Search Results (780)

As a noninvasive liquid biopsy approach, extracellular vesicle (EV)-based detection offers significant advantages in reflecting real-time tumor dynamis and overcoming the limitations of conventional tissue biopsy. EVs, nanoscale vesicles secreted by cells, carry diverse biomolecules such as proteins and nucleic acids, playing key roles in tumor progression, metastasis, and immune evasion, and have emerged as promising biomarkers for cancer liquid biopsy. Surface-enhanced Raman spectroscopy (SERS), characterized by high sensitivity, resistance to photobleaching, minimal sample consumption, and multiplexing capability, has shown great potential in EV analysis. This review systematically summarizes current methods for EV isolation, characterization, and storage, with a focus on label-free and label-based SERS detection strategies for early cancer diagnosis, treatment response monitoring, and prognosis evaluation. Furthermore, the integration of SERS with machine learning and deep learning algorithms has substantially improved diagnostic accuracy and cancer subtyping. Despite remaining challenges, such as optimization of SERS substrate performance, intelligent processing of Raman spectral fingerprints, and clinical translation, EV-based SERS technology holds great promise for precision oncology. Full article

Raw Chinese lacquer, tapped from the sap of Toxicodendron vernicifluum, is a natural water-in-oil microemulsion containing urushiol, polysaccharides, proteins, and laccase. Because this reactive system continues to oxidize and polymerize after harvesting, handling conditions directly determine water content, viscosity, and later film-forming performance. This review analyzes potential in situ stabilization routes that couple purification, low-temperature vacuum dehydration, and quality conditioning at, or near, the collection site. Emphasis is placed on how laccase retention, oxygen exposure, and urushiol polymerization are controlled together to limit transport losses and premature crusting. Portable filtration devices, reported centrifugal filtration systems, and proposed vacuum dehydration strategies are compared in terms of throughput, field compatibility, and process control. Physical and bio-based conditioning strategies, including shear adjustment, oxygen management, and natural film-forming aids, are further considered for on-site regulation. Surface-enhanced Raman spectroscopy (SERS) and portable spectroscopic devices are examined as feedback tools for parameter adjustment under field temperatures, humidity, and storage variation; however, these signals are treated as decision-support indicators that still require lacquer-specific calibration after tapping. The central task is to define a field-compatible process window for water removal, laccase retention, viscosity control, drying behavior, and storage stability before downstream coating preparation. The remaining challenges involve miniaturized equipment, standardized evaluation, evidence-level classification, and dynamic control of coupled variables. Full article

Structural colors, generated by the physical interaction of light with micro- and nanostructured architectures, have emerged as an important platform in nanophotonics owing to their high color saturation, exceptional photostability, and long-term color durability. This review provides a comprehensive overview of recent advances in structural colors and establishes a unified classification framework based on their macroscopic angular optical responses. The intrinsic angular characteristics of four fundamental color-generation mechanisms are first distinguished, providing the physical basis for classifying structural colors into iridescent and non-iridescent systems. Representative iridescent architectures, including thin films, one-dimensional (1D) to three-dimensional (3D) photonic crystals, and diffraction gratings, are systematically reviewed, together with non-iridescent strategies based on independent plasmonic and dielectric resonators, quasi-amorphous structures, and engineered metasurfaces. Strategies for enhancing structural color visibility and saturation through absorption management are further discussed, particularly for suppressing undesired broadband and multiple-scattering backgrounds. Additionally, this review systematically summarizes quantitative methodologies for evaluating structural colors, including spectral metrics, CIE 1931 and CIE1976 color spaces, CIEDE2000 color difference, quantitative angular-response metrics, spatial resolution and pixel limits, and structural-order characterization using orientation parameters and two-dimensional fast Fourier transform (2D FFT) analysis. Particular attention is given to the quantitative assessment of angular stability through wavelength shifts and perceptual color differences, while recognizing that a universally accepted numerical boundary between iridescent and non-iridescent coloration has not yet been established. Representative functional applications are also reviewed, including self-cleaning coatings, passive daytime radiative cooling, label-free chemical and gas sensing, reflectometric interference spectroscopy (RIfS), surface-enhanced Raman scattering (SERS), and anti-counterfeiting. By integrating color-generation mechanisms, angular optical responses, quantitative evaluation methods, and functional applications, this review provides a unified framework for objectively comparing structural color platforms and highlights key trade-offs among color quality, angular stability, structural precision, durability, scalability, and multifunctionality, thereby providing design guidance for next-generation optical materials and devices. Full article

Shigella is a foodborne bacterial pathogen with a low infectious dose and significant public health impact. Culture-based and molecular techniques provide reliable identification but are time-consuming. Nanoparticle-based biosensors offer sensitive, selective, and compact alternatives. Recent advances in nanoparticle-based biosensors for Shigella spp. (S. flexneri, S. sonnei, S. dysenteriae, and S. boydii) detection have been reviewed in terms of signal amplification, biorecognition, biological targets, sensor types, and performance in real food matrices. Detection strategies rely on gene-level and whole-cell recognition. Targeting virulence genes, invasion plasmid antigen H (ipaH), provides stable genus-level identification, whereas whole-cell recognition facilitates rapid detection without extensive sample preparation. Optical biosensors, including fluorescence-based methods, surface-enhanced Raman spectroscopy (SERS), and localized surface plasmon resonance (LSPR), achieve low detection limits with strong tolerance to complex food matrices. Electrochemical biosensors offer operational simplicity, portability, and suitability for food screening. Lateral flow and hybrid systems provide rapid detection through simplified assay formats and visual readout, with performance influenced by the balance between speed and sensitivity. Validation in real food matrices shows acceptable recoveries, minimal cross-reactivity, and agreement with reference methods. This overview provides a design-oriented framework for nanoparticle-based biosensor selection in food safety by integrating nanomaterial function, biosensor design, and performance characteristics. Full article

Fish pathogens pose significant challenges to aquaculture, causing high mortality and economic losses. Thus, rapid, accurate, and cost-effective identification methods for disease management are needed. Conventional bacterial identification techniques are time-consuming and costly. Raman spectroscopy is an emerging alternative, enabling species differentiation based on molecular signatures. This study investigates surface-enhanced Raman spectroscopy (SERS) combined with principal component analysis with linear discriminant analysis (PCA-LDA) and partial least-squares discriminant analysis (PLS-DA) to classify three relevant Vibrio species: Vibrio alginolyticus, Vibrio parahaemolyticus, and Vibrio vulnificus. SERS spectra from bacterial cultures were analyzed to identify species-specific variations. Distinct clustering was observed, with V. vulnificus showing stronger amide-related signals, V. parahaemolyticus exhibiting higher sulfur-related Raman peaks, and V. alginolyticus showing distinct lipid region signals. The PCA-LDA model achieved 100% classification and 97.06% cross-validation accuracy under standardized laboratory conditions in this controlled proof-of-concept study using spectral replicates obtained from cultured laboratory bacterial stocks. The PLS-DA model showed excellent discriminatory performance (AUC = 1.00), with strong calibration for two species (R² > 0.80) and slightly lower for V. alginolyticus (R² = 0.71). These findings demonstrate that SERS combined with multivariate analysis is a promising approach for rapid and label-free Vibrio identification and classification under controlled laboratory conditions. Full article

Hospital wastewater has begun to act as an important source of emerging contaminants, including rare earth elements (REEs), mostly lanthanides, and radiopharmaceutical residues originating from diagnostic and therapeutic nuclear medicine procedures. The increasing use of gadolinium-based contrast agents, lutetium-containing therapeutics, technetium-99m tracers, iodine-131, yttrium-90, and gallium-68 radiopharmaceuticals has raised growing concerns regarding the release of radioactive and metal-containing compounds into aquatic environments. Conventional wastewater treatment plants are often inefficient in removing these contaminants because of their high chemical stability, low environmental concentrations, and complex aqueous speciation. In this context, hybrid nanomaterials containing silver nanoparticles (AgNPs) have attracted increasing interest for both monitoring and remediation applications. AgNP-based systems exhibit unique plasmonic, catalytic, antimicrobial, and sensing properties that can be exploited in adsorption, photocatalysis, membrane filtration, electrochemical detection, and surface-enhanced Raman spectroscopy (SERS). This review critically analyzes recent advances in hospital wastewater treatment and how hybrid nanomaterials containing silver nanoparticles (AgNPs) are emerging. The review places a particular focus on contamination by REEs and radiopharmaceuticals residues, which to date, as far as we know, remains a challenging and understudied aspect, as reflected by limited publications. Full article

Chili products are vulnerable to the addition of unauthorized substances, including Sudan dyes, Rhodamine B, Basic Orange 2, poppy-derived materials and improperly used processing chemicals. Their analysis is complicated by the high contents of lipids, carotenoids, capsaicinoids and other co-extracted matrix components in chili powder, chili oil, chili sauce and composite seasonings. This review critically evaluates conventional and emerging sample-preparation strategies, including solid-phase extraction; the quick, easy, cheap, effective, rugged and safe (QuEChERS) procedure; deep eutectic solvent (DES)-assisted extraction; enhanced matrix removal for lipids (EMR-Lipid); and molecularly imprinted sorbents. Laboratory methods based on high-performance liquid chromatography (HPLC), liquid chromatography–tandem mass spectrometry (LC–MS/MS) and gas chromatography–mass spectrometry (GC–MS) are compared with enzyme-linked immunosorbent assay (ELISA), surface-enhanced Raman spectroscopy (SERS), electrochemical sensors, miniature mass spectrometry and artificial intelligence-assisted hyperspectral imaging (AI–HSI). The comparison considers representative limits of detection and quantification, recovery, precision, sample-preparation burden, cost, portability, validation status and regulatory role. LC–MS/MS remains the preferred confirmatory platform for targeted multi-residue analysis, whereas rapid and portable methods are more appropriate for screening and sample triage. A three-tier workflow linking rapid screening, laboratory confirmation, and emerging-risk identification and traceability is proposed. Future priorities include standardized chili reference materials, open AI training and validation datasets, greener DES-based extraction and interlaboratory validation of field-deployable methods. Full article

Detection of nanoplastics in drinking-water systems is only the first analytical step toward exposure interpretation; the next challenge is source attribution. This review examines molecular fingerprinting and transformation pathways that can link nanoscale polymer signals to source waters, drinking-water treatment, distribution infrastructure, packaging materials, laboratory background, or aging processes across the potable-water chain. Nanoplastics are treated here as operationally defined particles below 1 µm, including intentionally manufactured primary nanoplastics and secondary nanoplastics generated by fragmentation, abrasion, weathering, treatment, storage, or packaging stress. The synthesis evaluates how polymer identity, particle morphology, surface oxidation, additive and oligomer profiles, thermal degradation markers, matrix context, and quality assurance/quality control (QA/QC) can be combined into defensible source assignments. Analytical platforms considered include surface-enhanced Raman spectroscopy (SERS), atomic force microscopy–infrared spectroscopy (AFM-IR), optical photothermal infrared spectroscopy (O-PTIR), stimulated Raman scattering microscopy (SRS), pyrolysis–gas chromatography–mass spectrometry (Py-GC/MS), asymmetric flow field-flow fractionation coupled to Py-GC/MS (AF4-Py-GC/MS), matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS), and chemometric workflows. The central conclusion is that source attribution cannot be inferred from polymer identity alone; robust interpretation requires convergent evidence from particle-level chemistry, polymer-specific mass, additive or marker-ion signatures, aging state, blanks, recovery, and contextual sampling design. Full article

The study of ancient sculptural polychromy increasingly relies on non-invasive analytical approaches capable of identifying pigments and reconstructing original decorative schemes while preserving the integrity of archaeological objects. This paper presents the investigation of the polychromy, extraordinarily preserved, on two Roman marble statues discovered in the forum of Formia (southern Latium) and currently housed in the National Archaeological Museum of Formia: a togate statue (inv. 147614) and a headless draped female figure (inv. 147680). Both sculptures retain exceptionally well-preserved traces of pigments, offering a rare opportunity to investigate materials and painting techniques applied to Roman marble statuary. The analytical protocol combined multiband imaging (Visible-Induced Luminescence and Ultraviolet Luminescence), optical microscopy, Fiber Optic Reflectance Spectroscopy (FORS), portable X-ray Fluorescence (XRF), and Surface Enhanced Raman Spectroscopy (Raman-SERS) applied to two micro-samples. The analyses allowed the identification of Egyptian blue, iron-based pigments, gilding, and an organic red lake on the palettes used for the statues. Raman-SERS measurements provided additional information on the composition of the organic lake detected on the female statue’s himation, supporting its attribution to a natural vegetal-derived dye, as madder lake. The results highlight the success of integrated non-destructive methodologies for the study of Roman sculptural polychromy and contribute to the reconstruction of complex decorative schemes on marble statuary. Full article

Surface-enhanced Raman spectroscopy (SERS) is a powerful technique for detecting, identifying, and quantifying analytes of interest in both environmental and clinical contexts. A key factor in SERS is the choice of substrate, which directly influences the enhancement factor and measurement reproducibility. A wide range of commercial SERS substrates is currently available, featuring tailored nanostructures and surface patterns designed to optimize signal enhancement. Recently, SERS has also been applied to the development of detection strategies for phenolic compounds. Within this framework, we aimed to evaluate several commercial SERS substrates and one homemade SERS substrate using catechol as a molecular probe. Each substrate was initially assessed by acquiring spectra of the bare substrate using the laser excitation wavelengths recommended by the manufacturers. Raman spectra of catechol solutions at relatively high concentrations were also acquired using the same wavelengths. These preliminary measurements guided the selection of experimental conditions for subsequent substrate performance evaluations. Hyperbola and linear function fitting were performed to quantitatively characterize the tested substrates in catechol detection. The proposed approach allowed for the identification of a parameter that can be used for estimating a substrate’s overall efficiency, along with the main sensing figures of merit. Full article

This systematic review summarises advances in artificial intelligence (AI) and machine learning (ML) for foodborne pathogen detection, covering applications in various technologies (AI-assisted microscopy, spectroscopy, biosensors and sensor-based systems), food supply chains, analytical performance, operational metrics and regulatory developments, addressing gaps in previous reviews limited to individual technologies or lacking regulatory analysis. Following PRISMA 2020 guidelines, Scopus, PubMed, and Web of Science were searched from 1 January 2010 to 25 June 2026 using a validated string. Inclusion criteria were explicit detection of a pathogen, clearly described AI/ML algorithm, study evaluation on food or supply chains, and quantitative validation metrics. Exclusion criteria were chemical-only studies, human-diagnostic studies, or purely theoretical studies. Given heterogeneity in the evidence, qualitative quality indicators were favoured over formal quantitative risk-of-bias tools, in distinction to internal cross-validation versus independent external validation. Key data were extracted using a standardised matrix, and after screening and snowballing, the final corpus consisted of 152 studies. CNN (Convolutional Neural Network)-based microscopy provides >99% accuracy in bacterial identification, SERS (Surface-Enhanced Raman Spectroscopy) and CNN 98.68% for pathogens and 99.85% for resistant strains. ML-driven biosensors show 80–100% prediction accuracy in the presence of environmental noise. Yet, performance drops dramatically on external validation, with models falling from 95% internal to 78–82% on independent test sets. Supply chain applications cover meat, dairy, seafood and produce, but most are still at pilot scale. The main constraints are data heterogeneity, lack of public benchmarks, matrix interference, non-standard validation protocols, and regulatory dissonance. However, the integration of AI with Internet of Things (IoT), blockchain and edge computing improves sensitivity, reduces false results and enables real-time monitoring despite the challenges. AI is a powerful decision-support tool that complements existing food safety controls rather than replacing them. To translate these technologies reliably into routine practice, effective implementation requires rigorous external validation and regulatory harmonisation. Full article

Early sex identification is essential for the propagation, cultivation, quality improvement, and germplasm management of dioecious horticultural plants and related functionally dioecious systems, particularly in perennial species with long juvenile phases. However, the reliability and transferability of sex-identification technologies depend strongly on the underlying sex-determining mechanism. Here, we synthesize recent advances in plant sex determination and diagnostic technologies, ranging from morphological and biochemical traits to molecular markers, high-throughput sequencing, structural-variant detection, and emerging non-invasive phenotyping. We propose that sex-identification strategies should be selected according to the biological target generated by each mechanism, including heteromorphic sex chromosomes, homomorphic sex-determining regions (SDRs), functional sex-determining genes, sex chromosome turnover, dosage-dependent systems, and environmentally labile sex expression. We further distinguish genetic, developmental, physiological, and phenotypic layers of plant sex, emphasizing that DNA markers and spectral phenotyping provide complementary information. Genomic markers and non-invasive phenotyping are expected to be consistent when genetic sex is stably expressed, but they may become inconsistent when sex expression is developmentally, hormonally, or environmentally modulated. While molecular markers remain the most reliable tools for confirmatory genotyping, Raman spectroscopy, surface-enhanced Raman scattering (SERS), hyperspectral imaging, and machine learning may serve as rapid prescreening tools in large breeding populations, although their application remains at the proof-of-concept stage. Finally, we present a mechanism-guided decision framework for integrating genomic markers and non-invasive phenotyping to support early sex screening, propagation planning, planting-material optimization, and marker-assisted improvement in dioecious horticultural plants. Full article

The ability to manipulate and probe individual nano-particles, viruses, and organelles with high sensitivity and specificity is an essential part of modern nanoscience and molecular biology. Plasmonic optical tweezers (POT), which use localized surface plasmons to create nanoscale-confined optical fields, have emerged as a powerful platform for trapping and manipulating single nano–bio entities at low optical powers. When combined with surface-enhanced Raman spectroscopy (SERS) from the same plasmonic nanostructures, these platforms offer a unique multi-modal capability: simultaneous optical manipulation and label-free chemical fingerprinting of a single specimen. However, the field faces critical challenges, including low throughput, thermal noise, photothermal damage, and the overwhelming complexity of interpreting dynamic, single-molecule SERS data. This review examines the transformation of plasmonic optical trapping and spectroscopy as they evolve toward autonomous operation and intelligent decision-making. We begin with the fundamental principles that enable these tools to manipulate and probe single viruses, organelles, and nano-particles. Building on this foundation, we explore how computational intelligence is being integrated into the field to address long-standing challenges. This includes the emergence of data-driven methods for designing optimized plasmonic nanostructures, for decoding the complex molecular fingerprints hidden in single-molecule SERS spectra, and for creating feedback-driven systems capable of adaptive, real-time experiment control. By synthesizing these developments, we illustrate a clear trajectory: from manually operated instruments toward fully integrated intelligent nanophotonic laboratories that can autonomously discover and characterize the nano-world. We conclude by discussing the remaining challenges—from data acquisition and model interpretability to the mitigation of photothermal effects—and the most promising pathways toward realizing this transformative vision for virology, cell biology, and nanomedicine. Full article

Surface-enhanced Raman spectroscopy (SERS) has emerged as a highly promising analytical technique for disease diagnostics due to its exceptional sensitivity, molecular specificity, and ability to detect a broad range of biomarkers in complex biological matrices. This review provides a comprehensive overview of gold- and silver-nanoparticle-based SERS platforms for plasma disease diagnostics, covering advances in plasmonic nanostructures, biological sample analysis, biomarker detection, and AI-driven spectral data processing. Particular emphasis is placed on the application of SERS to clinically relevant biofluids, especially plasma, where the technique has demonstrated considerable potential for detecting diseases such as cancer, inflammatory disorders, and neurological conditions. The review also critically examines the major challenges currently limiting the clinical translation of SERS technologies. These include variability associated with substrate fabrication, matrix-induced signal fluctuations, limited interlaboratory reproducibility, and the lack of standardized protocols for spectral preprocessing and data analysis. Strategies proposed to address these issues are discussed, including comprehensive post-synthesis substrate characterization, optimization of biological sample preparation, advanced spectral preprocessing workflows, and the integration of machine learning and artificial intelligence algorithms to improve diagnostic robustness and reproducibility. Collectively, the advances summarized in this review indicate that SERS-based diagnostic technologies are rapidly progressing beyond proof-of-concept studies toward clinically applicable systems. Continued interdisciplinary collaboration and standardization efforts will be essential to bridge the remaining gap between experimental SERS methodologies and routine clinical implementation. Full article

This review systematically summarizes the inherent characteristics and application superiorities of various nanomaterials, including metallic nanomaterials, metal oxides, carbon-based materials, metal–organic frameworks (MOFs), and quantum dots (QDs). State-of-the-art research progress is elaborated on the applications of these nanomaterials in multiple analytical techniques, such as surface-enhanced Raman spectroscopy (SERS), fluorescence spectroscopy, infrared spectroscopy, ultraviolet-visible spectroscopy, and electrochemistry. Furthermore, their pivotal functions in signal amplification, specific molecular recognition, and rapid analyte enrichment are thoroughly discussed. Additionally, this paper analyzes the prevailing challenges, including material heterogeneity, potential biosafety risks, poor anti-interference capacity against complex matrices, and the absence of unified industrial standardization. Future development directions are also proposed, involving green synthesis strategies, precise functional modification, portable intelligent detection, and simultaneous multi-component detection. This work aims to provide a reliable reference for further fundamental research and industrial translation of nanomaterials in the rapid and high-precision detection of pesticide residues. Full article