MICROCHEMICAL JOURNAL
·
2026
·
IF 5.2
Minhang Zhu,Jiaxin Shi,Jing Huang,Fasheng Zhang,Yubo Gao,Shuai Chen,Shengrong Du,Zufang Huang
A suspended liquid membrane-assisted SERS biosensing platform was developed for ultrasensitive E. coli detection. • Dual-recognition “magnetic bead– E. coli –SERS tag” complexes enabled specific E. coli capture and signal amplification. • Under optimized conditions, a limit of detection of 10 CFU/mL was achieved. • The SERS platform enables favorable detection of E. coli in complex matrices. Sensitive detection of pathogenic bacteria with a shorter turnaround than conventional culture-based methods is essential for clinical diagnosis, public health monitoring, and food safety. Herein, we developed a magnetically enriched suspended liquid membrane (SLM)-assisted surface-enhanced Raman scattering (SERS) biosensing platform for the sensitive detection of Escherichia coli ( E. coli ). Wheat germ agglutinin (WGA)-functionalized carboxylated magnetic beads were used to capture and enrich bacteria, while aptamer-modified Au@4-mercaptobenzoic acid (MBA)@Ag core–shell nanoparticles served as SERS tags for specific recognition and signal amplification. By combining magnetic enrichment with the directional concentration effect of the SLM, the platform enabled favorable Raman detection with enhanced signal intensity and improved quantitative performance. Under optimized conditions, the platform enabled detection of E. coli at a lowest experimentally verified concentration of 10 colony-forming units per milliliter (CFU/mL), provided an 8–10-fold SERS signal enhancement compared with the non-enriched system, and exhibited a quantitative linear response over the range of 1 × 10 3 to 1 × 10 8 CFU/mL with an R 2 of 0.9634. The platform showed proof-of-concept feasibility for E. coli detection in pretreated and post-spiked complex matrices, including human seminal plasma (HSP), human urine and commercial fruit juice, with apparent recoveries ranging from 85.76% to119.94% for juice, 93.26% to 114.82% for human urine, and 79.38% to115.40% for HSP. Download: Download high-res image (173KB) Download: Download full-size image
✓ 已验证
DOI
高影响因子
SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY
·
2026
·
IF 4.8
Panpan Jiang,Nan Gao,Yuxiang Wu
A simple and low-cost three-phase interface self-assembly method was employed to fabricate a high-quality Ag NWs@CTAC array. • Creatinine was used as an internal standard, ensuring the accuracy of the detection • The positively charged CTAC layer enhanced selectivity by electrostatically adsorbing negatively charged uric acid. Rapid and reliable detection of uric acid is crucial for a prompt diagnosis of metabolic disorders. However, this remains challenging due to the intricacy of biological matrices and the limitations of current analytical methods. In this study, a surface-enhanced Raman scattering (SERS) sensor based on silver nanowire arrays functionalized with cetyltrimethylammonium chloride (Ag NWs@CTAC) was developed for the quantitative detection of uric acid in human urine. To ensure accuracy, creatinine, a metabolite excreted at a steady rate, was introduced as an internal standard to correct for signal fluctuations. The positively charged CTAC layer enhanced selectivity by electrostatically adsorbing negatively charged uric acid. Additionally, the uniformly arranged Ag NWs@CTAC array provided high density and repeatable SERS hotspots, ensuring both uniformity and signal enhancement. The sensor achieved a detection limit as low as 1.27 mg L −1 for uric acid, along with accurate recovery rates in artificial urine samples. These features make the Ag NWs@CTAC SERS sensor a promising candidate for quick, accurate, and reliable uric acid detection in clinical and point-of-care applications. Download: Download high-res image (118KB) Download: Download full-size image
✓ 已验证
DOI
高影响因子
Food Bioscience
·
2026
·
IF 6.2
Jiwei Song,Yunpeng Shao,Yuan Yao,Zicheng Zhang,Yaqin Ren,Linyu Mei
Surface-enhanced Raman scattering (SERS) has emerged as a powerful analytical technique for ultrasensitive and rapid detection. However, the performance of SERS substrates largely depends on their ability to generate intense localized electromagnetic fields at nanoscale hot spots, while simultaneously achieving high sensitivity and signal reproducibility remains a major challenge. In this work, a highly ordered SU-8 micropillar array was fabricated by Ultraviolet Lithography(UL), followed by biomimetic modification with polydopamine (PDA) to achieve the uniform immobilization of silver nanoflowers (AgNFs). The resulting three-dimensional (3D) AgNFs/PDA/SU-8 micropillar SERS substrate integrates the structural advantages of the 3D architecture, the abundant electromagnetic hot spots and physical enrichment effect generated by densely packed AgNFs, and the interfacial regulation provided by PDA, thereby synergistically enhancing the SERS performance. The substrate exhibited an enhancement factor(EF) of 2.16 × 10 9 for Rhodamine 6G (R6G), with an experimental limit of detection as low as 1 × 10 −14 M (S/N = 3) and a relative standard deviation below 8.30%. Furthermore, the limits of detection for enrofloxacin (ENR), clothianidin (CLO), and 100 and 800 nm polystyrene (PS) particles in seawater reached 1 × 10 −13 M, 1 × 10 −14 M, 2.5 μg mL −1 , and 0.6 mg mL −1 , respectively. Simultaneous detection of multiple contaminants was also successfully achieved in complex food matrices, including mineral water, milk, and liquor. This work provides an effective strategy for constructing high-performance 3D SERS substrates and demonstrates their promising potential for food safety monitoring and aquatic environmental analysis.
✓ 已验证
产品关联
DOI
高影响因子
TALANTA
·
2026
·
IF 6.7
Dan Huang,Ling Li,Renjie Li,Wenxi Wang,Wenli Chen,Xiandeng Hou,Li Wu
Tetracycline hydrochloride (TCH) is a widely used antibiotic in animal husbandry and aquaculture practices, and its residual accumulation in food commodities constitutes a persistent public health concern. Therefore, sensitive, time-saving, and reliable analytical strategies for TCH are critical for guaranteeing food safety. Surface-enhanced Raman spectroscopy (SERS) has become an ideal analytical modality, attributed to its ultrahigh sensitivity, operational simplicity, and inherent molecular fingerprinting capability. The integration of plasmonic metal materials (PMPs) with the functional material provides a rational design strategy for advanced SERS substrates. A case in point is that of metal-organic frameworks (MOFs). However, the construction of MOF-PMPs composites is hindered by complex fabrication protocols, prolonged preparation time and high cost. Electrochemical deposition, characterized by rapid processing, facile parameter tuning, and environmental compatibility, presents a promising alternative for SERS substrate construction. In this work, we present a novel Ag/Zn-BTC@NF composite SERS substrate synthesized via two-step electrodeposition. The as-fabricated material delivers prominent SERS sensitivity. When R6G is used as the specific molecule, LOD (detection limit) can reach 10 −9 M, and the enhancement factor is 6.6 × 10 6 . Furthermore, the substrate achieved an LOD of 10 −8 M for TCH and enabled its ingenious detection in milk and honey samples, with recoveries of 87.4 − 112.5%. The proposed methodology establishes an efficient and eco-friendly synthetic strategy for MOF-PMPs composites, thereby broadening the scope of SERS technology in food safety applications.
✓ 已验证
产品关联
DOI
PubMed
高影响因子
Small
·
2026
·
IF 11.8
Chaochao Ma,Xiaojiao Zhao,Jiacheng Li,Xinyu Liu,Mingxu Zhang,Zhidan Liu,Jing Wu,Zhifeng Liu,Yunpeng Wang,Yang Li
Accurate detection of trace-level microplastics in natural waters remains challenging due to inefficient particle enrichment, unstable particle-substrate interactions, inhomogeneous hotspot distributions, and spectral similarity among polymers. Here, we develop a membrane-confined SERS platform integrated with machine-learning-assisted spectral analysis for sensitive and reproducible microplastic detection. In this platform, PSS-Na-modified Au@Ag nanocubes are employed as plasmonic building blocks to construct a charge-regulated, vertically stacked hotspot architecture on a cellulose acetate membrane. By improving microplastic enrichment, hotspot accessibility, and interlayer electromagnetic coupling, the membrane-guided configuration enables reproducible detection of polystyrene down to 50 ng mL − 1 . It also delivers highly reproducible signals in microplastic spike samples prepared in six environmental water matrices without chemical pretreatment. To resolve spectral similarity among polymers, a multi-polymer SERS library covering PS, PMMA, PVC, and PC was integrated with an attention-based 1D-CNN, enabling reliable polymer identification and semi-quantitative analysis of binary and ternary mixtures (R 2 > 0.83). This membrane-confined plasmonic platform, together with data-driven spectral analysis, provides a robust route for intelligent microplastic monitoring in complex aquatic environments.
✓ 已验证
DOI
PubMed
高影响因子
MICROCHIMICA ACTA
·
2026
·Vol. 193
·期 6
·383
·
IF 5.4
Hu Rongjing,Fu Shilan,Zhang Jingwen,Zhang Jiadan,Fu Fengfu,Lin Zhenyu,Dong Yongqiang
A new method is developed to synthesize Au/Ag alloy nanoparticles (Au/Ag-NPs). The obtained Au/Ag-NPs are further dispersed in polyvinyl alcohol (PVA) solution to construct Au/Ag hydrogel surface enhanced Raman scattering (SERS) chips through a simple Ca 2+ -mediated aggregation of Au/Ag-NPs and a physical crosslinking of PVA. The formed aggregated Au/Ag-NPs (a-Au/Ag-NPs) show a strong localized surface plasmon resonance corresponding to the commonly used 532 nm laser. Therefore, under the action of 532 nm laser, the abundant nanogaps contained in the a-Au/Ag-NPs turn to high active electromagnetic “hot spots”, providing strong electromagnetic enhancement (EM). Moreover, Au/Ag-NPs provide a moderate Fermi level of -4.72 eV, which facilitates its photo-induced charge transfer with many molecules under 532 nm laser irradiation, resulting in the chemical enhancement (CM) effect. The synergistic effect of EM and CM leads to ultrahigh SERS activity. Additionally, the unique structure of hydrogel provides excellent signal uniformity and robust anti-interference ability. Thus, the obtained Au/Ag hydrogel SERS chips are ideal SERS platform for the quantitative determination of many molecules. For instance, a sensitive and reliable SERS method is established for the simultaneous detection of methyl parathion and triazophos in fruits and tea. Graphical The alternative text for this image may have been generated using AI.
✓ 已验证
DOI
PubMed
高影响因子
LANGMUIR
·
2026
·Vol. 42
·期 29
·21139-21154
·
IF 4.4
Mai Quan-Doan,Dang Thi Hanh Trang,Tran Van Tiep,Ngo Thi Loan,Ong Van Hoang,Cao Thi Hong,Nguyen Quang Hoa,Lich Le Van,Nguyen Cong Chi,Mai Xuan Trang,Phan Manh-Huong,Pham Anh-Tuan,Le Anh-Tuan
Surface-enhanced Raman spectroscopy (SERS) is a powerful
analytical
technique capable of providing rapid and noninvasive molecular fingerprint
identification through characteristic vibrational signatures. However,
developing SERS platforms that enable both ultrasensitive and highly
selective detection, particularly for molecules with intrinsically
weak Raman responses, remains a major challenge, yet it is highly
desirable for expanding the currently limited application scope of
SERS. Herein, we introduce a novel plasmonic porous multicore@shell
Ag@UiO-66 nanostructure that synergistically integrates the localized
surface plasmon resonances (LSPRs) of Ag nanoparticles (NPs) with
the selective molecular capture capability of the metal–organic
framework UiO-66. The hybrid structure is fabricated via a simple
solvent-induced self-assembly strategy, enabling in situ formation
of UiO-66 around dispersed AgNPs at room temperature. The resulting
architecture immobilizes multiple AgNP cores within a porous UiO-66
shell matrix, simultaneously stabilizing their plasmonic activity
and generating abundant interparticle hotspots. More importantly,
the UiO-66 shell provides molecular recognition toward salicylic acid
(SA─a bioactive aromatic compound) through synergistic adsorption
interactions involving bidentate coordination with Zr nodes, hydrogen
bonding, and π-π interactions. This cooperative plasmonic-molecular
recognition mechanism effectively concentrates target molecules at
electromagnetic hotspots, leading to significantly enhanced SERS signals.
As a result, the developed platform achieves highly selective and
ultrasensitive detection of SA with a detection limit down to 1 nM,
far surpassing that of traditional SERS substrates (∼1 μM).
Furthermore, machine learning-assisted spectral analysis confirms
the reliable and selective identification of SA in complex mixtures.
This work demonstrates an effective strategy for developing MOF-enabled
selective and ultrasensitive SERS platforms, opening new avenues for
advanced molecular sensing applications.
✓ 已验证
产品关联
DOI
PubMed
高影响因子
JOURNAL OF HAZARDOUS MATERIALS
·
2026
·
IF 10.6
Guanwen Su,Wenrui Yang,Guozhao Liu,Xiaolong Yang,Jiasong Zhang,Baoyang Wu,Leping Dang,Hongyuan Wei,Zhanzhong Wang
Accurate and rapid identification of pathogens is essential for public health and infectious disease control. Despite notable advances, label-free, high-throughput, and sensitive bacterial detection in complex samples remains a significant challenge. An interpretable multimodal deep learning model, RaSizeNet, was developed by integrating surface-enhanced Raman spectroscopy (SERS) and inertial microfluidic size distribution for high-precision identification of complex bacterial populations. Fe@PCN-Au hybrid particles exhibiting strong SERS activity and magnetic responsiveness were synthesized and embedded into a dual-zone microfluidic chip to enable efficient bacterial enrichment, size-based separation, and signal enhancement. A large-scale multimodal dataset comprising nine representative bacterial species was constructed by combining SERS spectra and outlet distribution data. A size-guided cross-modal attention mechanism was introduced to improve spectral discriminability and model robustness while maintaining independent modality representations. RaSizeNet achieved an average accuracy of 96.22 % in complex samples, significantly outperforming unimodal (SERSNet, 82.23 %) and conventional multimodal (MultiNet, 92.81 %) models. The model also demonstrated superior adaptability and stability under extreme concentrations, varying flow rates, and diverse media conditions. This work establishes a promising framework for integrating microfluidic-SERS sensing with artificial intelligence, providing both theoretical and technical support for real-time multimodal pathogen detection.
✓ 已验证
产品关联
DOI
PubMed
高影响因子
JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY
·
2026
·
IF 6.7
Huixia Di,Zhouhao Lei,Jianing Li,Xiaochun Li
The accurate detection of hazardous pesticide residues is crucial for public health. Surface-enhanced Raman scattering (SERS) holds potential but faces practical limitations, including spectral overlap and matrix interference. To address these limitations, we developed a convolutional neural network (CNN)-assisted SERS platform with a hybrid substrate comprising a silver nanostar (AgNS) and hydrophobic silver nanoisland film (AgNIF). This platform synergizes localized surface plasmon resonance with a local concentration effect to achieve high sensitivity, demonstrating a broad linear range and low detection limits for nine pesticides. Coupled with an optimal data preprocessing protocol, our CNN model achieved superior classification accuracy: 99.44% for single pesticides, 98.47% for binary mixtures, 98.09% for ternary mixtures, and 94.60% in spiked tomato samples. Therefore, this work demonstrates a label-free, sensitive and accurate tool for pesticide detection and identification, holding great promise for guiding pesticide application and ensuring food safety.
✓ 已验证
产品关联
DOI
PubMed
高影响因子
FOOD CHEMISTRY
·
2026
·
IF 10.4
Yan Sun,Lichun Yang,Leilei Wang,Yi Hou,Qin Luo,Xinglong Tao,Min Xie,Yongjun Hu
Accurate and rapid detection of glucose is of great significance for food safety and public health. In this study, we developed an Fe 3 O 4 @COF@Au(GOx) substrate based on magnetic COF materials. This substrate exhibits excellent adsorption capacity, enzymatic activity, and surface-enhanced Raman scattering (SERS) enhancement effect. This substrate can trigger a color reaction of 3,3′,5,5′-tetramethylbenzidine (TMB) and a change in the signal intensity of SERS when glucose is present. The ethanol Raman signal is used as the ratio signal to achieve the dual detection mode of colorimetric - ratiometric SERS. The dual-mode detection method boasts extremely high sensitivity and excellent selectivity with limits of detection for glucose of 0.17 μM in colorimetric and 10.7 nM in SERS. The dual-mode detection method effectively combines the simplicity of colorimetric detection with the high sensitivity of SERS, providing a highly promising solution for the detection of glucose in practical applications.
✓ 已验证
产品关联
DOI
PubMed
高影响因子
CHEMICAL ENGINEERING JOURNAL
·
2026
·
IF 12.5
Weiqing Xiong,Yu Yang,Lei Huang,Yue Zhang,Xiao Li
The accurate detection of trace pharmaceuticals and pesticides in aquatic environments remains a formidable analytical challenge, primarily due to ultralow contaminant concentrations, complex matrix interferences, and the structural similarity among many target analytes—factors that severely compromise selectivity and reliability in conventional sensing approaches. Surface-enhanced Raman scattering (SERS) offers a powerful alternative owing to its exceptional sensitivity and molecular fingerprinting capability. However, its practical deployment in real-world water samples has been impeded by insufficient selectivity and inadequate signal discrimination for structurally analogous compounds. To overcome these limitations, we present an innovative core-shell SERS substrate—gold superparticles encapsulated within a zeolitic imidazolate framework-8 (denoted GSPs@ZIF-8)—engineered to synergistically combine molecular preconcentration, matrix exclusion, and electromagnetic field enhancement. The porous ZIF-8 shell selectively enriches trace thiazole/imidazole-type analytes while excluding interfering species, thereby addressing the dual challenges of low abundance and matrix complexity. Meanwhile, the precisely controlled nanogaps between GSPs generate intense and uniform “hot spots”, delivering an enhancement factor of up to 3.01 × 10 8 and enabling a detection limit as low as 0.1 nM for 2-mercaptobenzothiazole (MBT). Critically, the GSPs@ZIF-8 platform achieves discrimination and simultaneous quantification of three highly similar compounds—MBT, 5-amino-2-mercaptobenzothiazole (AMT), and 2-mercaptobenzimidazole (MBI)—in mixed aqueous solutions. This work establishes a robust, selective, and ultrasensitive SERS strategy for monitoring structurally homologous contaminants in complex environmental matrices, paving the way toward practical on-site water quality assessment.
✓ 已验证
DOI
高影响因子
FOOD CHEMISTRY
·
2026
·
IF 10.4
Wenlong Liao,Juan Wu,Qian Long,Ying Zhu,Junling Shao,Yue Ma,Qiuzuo Shen,Huiming Li,Lingpu Jia,Kunping Liu
A MoS 2 @Ag nanoflower SERS substrate with 1T/2H mixed-phase is synthesized. • The prepared substrate achieving a high SERS enhancement factor of 1.76 × 10 8 . • The Transformer network achieved 99.82% classification accuracy for four antibiotics. Fluoroquinolone antibiotics (FQs) residues in dairy products pose a persistent challenge to food safety. This work establishes an integrated analytical platform combining a three-dimensional molybdenum disulfide‑silver nanoflower (MoS 2 @Ag NFs) SERS substrate with a transformer network based deep learning model for the sensitive detection and intelligent classification of FQs in milk. The prepared MoS 2 @Ag NFs substrate with a 1T/2H mixed-phase MoS 2 and a hierarchical structure densely decorated with silver nanoparticles (Ag NPs), exhibits a high enhancement factor and achieves nanomolar-level detection limits for four representative FQs. To overcome the discrimination challenge posed by the highly similar spectral signatures of different FQs, a transformer network is employed to automatically extract decisive spectral features via its self-attention mechanism, which results in an exceptional classification accuracy exceeding 99.82%. The integrated platform enables sensitive and intelligent monitoring of FQs in milk, providing a promising strategy for on-site detection of antibiotic residues in complex food matrices. Download: Download high-res image (125KB) Download: Download full-size image
✓ 已验证
DOI
高影响因子
危险品化学品经营许可证（带存储）