SinoTechIntel |SinoBioData |SinoGreenTech |SinoAILab
SinoGreenTech Academic Portal
Open Access Archive
SinoGreenTech
Clean Energy & Battery Portal
Search Database
ZX
Verified CAS / Academic Author2 Decoded Studies

Prof. ZHANG Xianghong

College of Materials Science and Engineering, Fuzhou University

Research Publications & English Decoded Briefs

Showing 2 publications
SCIENCE CHINA Materials•2026•DOI: 10.1007/s40843-025-3582-9

Dual-functional light adaptation in perovskite quantum dot synaptic devices for smart blue-light protection

Perovskite quantum dots (PQDs) hold great potential for brain-like neuromorphic computing. However, the development of PQDs-based synaptic devices is hindered by interfacial defects and limited stability. Here, we demonstrate a high-performance Cs2AgBiBr6 QDs/organic single crystal heterojunction synaptic device, fabricated via a novel space-confined vertical growth technique combined with a polymer-free transfer process. Vertically grown organic single crystals enable superior carrier mobility and facilitate the formation of low-defect interfaces with PQDs. The heterojunction exhibits remarkable photosensitivity (7.22 × 10^5 at 425 nm) and detectivity (2.15 × 10^15 Jones), owing to the strong optical absorption of PQDs coupled with the superior charge transport characteristics of organic single crystals. Notably, the device achieves dual-functional light adaptation, emulating synaptic behaviour under blue light while exhibiting photo-switching under green/red light. This unique capability enables smart blue-light hazard protection. This work not only provides a versatile platform for high-performance PQDs-based synaptic devices but also advances the development of brain-inspired neuromorphic systems for next-generation computing and intelligent sensing.

Read Full Analysis→Download Authentic PDF
SCIENCE CHINA Materials•2025•DOI: 10.1007/s40843-025-3524-y

Neuromorphic Parallel Computing Hardware Based on Quantum Dots for 12-Lead Electrocardiogram Monitoring

The 12-lead electrocardiogram (ECG) is indispensable for the initial diagnosis of cardiac conditions, yet existing neuromorphic hardware for multi-lead ECG monitoring requires multiple array circuits and two operational processes, imposing severe constraints on device consistency and diagnostic accuracy. This study introduces a neuromorphic parallel computing hardware architecture based on quantum dot synaptic transistors that leverages trap and surface electric field effects to enable 12-lead ECG monitoring within a single array circuit, eliminating the need for twelve separate circuits. The system concurrently processes multiple ECG signals and produces final outputs without external computing or control circuits. A 12-transistor array, termed STAC, directly processes one-dimensional ECG data without additional conversion circuits, integrating a feature extraction layer at the pixel level and a feature fusion layer at the circuit level. Classification of ECG signals from the MIT-BIH Arrhythmia Database and the Chinese Twelve-Lead ECG Challenge Database yields a training accuracy exceeding 98%. A five-class ECG signal classification task achieves 96.2% recognition accuracy, with a 5×5 confusion matrix confirming high classification precision across normal (N) and four abnormal categories (A, V, L, R). The architecture accurately detects myocardial infarction by fine-tuning internal weights, demonstrating proficiency in monitoring abnormal ECG signals. This advancement offers a compact, low-cost solution for wearable and portable 12-lead ECG monitoring devices, enabling real-time cardiac assessment with reduced hardware complexity and enhanced diagnostic reliability.

Read Full Analysis→Download Authentic PDF