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Verified CAS / Academic Author2 Decoded Studies

Prof. Liang Sun

School of Materials Science and Engineering, Tianjin University

Co-Affiliations:Not explicitly stated in the provided text; likely Chinese Academy of Sciences or a university.

Research Publications & English Decoded Briefs

Showing 2 publications
SCIENCE CHINA Materials2026DOI: 10.1007/s40843-025-3628-3

Regulating Solution Aggregation and Entanglement for Efficient Self-Powered All-Polymer Photodiodes in Water Quality Monitoring

The solution aggregation structures of conjugated polymers are pivotal in determining their film morphology and optoelectronic properties, yet the relationship between solution aggregation and device performance remains elusive in organic photodiode (OPD) systems. Herein, we introduce the first examination of solution aggregation structures of all-polymer OPD blends, with a focus on how molecular entanglement modulates aggregation behavior and subsequent photodiode performance of low-cost poly(3-pentylthiophene). Using small-angle neutron scattering and freeze-dried imaging, we provide a comprehensive analysis of the solution-state aggregation behavior of poly(3-pentylthiophene) and its evolution in the blend, revealing profound impacts on film morphology and device performance. With finely optimized aggregation, the resulting all-polymer OPD achieves a record-high specific detectivity of ~4×10^13 Jones at zero bias, outperforming all bulk heterojunction (BHJ)-type self-powered OPDs reported to date. This device also demonstrates remarkable thermal stability, with negligible performance degradation after over 800 h of thermal annealing at 85 °C. Furthermore, the self-powered OPD exhibits excellent performance across a broad spectral range, enabling its application in both water quality monitoring and biosensing. This work offers new insights into the solution aggregation behavior of conjugated polymers in OPDs and highlights the importance of resolving solution aggregation in optimizing device function.

SCIENCE CHINA Materials2026DOI: 10.1007/s40843-025-4057-4

Machine Learning-Assisted Rapid Development of High Performance Flexible Lead-Free Radiation Shielding Gels

The escalating use of ionizing radiation in medical and industrial applications necessitates lead-free, flexible, and sustainable shielding materials. Current development relies on empirical trial-and-error, which is inefficient. This study introduces a machine learning-assisted Monte Carlo simulation strategy for rapid optimization of metal filler compositions for X-ray attenuation across 40–120 kV. Guided by this AI-driven approach, polyvinyl alcohol (PVA)-based gels containing uniformly dispersed Bi/W/Gd2O3 nanoparticles were developed, forming within 1 minute at -20°C using a PVA-DMSO/H2O co-solvent system. The optimized gel with 50 wt% metal loading exhibits exceptional mechanical properties: tensile strength of 1.76 MPa, toughness of 6.3 MJ m−3, and elongation of 600%. It achieves >98% X-ray shielding efficiency at 5 mm thickness, outperforming lead composites at 120 kV. The physically cross-linked network provides recyclability and anti-freezing capability, retaining flexibility at -50°C. This work establishes a data-driven paradigm for designing high-performance radiation-shielding materials, demonstrating AI's potential to accelerate materials discovery and enable scalable fabrication of eco-friendly protective systems.