文献类型：期刊文献

英文题名：Radial-Hierarchical Chromatomimetic E-Nose for Spatiotemporal VOC Diffusion Mapping

作者：Zhai, Xingchun[1,6];Li, Junjie[2];Cheng, Weiwei[3];Li, Xiaolu[4,5];Zhang, Yongheng[1,6];Cao, Bingxue[1];Wen, Junjie[1];Zhu, Ninghui[1];Wu, Da[2];Wang, Tao[7];Xuan, Fuzhen[7];Shi, Guoyue[1];Zhang, Min[1,6]

机构：[1]East China Normal Univ, Sch Chem & Mol Engn, Shanghai 200241, Peoples R China;[2]Shanghai Tobacco Grp Co LTD, Key Lab Cigarette Smoke Tobacco Ind, Shanghai 201315, Peoples R China;[3]Shanghai Univ Engn Sci, Sch Mat Sci & Engn, Shanghai 201620, Peoples R China;[4]East China Normal Univ, Sch Math Sci, Key Lab Math & Engn Applicat, MOE, Shanghai 200241, Peoples R China;[5]East China Normal Univ, Shanghai Key Lab Pure Math & Math Practice, Shanghai 200241, Peoples R China;[6]East China Normal Univ, Chongqing Key Lab Precis Opt, Chongqing Inst, Chongqing 401120, Peoples R China;[7]East China Univ Sci & Technol, Sch Mech & Power Engn, Shanghai Key Lab Intelligent Sensing & Detect Tech, Shanghai 200237, Peoples R China

年份：2025

卷号：97

期号：35

起止页码：19380

外文期刊名：ANALYTICAL CHEMISTRY

收录：;EI(收录号：20253719136718);WOS:【SCI-EXPANDED(收录号:WOS:001560948200001)】；

基金：This work was supported by the National Natural Science Foundation of China (22274053, 22274051), Major Scientific and Technological Project of China National Tobacco Corporation (110202201033(XJ-04)), the Science and Technology Commission of Shanghai Municipality (24140711700), and Natural Science Foundation of Chongqing (CSTB2023NSCQ-MSX0339).

语种：英文

外文关键词：Binary mixtures - Convolutional neural networks - Diffusion in gases - Diffusion in liquids - Electronic nose - Learning algorithms - Learning systems - Machine learning - Signal processing

摘要：This study introduces a radial-hierarchical, diffusion-enhanced spatiotemporal sensing paradigm for volatile organic compound (VOC) analysis via an integrated microchamber paper-based chromatomimetic e-nose. The proposed system synergizes interlayer spatiotemporal dynamics with planar spatial variance by employing a radially symmetric electrode array and a hierarchical porous chemoresistive ink (CuP@G). This design leverages molecular diffusion gradients across the sensing plane, enabling precise discrimination of complex VOC mixtures through multidimensional "spatiotemporal fingerprints". A physics-informed framework integrates molecular transport principles with multitask learning convolutional neural network (MTL-CNN) analytics, achieving unprecedented resolution in real-sample classification. Systematic validation demonstrates superior performance in discriminating diverse VOCs, binary mixtures, and authentic tobacco samples (origin and level classification accuracy: 92-99%). This work establishes a scalable blueprint for high-fidelity VOC analytics, bridging gas diffusion physics with intelligent signal processing to advance e-nose technology toward precision-driven design.