SENSORS
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2018
·Vol. 18
·期 10
·3358
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IF 2.475
Xuemei Wu,Xiaoqing Bai,Yang Ma,Jie Wei,Juan Peng,Keren Shi,Huiqin Yao
In this work, binary hydrogel films based on carboxylated multi-walled carbon nanotubes/poly(N,N-diethylacrylamide) (c-MWCNTs/PDEA) were successfully polymerized and assembled on a glassy carbon (GC) electrode surface. The electroactive drug probes matrine and sophoridine in solution showed reversible thermal-, salt-, methanol- and pH-responsive switchable cyclic voltammetric (CV) behaviors at the film electrodes. The control experiments showed that the pH-responsive property of the system could be ascribed to the drug components of the solutions, whereas the thermal-, salt- and methanol-sensitive behaviors were attributed to the PDEA constituent of the films. The CV signals particularly, of matrine and sophoridine were significantly amplified by the electrocatalysis of c-MWCNTs in the films at 1.02 V and 0.91 V, respectively. Moreover, the addition of esterase, urease, ethyl butyrate, and urea to the solution also changed the pH of the system, and produced similar CV peaks as with dilution by HCl or NaOH. Based on these experiments, a 6-input/5-output logic gate system and 2-to-1 encoder were successfully constructed. The present system may lead to the development of novel types of molecular computing systems.Keywords:electrocatalysis;encoder;logic gate;matrine;sophoridine
研究领域:
材料科学
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生物医学工程
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DOI
PubMed
高影响因子
FOOD CHEMISTRY
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2025
·
IF 9.8
Fujia Dong,Zhaoyang Ma,Ying Xu,Yingjie Feng,Yingkun Shi,Hui Li,Fukang Xing,Guangxian Wang,Zhongxiong Zhang,Weiguo Yi,Songlei Wang
Veterinary drug residues in meat seriously harm human health. Rapid and accurate detection of veterinary drug residues is necessary to minimize contamination. Taking ofloxacin (OFX) residues in mutton as an example, the near-infrared hyperspectral imaging combined with explainable AI was used to evaluate the importance of feature wavelengths in the convolutional neural network-stacked sparse auto-encoder (CNN-SSAE) model for chemical properties. Based on this, the qualitative (residue identification-residue level identification) and quantitative detection of OFX residues in mutton was realized. The results showed that the accuracy of CNN-SSAE in identifying residue and residue level of OFX was 100% and 93.65%, respectively, and the correlation coefficients for validation (R 2 P ) in quantitative detection of OFX residue was 0.8980. In addition, SHapley Additive exPlanation (SHAP) values were used to identify feature wavelengths that contribute the most in the CNN-SSAE model, which effectively explained the quality attribute information that spectral and chemical values may improve the predicted results in the model decision process. The reliability of the CNN-SSAE model was evaluated by statistical validation methods (F-test and T-test). Finally, the visualization diagram of OFX content distribution was established. This study provides a method reference for explainability detection of veterinary drug residues.
研究领域:
食品科学
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人工智能
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生物医学
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DOI
PubMed
高影响因子
SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY
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2023
·
IF 4.4
Shiwen Li,Tian Li,Yaoyi Cai,Zekai Yao,Miaolei He
Rice flour is a raw material for various foods and is used as a substitute for wheat flour. However, some merchants adulterate rice flour with the illegal additive Rongalite to extend the shelf life and earn illegal profits. Rongalite is highly carcinogenic, and ingestion of more than 10 g can even cause death. high-performance liquid chromatography (HPLC) and mass spectrometry (MS) are currently the main methods for detecting food adulteration, however, the existing methods have many limitations, complex operation, expensive instrumentation, etc. Raman spectroscopy has the advantages of convenience and non-destructive samples, but Raman spectroscopy can be affected by interference such as fluorescence background that affects detection, in addition to the problem of difficult quantitative analysis due to nonlinear bias. In this article, we used the preprocessing method of Savitzky-Golay smoothing filtering and VTPspline to improve the quality of the spectra and proposed the SARNet, which combines autoencoder and residual network to achieve the quantitative analysis of Rongalite content in rice flour. The new model combines a linear model with a nonlinear model, which can solve the nonlinear problem effectively. Experiments showed that the new SARNet model achieved state-of-the-art results, achieving the best R 2 of 0.9703 and RMSEP of 0.0075. The lowest Rongalite concentration detected by the portable Raman spectrometer was 0.49%. In summary, the proposed method using portable Raman spectroscopy combined with machine learning has low detection bias and high accuracy, which can realize quantitative analyses of adulterated Rongalite in rice flour quickly. The method provides an accurate and nondestructive analytical tool in the field of food detection.
研究领域:
食品安全
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光谱学
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机器学习
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DOI
PubMed
高影响因子
FOOD CONTROL
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2024
·
IF 5.6
Jihong Deng,Zhenyu Chen,Hui Jiang,Quansheng Chen
The quality of edible oils is closely related to their chemical compositions. Antioxidants have widespread application in edible oil production. In this study, a pioneering detection approach involving the use of a one-dimensional convolutional autoencoder (1D-CAE) was introduced to compress spectral data for assessing antioxidant levels in edible oils. Fourier-transform near-infrared (FT-NIR) characterisation of edible oil samples with varying antioxidant concentrations was also conducted. An 1D-CAE model was developed to compress different pre-processed spectra into a condensed representation. These compressed features were then integrated with a support vector machine and partial least squares regression models to establish correlations for each target. The study examined the influence of pre-processing steps and feature engineering methods on near-infrared spectral analysis through independent or combined model analysis. The findings revealed that features derived from the 1D-CAE model demonstrated remarkable repeatability and can be utilised to construct robust detection models. The experimental results showed that the optimal detection model derived based on the 1D-CAE compression features has an average R 2 , RPD and RMSE of 0.9953, 15.1664 and 1.2035, respectively, on the prediction set. FT-NIR spectroscopy can be used to accurately detect butylated hydroxytoluene in edible oils. Therefore, autoencoders are an effective tool in spectroscopic analysis, offering promising avenues for future research and application.
研究领域:
食品科学
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分析化学
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DOI
高影响因子
ACS Applied Materials & Interfaces
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2023
·
IF 9.5
Zichen Ren,Gaoping Xu,Bo Wang,Shanshan Song,Tingting Hao,Dongqi Liu,Yike Zhang,Jiupeng Zhao,Leipeng Zhang,Yao Li
A high-level infrared dynamic patterned encoder (IR-DPE) possesses prospective applications for energy-harvesting and information, but a simple and reliable method for fabrication remains challenging. Herein, we first report an IR-DPE with multiple thermal radiation characteristics based on polyaniline (PANI). Specifically, the electron-beam evaporation technique is introduced to obtain the divanadium pentoxide (V2O5) coating, and then the V2O5 film acts as an oxidant to drive in situ polymerization of the PANI film. During the process, we experimentally explore the relationship between the thickness of V2O5 and the emissivity of PANI to obtain up to six emissivity levels and achieve the IR pattern integrated into multiple thermal radiation characteristics. The device shows multiple thermal radiation characteristics at the oxidized state, realizing a pattern visible with the IR camera and the same thermal radiation properties at the reduced state, leading to the pattern concealed in the IR regime. In addition, the highest emissivity tunability of the device is to be tuned from 0.40 to 0.82 (Δε = 0.42) at 2.5–25 μm. Meanwhile, the device exhibits a maximum temperature control of up to 5.9 °C. The results show the enormous potential of IR-DPEs for IR information transfer and thermal management.
研究领域:
材料科学
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信息科学
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能源技术
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DOI
PubMed
高影响因子
Advanced Optical Materials
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2025
·
IF 7.2
Yun Liu,Junwen Yu,Quanwang Niu,Xiangfu Wang
Optical information encryption is an effective security strategy currently used to protect data and prevent information leakage. However, the optical information encryption technology based on mechanoluminescent (ML) materials faces challenges of the single encryption mode and the lack of systematic dual-key decryption strategies. In this study, a three-layer structure optical encoder is proposed using porous ML material as the luminescent layer, a quartz transparent tube with pressure-sensing capabilities as the stress-loading layer, and photodetector as the optical recognition layer. A dual-key encryption system based on the evolution mechanism of the Ba-Gua is constructed. The encoding process of the optical encoder is completed within a micro darkroom fabricated using 3D printing technology. This system incorporates virtual position encoding in conjunction with trigram orientation, requiring two keys for correct decryption: the “Combination Relative Optical Intensity Ratio” and the “Virtual Position Space Code”. By linking the ML emission spectrum with the trigram information, a dynamic, rewritable encryption system for hybrid encoding of digital and Chinese character based on Ba-Gua evolution is established. The proposed optical encoder and dual-key encryption system offer new directions for concealing confidential information and multidimensional encryption applications.
研究领域:
光学
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信息安全
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材料科学
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DOI
高影响因子
ANALYTICAL CHEMISTRY
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2023
·
IF 7.4
Guangpei Qi,Haixia Zou,Xiaohong Peng,Shiliang He,Qiqi Zhang,Wei Ye,Yizhou Jiang,Wentao Wang,Guangli Ren,Xiangmeng Qu
Metabolic footprinting as a convenient and non-invasive cell metabolomics strategy relies on monitoring the whole extracellular metabolic process. It covers nutrient consumption and metabolite secretion of in vitro cell culture, which is hindered by low universality owing to pre-treatment of the cell medium and special equipment. Here, we report the design and a variety of applicability, for quantifying extracellular metabolism, of fluorescently labeled single-stranded DNA (ssDNA)-AuNP encoders, whose multi-modal signal response is triggered by extracellular metabolites. We constructed metabolic response profiling of cells by detecting extracellular metabolites in different tumor cells and drug-induced extracellular metabolites. We further assessed the extracellular metabolism differences using a machine learning algorithm. This metabolic response profiling based on the DNA-AuNP encoder strategy is a powerful complement to metabolic footprinting, which significantly applies potential non-invasive identification of tumor cell heterogeneity.
研究领域:
细胞生物学
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纳米技术
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生物化学
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癌症研究
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机器学习
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DOI
PubMed
高影响因子
Science Bulletin
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2025
·
IF 21.1
Bin Cao,Yin Qin,Yan Luo,Zhehan Ying,Zilin Yan,Lu-Tao Weng,Kaikai Li,Tong-Yi Zhang
Oxygen evolution reaction (OER) catalysts face a major challenge in the practical implementation of acidic water electrolysis for hydrogen production, primarily due to limitations in catalytic activity and stability. Despite extensive research, the development of acidic OER catalysts still relies largely on trial-and-error experimentation rather than AI-driven, target-oriented approaches. In this work, we address these limitations by introducing a spatial-adaptive active learning strategy integrated with closed-loop experimentation for targeted catalyst optimization in two stages. In the first stage, Bayesian optimization identifies highly active catalysts and a conditional variational autoencoder generates an adaptive low-overpotential subspace of stability candidates, while the second stage active learning finds the most stable catalyst within this subspace. Using this strategy, we discover a novel Cu-RuO 2 catalyst that exhibits remarkable stability for 625 h and an overpotential of 177 mV at a current density of 10 mA cm −2 . We provide detailed characterization and mechanistic insights into the newly discovered catalyst. Our study presents a transformative method for accelerating the design of stable acidic OER catalysts, thereby advancing the feasibility of large-scale green hydrogen production via acidic water electrolysis.
研究领域:
材料科学
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电催化
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化学中的人工智能
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可再生能源
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计算材料设计
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产品关联
DOI
PubMed
高影响因子
Molecular Catalysis
·
2023
·
IF 4.6
Zhenhua Zhai,Xiaoyan Shan,Mingming Li,Ying Liu,Feng Yang,Fuke Wang,Zhiqiang Tian,Keren Shi,Huiqin Yao
In this work, poly(N',N-diethylacrylamide-co-4-vinylphenylboronic acid) (P(DEA-co-VPBA)) hydrogel films were successfully assembled on the pyrolytic graphite (PG) electrode by a simple radical polymerization. Reversible thermal, glucose and pH-triggered electrochemical reactions were obtained in the presence of 1,1′-ferrocene dicarboxylic acid (FDA) with electro-redox properties. The thermal response performances of the thin-film electrode were attributed to the PDEA component, the sensitive behaviors of glucose were ascribed to the VPBA component, and the sensitive behaviors of pH were affected by PDEA and VPBA. The electrocatalytic oxidation of l -cysteine by FDA on the hydrogel film electrodes significantly enhanced the difference between the on and off states of the multi-response cyclic voltammetry (CV) signals. This smart polymer hydrogel film electrode with amplified signals could be further used to realize a 4-input/9-output binary logic circuit, which was constructed with temperature, glucose, pH, and cysteine as inputs and different levels of I pa and I pc responses as outputs. Furthermore, a 2-to-4 decoder, a 4-to-2 encoder, and a 4-input keypad lock were built on the same platform. This polymer electrocatalysis system provides a novel idea for constructing complex biological computing systems.
研究领域:
材料科学
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电化学
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生物医学工程
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纳米技术
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计算生物学
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产品关联
DOI
高影响因子
危险品化学品经营许可证（带存储）