ANALYTICA CHIMICA ACTA
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2025
·
IF 6
Hairong Chen,Hengchang Zhang,Lingfeng Duan,Kaidi Sun,Rui Li,Yunxia Wang,Jiacheng Yao,Changchun Yan,Ying Liu,Ying Wu,Guohai Yang,Caiqin Han
Interleukins (IL) play a vital part in molecular recognition as well as immune response, and their excessive or inappropriate production can lead to inflammatory and cardiovascular diseases, etc., which are harmful to human physical and mental health. Thus, there is a pressing need to develop a strategy for the rapid and highly sensitive detection of such low concentration substances. In this study, we developed a Surface enhanced Raman spectroscopy (SERS) immunosensor for qualitative and quantitative analysis of multiple cytokines (IL-1, IL-4 and IL-6). Three Raman reporters were modified on gold nanoparticles and coupled with corresponding antibodies to form immunoprobes. The silver nanospace folded rods were prepared by tilted angle deposition technique, connected with antibodies to form an immunosensor. When the target antigen appeared on the immunosensor, it would specifically bind with the antibody to form a “sandwich” structure. Respectively, the detection limits of IL-6, IL-1 and IL-4 were 1.00 pg/mL, 0.93 pg/mL and 1.00 pg/mL. The three inflammatory factors were successfully distinguished and predicted by machine learning. In addition, we detected and calculated the recovery of IL-6 cytokines in saliva samples. The method can achieve quantitative detection and qualitative differentiation of target detectors in a wide concentration range. Meanwhile, it has the advantages of rapidity, simplicity and high specificity, which has a broad application prospect in biomedical field.
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SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY
·
2025
·
IF 4.6
Yan Wu,Junshi Huang,Longwei Shang,Qi Chen,Fang Peng,Tiantian Liu,Zhijuan Luo,Muhua Liu,Jinhui Zhao,Shuanggen Huang
In this investigation, surface-enhanced Raman spectroscopy (SERS) combined with multivariate analysis was investigated for detection and characterization of ofloxacin residues in egg white. Gold nanoparticles (AuNPs) were meticulously synthesized to serve as the substrate for enhancing Raman signals, its average particle size was approximates 50 nm, and a significant absorption peak appears at 536 nm. The SERS characteristic peaks of ofloxacin were conducted using the density functional theory (DFT) calculations. In an effort to optimize the quantitative detection conditions for ofloxacin residues in egg white, the optimal detection parameters were determined via a single-factor experiment. Following a meticulous comparison of seven distinct spectral preprocessing methodologies, the utilization of adaptive iterative re-weighted penalized least squares (air-PLS) combined with savitzky-golay (SG) preprocessing for the spectral datawithin 400 to 1800 cm −1 were finally selected. The principal component analysis (PCA) helped establish an optimal support vector regression (SVR) model, which was subsequently employed to quantitatively detect the concentration of ofloxacin residues in egg white. It is noteworthy that the SVR model demonstrated outstanding performance metrics, with an R-squared prediction (R2 p) value of 0.9896, root mean square error of prediction (RMSEP) of 1.6246, and residual predictive deviation (RPD) of 8.1720. The adopted methods for detecting ofloxacin is feasible even below 0.5 mg/L. the findings indicate that the SERS-based multivariate analysis technique can swiftly and precisely identify the ofloxacin residues in egg white
研究领域:
分析化学
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食品安全
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光谱学
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纳米技术
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NANO LETTERS
·
2025
·
IF 9.1
Aumber Abbas,Qian Zhang,Jamal Kazmi,Ying Li,Wenbo Li,Waqas Ahmad,Chao Zou,Qijie Liang
Chiral discrimination is crucial in chiral synthesis, pharmaceuticals, and biology. Although surface-enhanced Raman scattering (SERS) provides highly sensitive molecule-specific vibrational data, existing approaches depend on indirect detection involving supplementary selectors, impeding clear and versatile chiral discrimination. Here, we report the direct label-free SERS sensing of different biologically prominent enantiomers with a two-dimensional transition metal dichalcogenide, such as TaS2, based chiral superlattice to produce enantiospecific molecular fingerprints. The clear enantiodiscrimination between a model chiral pair of l- and d-glucose is achieved with a detection limit of 8 × 10–10 M. In addition, noninvasive identification of l- and d-glucose in actual saliva samples and multiplex quantification of enantiomeric ratios with superior predictive accuracy are accomplished.
研究领域:
分析化学
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纳米技术
·
手性光谱学
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生物分析传感
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材料科学
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JOURNAL OF FOOD COMPOSITION AND ANALYSIS
·
2025
·
IF 4.6
Yalin Xie,Rui Tan,Min Hu,Xiaojun Luo,Xiaojun Deng,Yuanyuan Li
Certain diuretics have been identified as potential contaminants in pharmaceutical and food products, posing a risk to food safety and public health. In this study, a surface-enhanced Raman spectroscopy (SERS) detection method based on Silver nanostars (Ag NSs) was developed for the detection of motion-prohibited diuretics in food and biological samples. Ag NSs synthesized via a simple solution-based method exhibited a star-like morphology with high-density "hot spot", achieving an enhancement factor of 1.0 × 10 5 —3 orders of magnitude higher than that of traditional spherical silver nanoparticles. A highly sensitive SERS detection system was established for bumetanide (limit of detection: 7.08 × 10 −9 g/mL), triamterene (1.13 × 10⁻⁸ g/mL), and dorzolamide (1.04 × 10⁻⁸ g/mL) for the first time. Characteristic fingerprint peaks enabled accurate molecular identification. The method was validated in milk and urine with recoveries of 94.2 %–104.9 %, and relative standard deviation (RSD) < 7 %. Results were consistent with those obtained by HPLC-HRMS/MS, confirming the method's reliability. The proposed system offers advantages of rapid detection (<10 min), low cost, and minimal matrix interference, enabling efficient multi-residue analysis in complex samples. This work provides a promising tool for food safety monitoring and anti-doping surveillance, contributing to effective regulatory enforcement.
研究领域:
分析化学
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纳米技术
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食品安全
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药物分析
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生物分析
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ANALYTICAL CHEMISTRY
·
2025
·
IF 6.7
Tiying Zhu,Zhiyang Pei,Xiaofei Zhao,Jing Yu,Baoyuan Man,Zhen Li,Ping-Heng Tan,Yuefeng Zhao,Chao Zhang
Gas is a fundamental element for sustaining life, and its types and concentrations not only reflect the functioning of the human body but also indicate environmental changes. Surface-enhanced Raman scattering (SERS) has become a popular gas detection method due to its high sensitivity and rapid response characteristics. However, in complex environments, efficiently capturing and enriching gases in the “hotspot” regions of substrates still remains a challenge. This study presents an innovative gas SERS sensor that integrates porous Ag cavity structures with zeolitic imidazolate framework-8 (ZIF-8), a type of metal–organic framework (MOF), maintaining high sensitivity and rapid detection capabilities even under varying gas flow rates. The vortex effect within the porous Ag cavities can effectively slow down the gas flow, promoting the adsorption and detection of target molecules; furthermore, the embedded ZIF-8 particles can enhance the gas molecule enrichment efficiency. Experimental results show that, at specific flow rates, the sensor achieves a detection limit of 10 ppb for 4-ethylbenzaldehyde (4-EBA), improving by an order of magnitude (100 ppb) compared to conventional convex (Ag/ZIF-8) structures. What’s more, the multifunctional regions can be achieved facilely on the proposed sensor, supporting the diversified detection of different gases and offering new insights for the development of high-performance gas monitoring technologies.
研究领域:
分析化学
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材料科学
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传感器技术
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物理化学
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纳米科技
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ANALYTICAL CHEMISTRY
·
2025
·
IF 6.7
Tingting Xiao,Weiqiang Wang,Lingyi Zhao,Jian Xie,Weiping Cai
Illicit drug abuse has brought big challenges to public health, because it has caused severe damage to the human central nervous system. The development of a rapid, quantified, and on-site detection of illicit drugs is of key importance for the control of drug abuse. However, it remains challenging due to biological sample interference and difficulties in achieving specific recognition of the drug molecule. Here, we report a molecule-responsive two-component surface-enhanced Raman scattering (SERS) platform for the capture and detection of illicit drugs. Under optimized conditions, the system demonstrates a linear response to cocaine concentrations ranging from 1 ppb to 10 ppm (R2 > 0.99), with an ultralow detection limit of 1.5 ppb. Successful application in spiked bovine serum validates the method’s robustness against complex biological matrices. This work establishes a novel paradigm for detecting trace-level illicit drugs with significant forensic potential. The design principle might be applied to other chemical detection.
研究领域:
分析化学
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生物传感
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法医学
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纳米技术
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JOURNAL OF COLLOID AND INTERFACE SCIENCE
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2025
·
IF 9.7
Xiangqian Kong,Wenyan Xia,Yuruo Qiu,Bingrui Zhao,Yuan Zhao,Wei Ma
Polystyrene (PS) plastics were potentially present in consumer products through the use of plastics packaging. The migration and enduring presence for bioaccumulation posed significant threats to human health and the environment. It was significant to develop a simple and reliable approach for sensitive PS plastics detection. In this study, Au-Bi 2 Se 3 hybrid nanosheets (NSs) were designed and utilized as SERS probes for the detection of PS plastics. The electron transfer from Bi 2 Se 3 to AuCl 4 − induced the formation of Au NPs on the surface of thin Bi 2 Se 3 NSs. Au-Bi 2 Se 3 hybrid NSs were found to display enhanced SERS activity by using PS plastics as Raman tags. Besides the unique electromagnetic enhanced mechanism from Au NPs, the introduction of Bi 2 Se 3 supported the Dirac plasmon polaritons in electromagnetic modes, and Bi 2 Se 3 with high dielectric constants inhibited the decay of electromagnetic waves. Au-Bi 2 Se 3 hybrid NSs engineered SERS sensor achieved stable and sensitive detection of PS plastics in essence liquid. Different sized PS plastics ranging from 89.3 nm to 1.48 μm were detected with the LODs of ppm levels. These findings highlight the potential of Au-Bi 2 Se 3 hybrid NSs as effective and universal SERS substrates for detecting different sized plastics in real samples.
研究领域:
纳米技术
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分析化学
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环境科学
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材料科学
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CHEMICAL ENGINEERING JOURNAL
·
2022
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IF 16.744
Yanbo Wang,Haojie Ni,Huan Li,Jian Chen,Dequan Zhang,Linglin Fu
Microbial contamination of food is a critical public health issue worldwide, making the rapid, efficient, and safe sensing strategies for foodborne microbes in urgent need. In this work, a plasmonic microneedle (MN) array patch was developed for rapid extraction and surface-enhanced Raman spectroscopy (SERS) detection of a model bacteria, Escherichia coli ( E. coli ). A hydrophilic MN matrix was firstly prepared by crosslinking [2-(methacryloyloxy)ethyl]trimethylammonium chloride (TMA), 2-carboxyethyl acrylate (CAA), and 2-hydroxyethyl methacrylate (HEMA) in a micro-mold. Then the obtained MN patch was treated with ozone and coated with SERS-active gold nanopopcorns (GNpops) that were modified with the specific aptamer towards E. coli . The resultant plasmonic MN patch could extract E. coli from agarose simulant in 3 min and was capable of on-MN SERS analysis with a detection limit of 143 CFU/g. The proposed detection method exhibited great specificity to E. coli and was feasible for evaluating the E. coli contamination of commercial mutton samples. Moreover, the extracted bacteria could be effectively inactivated through photothermal sterilization. The work opened a new avenue for simplifying the detection procedures for microorganisms and provided a potential approach for rapid and on-site microbial monitoring through the SERS-active MN patch.
研究领域:
生物医学工程
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分析化学
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食品安全
·
微生物学
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Nanoscale
·
2026
·
IF 5.1
Yide Yang,Zehou Su,Xuanting Liu,Jiaying Song,Chunchun Li,Yikai Xu,Jian Ye,Linley Li Lin
The growing demand for real-time, non-invasive monitoring of biochemical molecules has driven the development of advanced, flexible sensing materials. Surface-enhanced Raman spectroscopy (SERS) offers high molecular specificity and ultralow detection limits. While rigid SERS substrates based on plasmonic nanoparticle arrays provide strong signal enhancements, they lack the mechanical compatibility and conformal adhesion required for dynamic biological surfaces, such as human skin or neural tissues. Here, we present a flexible SERS hydrogel patch for the label-free detection of metabolites at bio-interfaces. The patch integrates a self-assembled silver nanoparticle film with an ultrathin polyvinyl alcohol (PVA) hydrogel layer to achieve good plasmonic enhancement, mechanical durability, conformity and reliable SERS stability. The SERS patch allows the detection of metabolites within 6 min upon analyte exposure, enabling the label-free detection of key metabolites, such as glucose, uric acid and urea with concentrations down to 1 μM, 50 μM and 1 mM, respectively. We demonstrate the versatility of this platform by performing ex vivo experiments on porcine brain and muscle tissues to simulate real-world application scenarios in brain–machine interfaces and implantable sensors. This work demonstrates the feasibility of SERS hydrogel-based flexible platforms for the in situ monitoring of metabolites at bio-interfaces.
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SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY
·
2026
·
IF 4.6
Huizhen Lin,Jiawang Chen,Yiming Chen,Dechan Lu
Early and precise diagnosis of gestational diabetes mellitus (GDM) is crucial for improving maternal and neonatal outcomes and reducing the risk of adverse pregnancy events. However, current clinical screening methods for GDM still exhibit limitations in detection speed, sensitivity and convenience, making it difficult to meet the clinical demand for rapid early-pregnancy screening. To address this, we propose a novel strategy for early GDM diagnosis based on surface-enhanced Raman spectroscopy (SERS) combined with deep learning, aiming to achieve rapid and accurate early screening. Characteristic SERS spectra of serum were obtained using a substrate based on silver nanoparticles (Ag NPs). A fused PCA-CNN model integrating principal component analysis (PCA) for dimensionality reduction and a one-dimensional convolutional neural network (1D-CNN) for feature learning was developed. The PCA-CNN model effectively extracts potential biomarker features from serum SERS spectra, achieving a diagnostic accuracy of 93.7%, with sensitivity and specificity of 0.95 and 0.93, respectively. Moreover, the entire detection process can be completed within 30 min, requires about 2.5 μL of serum per sample, and involves minimal preprocessing, highlighting both efficiency and practicality. This study provides a novel method for early GDM screening that combines high diagnostic performance with clinical applicability, offering promising technical support for early intervention and clinical management of GDM.
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Food Analytical Methods
·
2026
·Vol. 19
·期 1
·69
·
IF 3
Lin Hui,Chen Zhenglong,Ren Chunfeng,You Ruiyun,Lu Yudong
The aging process has a significant impact on the quality and market value of white tea (WT), as described in a traditional proverb: “One year’s tea, three years’ medicinal value, and seven years becomes a treasure.” However, the lack of a rapid and reliable method for age identification poses challenges for market regulation. This study proposes a novel approach that combines surface-enhanced Raman spectroscopy (SERS) with machine learning (ML) to accurately identify the age of white tea. SERS is used to obtain molecular fingerprint spectra from Fujian white tea samples covering seven different years (2011−2023). Seven machine learning models, including logistic regression (LR), support vector machine (SVM), and random forest (RF), were systematically evaluated. The LR model, after being preprocessed through principal component analysis—logistic discriminant analysis, demonstrated relatively excellent performance. This SERS-ML method can perform rapid analysis and requires very little samples preparation. Our work establishes a robust, efficient, and field-deployable strategy for identifying the age of white tea, which is of great significance for combating fraud and protecting consumers. Graphical
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ADVANCED FUNCTIONAL MATERIALS
·
2026
·
IF 19.9
Xueqin Wang,Fajun Li,Jiaqing Shen,Shuya Yang,Zekai Wang,Yinong Xie,Kaibin Chen,Zhaogang Dong,Yahong Chen,Jinfeng Zhu
Surface-enhanced Raman spectroscopy (SERS) has achieved extensive applications through carefully engineered plasmonic metasurfaces. Disordered plasmonic metasurfaces are widely used for SERS applications due to their simple fabrication and low cost. However, their poor spatial uniformity usually causes severe signal fluctuations, degrading the repeatability and robustness of SERS measurements. We propose an order-in-disorder metasurface to address this challenge. The design relies on regulating the degree of disorder by introducing a controlled level of ordered features, facilitating the orderly manipulation of photonic Anderson localization. As a representative proof-of-concept example, we fabricate a flexible plasmonic nanoflower-like metasurface by a cost-effective process and each nanoflower contains randomly distributed nanosheets. Compared with the traditional disordered metasurface, it shows competitive SERS signal intensities while dramatically reducing the relative standard deviation (RSD). In detecting trace pesticide residues on fruit surfaces, this metasurface exhibits a low RSD. The enhanced robustness stems from the strong electromagnetic coupling between short-range disordered states of Anderson localization and long-range ordered plasmonic polaritons. The disordered states provide great near-field enhancement, while the ordered mode manipulates hotspot uniformity. This scheme supports the development of a low-cost, high-repeatability, versatile SERS metasurface, and will inspire more practical agriculture and supply chain applications.
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