Au NPs, was developed in this study. The substrate aims to improve SERS performance by combining the molecular trapping layer of ZIF-67 with double electromagnetic hot spots distributed on the inner surface and the outer surface. The interface self-assembly strategy was adopted to combine MOF with plasma nanostructures, thus combining excellent molecular adsorption ability with strong local field enhancement effect. The results showed that the substrate had a low detection limit of 1.43 & times; 10-10 M for Rhodamine 6G, achieved rapid adsorption equilibrium within 10 min, and showed high signal reproducibility (relative standard deviation, RSD = 1.66%, n = 20). The substrate was applied to the detection of fenpropathrin residues in citrus peels, showing good sensitivity, and the detection limit was 0.995 mu g & sdot;L-1, with an enhancement factor of 3.4 & times; 106 and excellent reproducibility. This work builds a multifunctional and environment-friendly SERS platform, which effectively combines advanced nano-material design with practical applications of food safety monitoring

Rapid detection of pesticide residue using spectral technology is often hindered by the complex constituents of food matrices. Herein, a ratiometric SERS strategy is reported for detecting thiram in tea using an efficient core-shell magnetic-plasmonic substrate loaded with an internal standard (IS), Fe₃O₄@Au4−MBN@Ag. The substrate not only utilizes its magnetic properties to enable the efficient enrichment and separation of thiram from complex matrices, but also incorporates the IS method to mitigate the issue of magnetic field non-uniformity typically arising from the aggregation of magnetic nanomaterials. Critically, high-density hotspots were engineered by optimising the gold seed loading and Ag shell thickness to achieve outstanding SERS performance. The core-shell structure shielded the Raman signal of the IS from matrix interference, thereby ensuring accurate and reliable analysis. Under optimal conditions, the method exhibited a wide linear range from 500 ng/mL to 5 ng/mL and a low detection limit of 0.15 ng/mL, which is below the EU maximum residue limit of 10 ng/mL. This work provides a robust SERS signal correction approach with promising applications in agricultural product safety.