Radial-Hierarchical Chromatomimetic E-Nose for Spatiotemporal VOC Diffusion Mapping.

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.