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JZ
Verified CAS / Academic Author2 Decoded Studies

Prof. Jiamin Zheng

College of Materials, Xiamen University

Research Publications & English Decoded Briefs

Showing 2 publications
SCIENCE CHINA Materials2026DOI: 10.1007/s40843-026-4315-2

Ultra-high-crystallinity transparent glass-ceramic scintillators for high-temperature X-ray imaging

High-temperature X-ray imaging demands scintillators with high crystallinity, efficient scintillation, and robust thermal stability, yet suitable materials remain scarce. Here, we report an ultra-high-crystallinity transparent glass-ceramic (GC) scintillator strategically designed via controllable heat-treatment-induced crystallization. A sequential precipitation method is employed, where cubic CaF2 nanocrystals initially form, subsequently promoting heterogeneous nucleation and growth of hexagonal BaAl2Si2O8. Intrinsic nanoscale phase separation into F-rich and O-rich domains significantly reduces atomic diffusion distances, yielding an unprecedented crystallinity of up to 97.6%. Notably, defect traps (oxygen vacancy defects, likely located within the lattice or at crystalline/amorphous interfaces) enable efficient carrier capture and thermally stimulated release, contributing to remarkable resistance to thermal quenching. Consequently, the GC scintillator maintains 90.6% of its integrated X-ray excited luminescence (XEL) intensity at 300 °C, with the integrated XEL intensity reaching 94.2% of commercial Bi4Ge3O12 (BGO) at room temperature. This enables stable high-temperature X-ray imaging with a spatial resolution of ~10.4 lp mm−1 up to 225 °C. This work provides a versatile pathway for developing high-sensitivity scintillators for extreme-environment X-ray imaging.

SCIENCE CHINA Materials2026DOI: 10.1007/s40843-026-4151-2

Machine Learning for Ionic Liquids in CO2 Conversion: Advances, Challenges, and Perspectives

The rapid increase in atmospheric CO2 due to fossil-fuel consumption has heightened the demand for efficient carbon capture and utilization technologies. Ionic liquids (ILs) have emerged as versatile media and catalysts for CO2 conversion, offering advantages such as negligible volatility, wide electrochemical windows, and strong CO2 affinity. However, the vast design space of ILs and limited experimental data make traditional trial-and-error screening inefficient. This review summarizes recent advancements in applying machine learning (ML) to the design and screening of ILs for CO2 conversion. The roles of ILs in catalytic processes and the limitations of traditional screening methods are discussed. ML-based workflows are explored, with emphasis on addressing challenges posed by small and noisy datasets. Finally, future opportunities in mechanism-informed descriptors, multi-objective optimization, and the integration of domain expertise with data-driven approaches are highlighted to accelerate the discovery of next-generation ILs for sustainable CO2 conversion.