Nanomaterials
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2024
·Vol. 14
·期 8
·717
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IF 4.4
Pengxing Sha,Chushu Zhu,Tianran Wang,Peitao Dong,Xuezhong Wu
In this research, a method was developed for fabricating Au–Au nanorod array substrates through the deposition of large-area Au nanostructures on an Au nanorod array using a galvanic cell reaction. The incorporation of a granular structure enhanced both the number and intensity of surface-enhanced Raman scattering (SERS) hot spots on the substrate, thereby elevating the SERS performance beyond that of substrates composed solely of an Au nanorod. Calculations using the finite difference time domain method confirmed the generation of a strong electromagnetic field around the nanoparticles. Motivated by the electromotive force, Au ions in the chloroauric acid solution were reduced to form nanostructures on the nanorod array. The size and distribution density of these granular nanostructures could be modulated by varying the reaction time and the concentration of chloroauric acid. The resulting Au–Au nanorod array substrate exhibited an active, uniform, and reproducible SERS effect. With 1,2-bis(4-pyridyl)ethylene as the probe molecule, the detection sensitivity of the Au–Au nanorod array substrate was enhanced to 10−11M, improving by five orders of magnitude over the substrate consisting only of an Au nanorod array. For a practical application, this substrate was utilized for the detection of pesticides, including thiram, thiabendazole, carbendazim, and phosmet, within the concentration range of 10−4to 5 × 10−7M. An analytical model combining a random forest and a one-dimensional convolutional neural network, referring to the important variable-one-dimensional convolutional neural network model, was developed for the precise identification of thiram. This approach demonstrated significant potential for biochemical sensing and rapid on-site identification.
研究领域:
分析化学
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纳米技术
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机器学习
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环境科学
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Journal of Environmental Chemical Engineering
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2025
·
IF 7.2
Nian Liu,Tianlin Ren,Ziyan Cai,Wenjie Liu,Li He,Xichu Qiao,Xuhui Mao
Advanced Oxidation Processes (AOPs) are widely employed for the degradation of polycyclic aromatic hydrocarbons (PAHs). Determining the optimal oxidation method and corresponding process parameters is crucial for achieving the maximum degradation efficiency. Due to the complexity of AOPs, conventional kinetic models and machine learning approaches show limited accuracy in predicting the degradation kinetics of PAHs, making them inadequate in optimizing process parameters for engineering practices. This study pioneers the use of convolutional neural networks (CNN) for deep feature extraction from the gas chromatography-mass spectrometry (GC-MS) data of pyrene degradation. Meanwhile, the Fourier-based temporal embedding (FTE) method was adopted to map the discrete time into continuous frequency-domain features, preserving a temporal continuity across reaction trajectories. The fusion of CNN-extracted MS features, FTE, and reaction conditions within an eXtreme Gradient Boosting (XGBoost) framework yielded a novel CNN-FTE-XGBoost hybrid model, enabling a cross-scale modeling of degradation kinetics. The CNN-FTE-XGBoost architecture demonstrated exceptional prediction accuracy on pyrene degradation efficiency ( R ²=0.951, RMSE=0.046, MAE=0.039). SHapley Additive exPlanations (SHAP) analysis further identified the key factors influencing pyrene degradation efficiency, verifying the interpretability of model. This work establishes an interpretable multimodal fusion-driven modeling framework for a high-precision prediction of pollutant degradation in AOPs, providing a reliable tool for AI-guided optimization of AOPs in remedial applications.
研究领域:
环境工程
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环境科学中的机器学习
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化学降解动力学
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分析化学
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可持续性人工智能
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ACS Applied Materials & Interfaces
·
2024
·
IF 8.3
Siyue Xiong,Chengxuan Wang,Chushu Zhu,Peitao Dong,Xuezhong Wu
Sweat, a noninvasive metabolic product of normal physiological responses, offers valuable clinical insights into body conditions without causing harm. Key components in sweat, such as urea and glucose, are closely linked to kidney function and blood glucose levels. Portable sweat sensors, equipped with diverse sensing systems, can monitor fluctuations in urea and glucose concentrations, thus providing methods for assessing kidney function and monitoring diabetes. This study presents a flexible, portable microfluidic surface-enhanced Raman scattering (SERS) sensor designed to detect the unique fingerprint of target biomarkers. This flexible, self-adhesive microfluidic chip, constructed from modified polydimethylsiloxane, features silver nanotripods (AgNTs) with densely distributed “hotspots” created via the oblique angle deposition technique. These AgNTs act as active substrates for SERS within the microfluidic platform, enabling direct skin contact to collect, transport, store, and analyze sweat. The chip functions as a quantitative urea sensor with a limit of detection (LOD) of 10–7 M. For enhanced sensitivity for glucose detection, the SERS substrate is modified with 4-mercaptophenylboronic acid, achieving a LOD of 10–7 M. This satisfies the measurement requirements for both urea and glucose in sweat under physiological conditions. Furthermore, the one-dimensional convolutional neural network model significantly enhances the accuracy of biomarker detection, facilitating the quantitative analysis of urea and glucose. This advancement contributes to the development of a controlled, convenient, and dynamic biosensing system for personalized point-of-care testing and supports the creation of intelligent wearable and nondestructive devices.
研究领域:
生物医学工程
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分析化学
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传感器技术
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PubMed
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ANALYTICAL AND BIOANALYTICAL CHEMISTRY
·
2023
·Vol. 415
·期 29
·7127-7138
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IF 4.3
Liu Shuai,Zhao Jingkai,Wu Junfeng,Wang Ling,Hu Jiandong,Li Shixin,Zhang Hao
A novel deep learning-enabled smartphone platform is developed to assist a colorimetric aptamer biosensor for fast and highly sensitive detection of dimethoate. The colorimetric determination of dimethoate is based on the specific binding of dimethoate and aptamer, which leads to the aggregation of AuNPs in high-concentration NaCl solution, resulting in an obvious color change from red to blue. This color change provides sufficient data for self-learning enabled by a convolutional neural network (CNN) model, which is established to predict dimethoate concentration based on images acquired from a smartphone. To enhance user-friendliness for non-experts, the CNN model is then embedded into a smartphone app, enabling offline detection of dimethoate pesticide in real environments within just 15 min using a pre-configured colorimetric probe. The developed platform exhibits superior performance, achieving a regression coefficient of 0.9992 in the concentration range of 0–10 μM. Moreover, the app’s performance is found to be consistent with the ELISA kit. These remarkable findings demonstrate the potential of combining colorimetric biosensors with smartphone-based deep learning methods for the development of portable and affordable tools for pesticide detection. Graphical Abstract
研究领域:
分析化学
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环境科学
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人工智能
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生物技术
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PubMed
高影响因子
MICROCHEMICAL JOURNAL
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2026
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IF 5.2
Kai Hu,Wuliji Hasi,Guoqiang Fang,Siqingaowa Han
Cinnabar, with low water solubility and poor oral absorption, poses challenges in toxicity assessment. Relying solely on total mercury leads to overestimation, making detection of soluble Hg 2+ essential. This study presents a surface-enhanced Raman scattering (SERS) strategy using Au concave nanocubes (Au CNC) modified with 3-mercaptopropionic acid (3-MPA) and 4-mercaptopyridine (4-MPY), combined with a data-augmented convolutional neural network-partial least squares regression (CNN-PLS) model, for quantifying soluble Hg 2+ in cinnabar and its preparations. 3-MPA binds Hg 2+ , inducing Au CNC aggregation and enhances the SERS signal, while 4-MPY improves sensitivity. To overcome insufficient data in practical, data augmentation was used to expand the SERS dataset, and the performance of CNN, PLS, and CNN-PLS models was compared before and after augmentation. The results indicate that the data-augmented CNN-PLS model performed best, with a coefficient of determination (R 2 ) of 0.999 in the range of 0.001–1 μg/mL and a Mean Squared Error of Prediction (MSEP) of 0.0001. This method was applied to quantify Hg 2+ leaching from cinnabar and four cinnabar-containing preparations after 12 h of immersion in artificial gastric juice. The mean Hg 2+ concentrations from three cinnabar batches were 0.691, 0.557, and 0.546 μg/mL, while for the preparations, the concentrations were 0.085, 0.047, 0.038, and 0.023 μg/mL. These results showed excellent agreement with atomic fluorescence spectrometry (AFS), confirming the method's reliability and accuracy. This simple, accurate, and safe approach offers a novel solution for mercury safety assessment in cinnabar and its preparations, with significant potential for traditional Chinese medicine safety evaluation and environmental monitoring.
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CHEMICAL ENGINEERING JOURNAL
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2025
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IF 13.2
Jialin Li,Shaowei Liu,Amil Aligayev,Hao Jiang,Muhammad,Shenhao Wang,Xin Yu,Jin Tao,Jiaqi Wang,Agnieszka Jastrzębska,Qing Huang
Hexanal, a volatile organic compound (VOC), is a potential biomarker for the early detection of lung cancer. In this study, we developed a dual-mode flexible biosensor that integrates surface-enhanced Raman scattering (SERS) and colorimetric detection for the quantitative analysis of hexanal in human exhaled breath. The biosensor employs Ag nanocubes wrapped with Co-Ni layered double hydroxide (AgNCs@Co-Ni LDH) as a functional matrix, offering both superior SERS enhancement and efficient VOC adsorption properties. To achieve selective detection, AgNCs@Co-Ni LDH was incorporated into agarose hydrogels along with 3-methyl-2-benzothiazolinone hydrazone (MBTH). The resulting MBTH-AgNCs@Co-Ni LDH/hydrogel patches facilitate the oxidation of hexanal, enabling simultaneous colorimetric and SERS signal generation, while producing acrizine, a blue-colored reaction product. This hydrogel-based dual-mode sensing platform exhibits high selectivity, excellent stability, and precision in SERS-based hexanal detection. The detection limit for the SERS method was determined to be as low as 3.34 × 10 −13 M. Furthermore, developed and optimized compact CNN-based multi-terminal intelligent recognition system for enhanced hydrogel-patch detection through AI-driven, portable, and real-time colorimetric analysis. Therefore, this work not only enables the effective detection of hexanal in the exhaled breath of suspected lung cancer patients, underscoring its potential for early lung cancer screening, but also establishes a foundation for the development of multimodal hydrogel biosensors for broader applications in disease diagnosis.
研究领域:
生物传感器
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纳米技术
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癌症诊断
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分析化学
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生物医学工程
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医疗人工智能
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呼吸系统疾病筛查
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高影响因子
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