SinoGreenTech Academic Portal
ZY
Verified CAS / Academic Author2 Decoded Studies

Prof. Zuqing Yuan

Not specified in the provided text

Co-Affiliations:Not specified in text

Research Publications & English Decoded Briefs

Showing 2 publications
SCIENCE CHINA Materials2026DOI: 10.1007/s40843-025-3958-8

Catching an Optical Photograph via a Focus-Tunable Real-Time Imaging System

Visual systems are the primary interface for humans to perceive the external environment. Mimicking the human eye, which integrates adjustable lenses with a curved retina, bio-inspired curved image sensors effectively mitigate field curvature and vignetting. To realize focus-tunable imaging, sensors must possess dynamic curvature while maintaining high sensitivity and mechanical stability. However, transitioning from rigid architectures to flexible devices often results in poor surface conformity through simple bending. Flexible sensors have explored intrinsic and structural designs for better flexibility and less stress concentration. Recent advances suggest that ultrathin devices with mesh-inspired designs offer a superior strategy, achieving seamless alignment with the curved surface without compromising optoelectronic performance. He et al. have developed a focus-tunable real-time curved imaging system inspired by the human visual system, based on an ultrathin perovskite curved image sensor with a hierarchical mesh architecture. They introduced an ultrathin image sensor with 5.4 μm thickness and soft interconnections, enabling it to be readily deformed into a hemispherical geometry. The ultrathin structure significantly reduces intrinsic mechanical behaviors, while interconnections effectively release twisting and stretching stress among pixels under various curvature conditions. As a result, the curved sensor array achieves a low detection limit of 10 nW cm−2, approaching the light sensitivity level of human photoreceptors. The focus-tunable imaging system integrates a curved image sensor with a shape-tunable convex lens, forming a conformal, skin-like architecture on a hemispherical surface. Finite element analysis revealed that when deformed to a curvature radius of 17.8 mm, the maximum strain on the Parylene C substrate reaches 5.72% and is primarily localized at pixel interconnections and edge regions. The curved image sensor achieves an overall thickness of approximately 5.4 μm and integrates a perovskite photodetector array comprising 127 pixels, enabling mechanically robust operation under pronounced curvature.

SCIENCE CHINA Materials2026DOI: 10.1007/s40843-025-3911-7

Generative AI Empowers Minimalist Wearable Personalized Human-Machine Interface

The seamless integration of electronics with the human body is pivotal for next-generation human-machine interfaces (HMI) and personalized healthcare. Traditional high-density sensor arrays, while capable of capturing complex biomechanical data, impose significant power and comfort penalties. This study introduces the Generative EMG Network (GenENet), a framework that synergizes generative artificial intelligence with soft bioelectronics to reduce hardware complexity. By leveraging a 32-channel stretchable sensor array as a 'teacher' dataset, GenENet employs a masked autoencoder architecture to learn spatiotemporal correlations within high-density electromyography (EMG) data. The trained model enables a simplified 6-channel wearable band to replicate the performance of the full 32-channel array. The sensor device utilizes a polydimethylsiloxane (PDMS) substrate, liquid metal (EGaln) interconnects, and a conductive PEDOT:PSS hydrogel interface, achieving low skin-contact impedance and high signal-to-noise ratios under mechanical strain. This approach addresses the bottleneck of data throughput and power consumption in wearable HMIs, offering a path toward minimalist, personalized devices for applications such as sign language decoding and gait analysis. The findings underscore the potential of generative AI to transform wearable bioelectronics by shifting computational burden from hardware to software.