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

Area of Science:

• Analytical Chemistry
• Sensor Technology
• Materials Science

Background:

• Volatile organic compounds (VOCs) analysis is crucial for environmental monitoring and disease diagnosis.
• Existing electronic nose (e-nose) technologies face challenges in sensitivity, selectivity, and complex mixture analysis.
• Developing high-fidelity e-nose systems requires innovative sensing paradigms and data processing techniques.

Purpose of the Study:

• To introduce a radial-hierarchical, diffusion-enhanced spatiotemporal sensing paradigm for VOC analysis.
• To develop an integrated microchamber paper-based chromatomimetic e-nose with improved analytical capabilities.
• To establish a scalable blueprint for advanced VOC analytics using e-nose technology.

Main Methods:

• Utilized a radially symmetric electrode array and a hierarchical porous chemoresistive ink (CuP@G) for synergistic interlayer spatiotemporal dynamics and planar spatial variance.
• Leveraged molecular diffusion gradients across the sensing plane to create multidimensional "spatiotemporal fingerprints" for VOC discrimination.
• Integrated a physics-informed framework combining molecular transport principles with multitask learning convolutional neural network (MTL-CNN) analytics.

Main Results:

• Achieved unprecedented resolution in real-sample classification of VOCs.
• Demonstrated superior performance in discriminating diverse VOCs and binary mixtures.
• Attained high accuracy (92-99%) in classifying authentic tobacco samples by origin and level.

Conclusions:

• The developed radial-hierarchical, diffusion-enhanced spatiotemporal sensing paradigm offers high-fidelity VOC analytics.
• The integrated e-nose system effectively bridges gas diffusion physics with intelligent signal processing.
• This work provides a scalable blueprint for advancing e-nose technology toward precision-driven design and applications.