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Verified CAS / Academic Author2 Decoded Studies

Prof. DAI Wei

Shenzhen University

Research Publications & English Decoded Briefs

Showing 2 publications
SCIENCE CHINA Materials•2026•DOI: 10.1007/s40843-025-3856-0

Photoelectrochemical Upgrading of Biomass-Derived Compounds over Hematite Nanorods Decorated with Bimetallic Zeolitic Imidazolate Frameworks

Replacing the kinetically sluggish oxygen evolution reaction (OER) with biomass oxidation at photoanodes offers a cost-effective and energy-efficient route for simultaneous hydrogen production and value-added chemical synthesis in a photoelectrochemical (PEC) cell. Here, titanium-doped hematite nanorods (Hem) decorated with CoNi bimetallic zeolitic imidazolate frameworks (ZIF) were prepared via room-temperature deposition and employed as photoanodes for 5-hydroxymethylfurfural (HMF) oxidation. Using 2,2,6,6-tetramethylpiperidine-1-oxyl (TEMPO) as a redox mediator in alkaline electrolyte, the CoNi-ZIF/Hem photoanode achieved a photocurrent density of 1.09 mA cm−2 at a low bias of 1.1 V vs. reversible hydrogen electrode (RHE). Experimental results and theoretical calculations reveal that CoNi-ZIF accelerates charge transfer and separation, and enhances TEMPO adsorption on the surface, benefiting PEC TEMPO-mediated HMF oxidation to 2,5-furandicarboxylic acid (FDCA). In a flow-cell reactor under 1 sun illumination, the photoanode achieved ~99% HMF conversion and ~98% FDCA yield within 2 hours. The photoanode also exhibited excellent performance for TEMPO-mediated oxidation of various aldehyde-containing biomass-derived compounds. This work demonstrates a rational design of hematite-based photoanodes for efficient biomass valorization coupled with hydrogen production.

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SCIENCE CHINA Materials•2025•DOI: 10.1007/s40843-025-3753-y

Breathable all-textile pressure sensor with conductivity-modulable polypyrrole for deep learning-enhanced sensing

Deep learning-enhanced pressure sensors that integrate signal processing with sensing capabilities offer transformative potential for wearable electronics. However, current implementations predominantly rely on petroleum-based polymers for sensing/encapsulating layers and metallic electrodes, resulting in limited biodegradability, poor biocompatibility, and insufficient breathability. This work presents an all-textile pressure sensor that combines conductivity-modulable polypyrrole (PPy) textiles for both electrode and sensing layers with real-time artificial intelligence algorithms. Eliminating metallic electrodes and petroleum-based polymers yields a device with excellent biocompatibility, biodegradability, and breathability. The textile sensing layer's structure ensures pressure-induced conductivity, contributing to high sensitivity and a wide detection range. The integrated deep learning model, a one-dimensional convolutional neural network (1D-CNN), achieves 99.6% classification accuracy on human motion datasets after 16 training epochs. Under Gaussian noise with standard deviations of 150 and 200, accuracy remains at 97.3% and 93.8%, respectively. Spraying 0.1 mL water on sensor surfaces yields 98.6% accuracy, demonstrating robustness to environmental disturbances. The system enables health monitoring, software/hardware control, and complex human motion analysis. These results confirm that the deep learning-enhanced fabric sensor can achieve accurate real-time human motion recognition, showing potential for immersive motion capture and intelligent feedback systems. This work provides a sustainable, breathable, and biocompatible platform for next-generation smart textiles.

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