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WQ
Verified CAS / Academic Author2 Decoded Studies

Prof. WANG Qiao

State Key Laboratory of Advanced Technology for Materials Synthesis and Processing, Wuhan University of Technology

Research Publications & English Decoded Briefs

Showing 2 publications
SCIENCE CHINA Materials2026DOI: 10.1007/s40843-025-3792-6

Multifunctional Permeable Electrodes for Synchronous Temperature-Electrophysiological Signals Monitoring and Intelligent Arrhythmia Diagnosis

The rapid expansion of home-based digital health monitoring necessitates electrodes capable of simultaneous, accurate acquisition of electrophysiological signals and body temperature. Conventional single-function electrodes, including metal block, gel, and Ag/AgCl types, suffer from limitations such as restricted movement, skin irritation, signal degradation over time, and poor permeability for prolonged use. To overcome these challenges, we developed a low-cost, multifunctional flexible electrode enabling concurrent body temperature and electrophysiological signal monitoring without cross-interference. Body temperature is assessed via visual colorimetric evaluation and precisely measured using a custom smartphone application. The electrode features high air permeability, ultra-thin architecture, superior flexibility, antibacterial properties, and strong skin adhesion, while maintaining low interfacial impedance for stable, long-term acquisition of high-fidelity signals such as electrocardiography (ECG) and surface electromyography (sEMG). Integrated with a Raspberry Pi platform and a hybrid convolutional neural network-long short-term memory (CNN-LSTM) algorithm, the system achieves intelligent arrhythmia detection with 99.30% accuracy. This novel electrode provides a powerful tool for multifunctional sensing of temperature and physiological electrical signals, with significant potential for wearable physiological tracking applications.

SCIENCE CHINA Materials2025DOI: 10.1007/s40843-025-3507-9

Machine Learning and High-Throughput Computation-Assisted Precise Synthesis of Quantum Dots for Reliable Neuromorphic Computing

Quantum dot (QD)-based memristors enable precise and energy-efficient neuromorphic computing through atomic-level control over electrical synapse performance. However, the stochastic nature of QD structures results in poor reliability of resistive switching, limiting practical applications. This work presents a data-driven QD synthesis optimization loop that integrates high-throughput density functional theory with machine learning to establish a cross-scale screening platform for precise QD synthesis. By minimizing structural disorder through pure phase, uniform size distribution, and highly preferred orientation, QD-based memristors demonstrate a 57% reduction in switching voltage, a two-order-of-magnitude increase in ON/OFF ratio, and endurance and retention degradation as low as 0.1% over 8.4 × 10^7 s of continuous operation and 10^5 rapid read cycles. The dynamic learning range and neuromorphic computing accuracy improve by 477% and 27.8% (reaching 92.23%), respectively. These findings establish a scalable, data-driven strategy for rational design of QD-based memristors, advancing next-generation reliable neuromorphic computing systems.