Search Results (936)

The detection of trace chemicals at low and ultra-low concentrations is critical for applications in environmental monitoring, medical diagnostics, food safety and other fields. Conventional detection techniques often lack the required sensitivity, specificity, or cost-effectiveness, making real-time, in situ analysis challenging. Surface-enhanced Raman spectroscopy (SERS) is a powerful analytical tool, offering improved sensitivity through the enhancement of Raman scattering by plasmonic nanostructures. While noble metals such as Ag and Au are currently the reference choices for SERS substrates, fabrication methods should balance enhancement efficiency, reproducibility and scalability. In this study, we propose a novel approach for SERS substrate fabrication using reactive Aerosol Jet Printing (r-AJP) as an innovative additive manufacturing technique. The r-AJP process enables in-flight Ag seed reduction and nucleation of Ag nanoparticles (NPs) by mixing silver nitrate and ascorbic acid aerosols before deposition, as suggested by computational fluid dynamics (CFD) simulations. The resulting coatings were characterized by X-ray diffraction (XRD) and scanning electron microscopy (SEM) analyses, revealing the formation of nanoporous crystalline Ag agglomerates partially covered by residual matter. The as-prepared SERS substrates exhibited remarkable SERS activity, demonstrating a high enhancement factor (10⁶) for rhodamine (R6G) detection. Our findings highlight the potential of r-AJP as a scalable and cost-effective fabrication strategy for next-generation SERS sensors, paving the way for the development of a new additive manufacturing tool for noble metal material deposition. Full article

Pesticide residues in fruits and vegetables pose a serious threat to food safety. Traditional detection methods have defects such as complex operation, high cost, and long detection time. Therefore, it is of great significance to develop rapid, non-destructive, and efficient detection technologies and equipment. In recent years, the combination of spectroscopic techniques and imaging technologies with machine learning algorithms has developed rapidly, providing a new attempt to solve this problem. This review focuses on the research progress of the combination of spectroscopic techniques (near-infrared spectroscopy (NIRS), hyperspectral imaging technology (HSI), surface-enhanced Raman scattering (SERS), laser-induced breakdown spectroscopy (LIBS), and imaging techniques (visible light (VIS) imaging, NIRS imaging, HSI technology, terahertz imaging) with machine learning algorithms in the detection of pesticide residues in fruits and vegetables. It also explores the huge challenges faced by the application of spectroscopic and imaging technologies combined with machine learning algorithms in the intelligent perception of pesticide residues in fruits and vegetables: the performance of machine learning models requires further enhancement, the fusion of imaging and spectral data presents technical difficulties, and the commercialization of hardware devices remains underdeveloped. This review has proposed an innovative method that integrates spectral and image data, enhancing the accuracy of pesticide residue detection through the construction of interpretable machine learning algorithms, and providing support for the intelligent sensing and analysis of agricultural and food products. Full article

The coffee-ring effect, while harnessed in diverse fields such as biosensing and printing, poses challenges for achieving uniform particle deposition. Controlling this phenomenon is thus essential for precision patterning. This study proposes a novel method to regulate coffee-ring formation by tuning surface wettability via integrated nanoporous and hexagonal microstructures. Four distinct surface types were fabricated using UV nanoimprint lithography: planar, porous planar, hexagonal wall, and porous hexagonal wall. The evaporation behavior of colloidal droplets and subsequent particle aggregation were analyzed through contact angle measurements and confocal microscopy. Results demonstrated that nanoscale porosity significantly increased surface wettability and accelerated evaporation, while the hexagonal pattern enhanced droplet stability and suppressed contact line movement. The porous hexagonal surface, in particular, enabled the formation of connected dual-ring patterns with higher particle accumulation near the contact edge. This synergistic design facilitated both stable evaporation and improved localization of particles. The findings provide a quantitative basis for applying patterned porous surfaces in evaporation-driven platforms, with implications for enhanced sensitivity and reproducibility in surface-enhanced Raman scattering (SERS) and other biosensing applications. Full article

Exosomes are nanoscale extracellular vesicles (EVs) that carry biomolecular signatures reflective of their parent cells, making them powerful tools for non-invasive diagnostics and therapeutic monitoring. Despite their potential, clinical application is hindered by challenges such as low abundance, heterogeneity, and the complexity of biological samples. To address these limitations, plasmonic biosensing technologies—particularly propagating surface plasmon resonance (PSPR), localized surface plasmon resonance (LSPR), and surface-enhanced Raman scattering (SERS)—have been developed to enable label-free, highly sensitive, and multiplexed detection at the single-vesicle level. This review outlines recent advancements in nanoplasmonic platforms for exosome detection and profiling, emphasizing innovations in nanostructure engineering, microfluidic integration, and signal enhancement. Representative applications in oncology, neurology, and immunology are discussed, along with the increasingly critical role of artificial intelligence (AI) in spectral interpretation and diagnostic classification. Key technical and translational challenges—such as assay standardization, substrate reproducibility, and clinical validation—are also addressed. Overall, this review highlights the synergy between exosome biology and plasmonic nanotechnology, offering a path toward real-time, precision diagnostics via sub-femtomolar detection of exosomal miRNAs through next-generation biosensing strategies. Full article

Au-Ag alloy nanoshells (ANSs) were synthesized via chemical reduction, exhibiting superior plasmonic photocatalytic performance. TEM revealed uniform hollow structures (~80 nm), while EDS mapping confirmed homogeneous Au-Ag distribution throughout the shell. According to EDX analysis, the alloy contained 71.40% Ag by weight. XRD verified the formation of a substitutional solid solution without phase separation. The photocatalytic activity was evaluated using p-nitrothiophenol (PNTP) to 4,4′-dimercapto-azobenzene (DMAB) conversion monitored by SERS. UV-Vis spectroscopy showed LSPR peaks of ANSs between Au and Ag NPs, confirming effective alloying. Kinetic studies revealed that ANSs exhibited reaction rates 250–351 times higher than those of Au NPs and 5–10 times higher than those of Ag NPs. This resulted from the synergistic catalysis of Au-Ag and enhanced electromagnetic fields. ANSs demonstrated dual functionality as SERS substrates and photocatalysts, providing a foundation for developing multifunctional nanocatalytic materials. Full article

Magnetotactic bacteria (MTB), a unique group of Gram-negative prokaryotes, have the remarkable ability to biomineralize magnetic nanoparticles (MNPs) intracellularly, making them promising candidates for various biomedical applications such as biosensors, drug delivery, imaging contrast agents, and cancer-targeted therapies. To fully exploit the potential of MTB, a precise understanding of the structural, surface, and functional properties of these biologically produced nanoparticles is required. Given these concerns, this review provides a focused synthesis of the most widely used microscopic and spectroscopic methods applied in the characterization of MTB and their associated MNPs, covering the latest research from January 2022 to May 2025. Specifically, various optical microscopy techniques (e.g., transmission electron microscopy (TEM), scanning electron microscopy (SEM), and atomic force microscopy (AFM)) and spectroscopic approaches (e.g., localized surface plasmon resonance (LSPR), surface-enhanced Raman scattering (SERS), and X-ray photoelectron spectroscopy (XPS)) relevant to ultrasensitive MTB biosensor development are herein discussed and compared in term of their advantages and disadvantages. Overall, the novelty of this work lies in its clarity and structure, aiming to consolidate and simplify access to the most current and effective characterization techniques. Furthermore, several gaps in the characterization methods of MTB were identified, and new directions of methods that can be integrated into the study, analysis, and characterization of these bacteria are suggested in exhaustive manner. Finally, to the authors’ knowledge, this is the first comprehensive overview of characterization techniques that could serve as a practical resource for both younger and more experienced researchers seeking to optimize the use of MTB in the development of advanced biosensing systems and other biomedical tools. Full article

It is difficult to simultaneously achieve surface-enhanced Raman scattering (SERS) and surface-enhanced fluorescence (SEF) for noble metals. Herein, a composite substrate is demonstrated based on the rational construction of Ag nanoparticles (Ag NPs) and inverse opal polydimethylsiloxane (PDMS) for surface Raman fluorescence dual enhancement. The well-designed Ag nanoparticle (Ag NP)-decorated inverse opal PDMS (AIOP) composite substrate is fabricated using the polystyrene (PS) photonic crystal method and the sensitization reduction technique. The inverse opal PDMS enhances the electromagnetic (EM) field by increasing the loading of Ag NPs and plasmonic coupling of Ag NPs, leading to SERS activity. The thin shell layer of polyvinyl pyrrolidone (PVP) in core–shell Ag NPs isolates the detected molecule from the Ag core to prevent the fluorescence resonance energy transfer and charge transfer to eliminate fluorescence quenching and enable SEF performance. Based on the blockage of the core–shell structure and the enhanced EM field originating from the inverse opal structure, the as-fabricated AIOP composite substrate shows dual enhancement in surface Raman fluorescence. The AIOP composite substrate in this work, which combines improved SERS activity and SEF performance, not only promotes the development of surface-enhanced spectroscopy but also shows promise for applications in flexible sensors. Full article

Salmonella is a rapidly spreading and widespread zoonotic infectious disease that poses a serious threat to the safety of both poultry and human lives. Therefore, the timely detection of Salmonella in foods and animals has become an urgent need for food safety. This work describes the construction of an aptamer-based sensor for Salmonella detection, using Fe₃O₄ magnetic beads and Ag@Au core–shell nanoparticles-embedded 4-mercaptobenzoic acid (4MBA). Leveraging the high affinity between biotin and streptavidin, aptamers were conjugated to Fe₃O₄ magnetic beads. These beads were then combined with Ag@4MBA@Au nanoparticles functionalized with complementary aptamers through hydrogen bonding and π-π stacking interactions, yielding a SERS-based aptamer sensor with optimized Raman signals from 4MBA. When target bacteria are present, aptamer-conjugated magnetic beads exhibit preferential binding to the bacteria, leading to a decrease in the surface-enhanced Raman scattering (SERS) signal. And it was used for the detection of five different serotypes of Salmonella, respectively, and the results showed that the aptamer sensor exhibited a good linear relationship between the concentration range of 10²–10⁸ CFU/mL and LOD is 35.51 CFU/mL. The SERS aptasensor was utilized for the detection of spiked authentic samples with recoveries between 94.0 and 100.4%, which proved the usability of the method and helped to achieve food safety detection. Full article

Highly infectious and pathogenic viruses seriously threaten global public health, underscoring the need for rapid and accurate diagnostic methods to effectively manage and control outbreaks. In this study, we developed a comprehensive Surface-Enhanced Raman Scattering–Lateral Flow Immunoassay (SERS-LFIA) detection system that integrates SERS scanning imaging with artificial intelligence (AI)-based result discrimination. This system was based on an ultra-sensitive SERS-LFIA strip with SiO₂-Au NSs as the immunoprobe (with a theoretical limit of detection (LOD) of 1.8 pg/mL). On this basis, a negative–positive discrimination method combining SERS scanning imaging with a deep learning model (ResNet-18) was developed to analyze probe distribution patterns near the T line. The proposed machine learning method significantly reduced the interference of abnormal signals and achieved reliable detection at concentrations as low as 2.5 pg/mL, which was close to the theoretical Raman LOD. The accuracy of the proposed ResNet-18 image recognition model was 100% for the training set and 94.52% for the testing set, respectively. In summary, the proposed SERS-LFIA detection system that integrates detection, scanning, imaging, and AI automated result determination can achieve the simplification of detection process, elimination of the need for specialized personnel, reduction in test time, and improvement of diagnostic reliability, which exhibits great clinical potential and offers a robust technical foundation for detecting other highly pathogenic viruses, providing a versatile and highly sensitive detection method adaptable for future pandemic prevention. Full article

This study investigates the effects of substrate flexibility and metal deposition methods on piezoelectric-enhanced Surface-Enhanced Raman Scattering (SERS) in metal-deposited ZnO nanorod (NR) nanocomposites (NCPs). ZnO NRs were grown on both rigid (ITO–glass) and flexible (ITO-PET) substrates, followed by gold (Au) deposition by pulsed-laser-induced photolysis (PLIP) or silver (Ag) deposition by thermal evaporation. Structural analysis revealed that ZnO NRs on flexible substrates exhibited smaller diameters (60–80 nm vs. 80–100 nm on glass), a higher density, and diverse orientations that enhanced piezoelectric responsiveness. Optical characterization showed distinct localized surface plasmon resonance (LSPR) peaks at 420 nm for Ag and 525 nm for Au systems. SERS measurements demonstrated that Ag-ZnO NCPs achieved superior detection limits (10⁻⁹ M R6G) with enhancement factors of 10⁸–10⁹, while Au-ZnO NCPs reached 10⁻⁸ M detection limits. Mechanical bending of flexible substrates induced dramatic signal enhancement (50–100-fold for Au-ZnO/PET and 2–3-fold for Ag-ZnO/PET), directly confirming piezoelectric enhancement mechanisms. This work establishes quantitative structure–property relationships in piezoelectric-enhanced SERS and provides design principles for high-performance flexible sensors. Full article

Galactomannan (GM) is a polysaccharide secreted by opportunistic pathogenic fungi of the Aspergillus genus. It is prescribed as a diagnostic biomarker of invasive aspergillosis in immunocompromised patients by the guidelines for diagnosis and management of Aspergillus diseases. It has been shown previously that the measurement of soluble horseradish peroxidase (HRP) using surface-enhanced Raman scattering (SERS) of 2,3-diaminophenazine enzymatic reaction product on silver nanoparticles is largely superior in detection limit compared to colorimetric readout. In this study, a highly sensitive SERS-based HRP measurement protocol was applied to enzyme-linked immunosorbent assay (ELISA) for GM quantification in biological fluids. The detection limit for GM was 4.3 pg per sample, which is one and a half orders of magnitude lower compared to colorimetric detection with o-phenylenediamine as a substrate and five times more sensitive than ELISA using 3,3′,5,5′-tetramethylbenzidine. Full article

In clinical applications of surface-enhanced Raman spectroscopy (SERS) immunosensors, accurately determining analyte biomarker concentrations is essential. This study presents a non-invasive approach for quantifying various breast cancer biomarkers—including human epidermal growth factor receptor II (HER-II) (2+, 3+ (I), 3+ (II), 3+ (III), and positive IV) and CA 15-3—using a directional, plasmonically active, label-free SERS sensor. Each stage of sensor functionalization, conjugation, and biomarker interaction was verified by UV–Vis spectroscopy. Atomic force microscopy (AFM) characterized the morphology of gold nanourchin (GNU)-immobilized printed circuit board (PCB) substrates. An enhancement factor of ≈ 0.5 × 10⁵ was achieved using Rhodamine 6G as the probe molecule. Calibration curves were initially established using standard HER-II solutions at concentrations ranging from 1 to 100 ng/mL and CA 15-3 at concentrations from 10 to 100 U/mL. The SERS signal intensities in the 620–720 nm region were plotted against concentration, yielding linear sensitivity with R² values of 0.942 and 0.800 for HER-II and CA15-3, respectively. The same procedure was applied to breast cancer serum (BCS) samples, allowing unknown biomarker concentrations to be determined based on the corresponding calibration curves. SERS data were processed using the filtfilt filter from scipy.signal for smoothing and then baseline-corrected with the Improved Asymmetric Least Squares (IASLS) algorithm from the pybaselines.Whittaker library. Principal Component Analysis (PCA) effectively distinguished the sample groups and revealed spectral differences before and after biomarker interactions. Key Raman peaks were attributed to functional groups including N–H (primary and secondary amines), C–H antisymmetric stretching, C–N (amines), C=O antisymmetric stretching, NH₃⁺ (amines), carbohydrates, glycine, alanine, amides III, C=N stretches, and NH₂ in primary amides. Full article

The detection of Fe²⁺ in environmental water sources is critical due to its biological relevance and potential toxicity at elevated levels. Herein, we report a plasmon-driven catalytic sensing nanoplatform based on p-aminothiophenol (pATP)-functionalized silver nanoparticles (AgNPs) for the selective and sensitive detection of Fe²⁺. The nanoplatform exploits the inhibition of the plasmon-driven catalytic conversion of pATP to 4,4-dimercaptoazobenzene (DMAB), monitored via surface-enhanced Raman scattering (SERS) spectroscopy. The catalytic efficiency was quantified by the intensity ratio between the formed DMAB-specific Raman band and the common aromatic ring vibration band of pATP and DMAB. This ratio decreased proportionally with increasing Fe²⁺ concentration over a range of 100 µM to 1.5 mM, with a calculated limit of detection of 39.7 µM. High selectivity was demonstrated against common metal ions, and excellent recovery rates (96.6–99.4%) were obtained in real water samples. Mechanistic insights, supported by chronopotentiometric measurements under light irradiation, revealed a competitive oxidation pathway in which Fe²⁺ preferentially consumes plasmon-generated hot holes over pATP. This mechanism clarifies the observed catalytic inhibition and supports the design of redox-responsive SERS sensors. The platform offers a rapid, low-cost, and portable solution for Fe²⁺ monitoring and holds promise for broader applications in detecting other redox-active analytes in complex environmental matrices. Full article

In recent years, non-invasive methods for the analysis of biological fluids have attracted growing interest. In this study, we propose a straightforward approach to fabricating silver nanoparticle (AgNP)-coated non-woven polyacrylonitrile substrates for surface-enhanced Raman scattering (SERS). AgNPs were synthesized directly on the substrate using borohydride reduction, ensuring uniform distribution. The optimized SERS substrates exhibited a high enhancement factor (EF) of up to 10⁵ for the detection of 4-mercaptobenzoic acid (4-MBA). To enable glucose sensing, the substrates were further functionalized with glucose oxidase (GOx), allowing detection in the 1–10 mM range. Machine learning classification and regression models based on gradient boosting were employed to analyze SERS spectra, enhancing the accuracy of quantitative predictions (R² = 0.971, accuracy = 0.938, limit of detection = 0.66 mM). These results highlight the potential of AgNP-modified substrates for reliable and reusable biochemical sensing applications. Full article

MXenes are a class of two-dimensional materials exhibiting excellent surface-enhanced Raman scattering (SERS) performance. Currently, the SERS studies of MXenes have been primarily focused on those with M₂X and M₃X₂ structural motifs. In order to expand the SERS sensing application based on MXenes, in this paper, a SERS sensor made of Ta₄C₃@AgNP nanocomposite material was fabricated by electrostatic self-assembly. Tests such as different concentrations of R6G probe molecules showed that the minimum detection limit of this SERS sensor was 10⁻⁸ M, demonstrating excellent sensitivity. When different test areas are selected, the relative error of intensity under the same wave number is less than 10.7%, showing good repeatability and consistency. Furthermore, the Ta₄C₃@AgNP nanocomposite SERS sensor was used to detect the pesticide Ziram, and a quantitative model was established. Application detection indicates that this sensor has good sensitivity for the pesticide Ziram, and the minimum detection limit was 10⁻⁶ M, which exceeded national standard requirements. The findings of this study have potential application value in the fields of food safety and environmental protection. Full article