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ZM
Verified CAS / Academic Author7 Decoded Studies

Prof. ZHENG Min

Beijing University of Chemical Technology

Co-Affiliations:School of Environment, Hangzhou Institute for Advanced Study, University of Chinese Academy of SciencesHuazhong University of Science and Technology

Research Publications & English Decoded Briefs

Showing 7 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.

The Chinese Journal of Process Engineering2026DOI: 10.12034/j.issn.1009-606X.225213

Gas-liquid dispersion characteristics in a stirred tank equipped with porous aeration tube

Gas-liquid stirred tanks are widely used in oxidation, hydrogenation, and other chemical processes, where the gas dispersion state directly affects production efficiency. This study systematically investigated the effects of impeller type, impeller installation height, and rotational speed on gas-liquid dispersion in a stirred tank equipped with a porous tube sparger. Two typical impellers, a wide hydrofoil (WH) and a half-elliptical disk turbine (HEDT), were tested at various installation heights (L/D ratios) and gassing rates. The critical rotational speed for complete gas dispersion, agitation power consumption, and overall gas holdup were measured. Results showed that for both impellers, the critical Froude number (Fr) decreased significantly with increasing gas flow number (FlG). Under the same gassing rate, the HEDT impeller generally required a higher critical Fr and greater agitation power for complete dispersion compared to the WH impeller. Relative power demand (RPD) decreased as FlG increased, with a more pronounced decline at higher L/D ratios. At different impeller positions, the RPD of the HEDT impeller was higher than that of the WH impeller, indicating that the HEDT impeller's power was less affected by gas. Notably, the impeller installation height significantly influenced gas holdup and power consumption. When L/D = 0.75, higher gas holdup and lower power consumption were observed. This work provides crucial theoretical and data support for optimizing the design of gas-liquid stirred tanks with gas sparging, offering clear engineering value for enhancing mass transfer efficiency and energy-saving operation in chemical processes.

Environmental Chemistry2026DOI: 10.7524/j.issn.0254-6108.2025101901

Formation and Emission of Hexachlorobutadiene during Chlorinated Chemical Production and Its Impact on the Surrounding Environment

Hexachlorobutadiene (HCBD) is a persistent organic pollutant (POP) regulated under the Stockholm Convention. Chlorinated chemical production processes are major sources of unintentional HCBD emissions, posing potential threats to ecosystems and human health. This study systematically reviews the formation, emission, and environmental impact of HCBD from such processes. HCBD is widely generated as a by-product during chlorination stages of producing carbon tetrachloride, dichloroacetylene, tri-/tetrachloroethylene, and chlorobenzene, via free-radical mechanisms. It is released through waste gas, wastewater, and solid waste. In the environment, HCBD exhibits multimedia distribution, undergoing long-range atmospheric transport and adsorbing onto soil and sediments, thereby becoming secondary pollution sources. HCBD shows significant bioaccumulation and food-chain magnification; it is toxic to aquatic organisms and causes hepatic and renal damage with potential carcinogenicity in mammals. Effective pollution control requires combined process improvements and end-of-pipe treatments, supplemented by stringent emission standards and life-cycle management. Future research should focus on developing precise emission inventories, elucidating multi-media transport and transformation mechanisms, and assessing composite ecotoxicological effects, thereby providing scientific support for implementing international conventions and formulating effective prevention strategies.

SCIENCE CHINA Materials2026DOI: 10.1007/s40843-025-3826-9

Intrinsic Cyclic Boron Dipyrromethene Nanoparticles with Tumor-Activated Disassembly for Enhanced Phototherapeutic Stability and Efficacy

Cyclic molecular architectures offer unparalleled functional diversity and assembly advantages, holding significant promise for applications in nanomedicine. Here, we propose a cyclic molecular engineering strategy designed to address the hydrophobicity of organic dyes while simultaneously enhancing their phototherapeutic efficacy. Through esterification of the boron dipyrromethene (BDP) core with adipic acid (CB-c) or dithiodiacetic acid (CB-s), we developed self-assembling nanoparticles (NPs) with exceptional colloidal stability (>60 d) and microenvironment-responsive dissociation. CB-s NPs exhibited unique antiparallel dimeric packing in crystallographic studies, enabling robust H-aggregation. The redox-sensitive disulfide bonds in CB-s NPs conferred tumor-selective disassembly (90% dissociation within 30 h), facilitating spatiotemporally controlled therapeutic activation. In vivo studies demonstrated superior synergistic photodynamic/photothermal therapy (PDT/PTT) efficacy, achieving 92% tumor suppression. This work establishes cyclic architecture-driven supramolecular organization as a paradigm-shifting approach for developing multifunctional nanomaterials.

SCIENCE CHINA Materials2026DOI: 10.1007/s40843-025-4079-4

Promoting cycling and thermal stability of ultrahigh-nickel oxide cathodes with well-controlled microstructure and stiffness

Utilization of ultrahigh-nickel LiNi_xCo_yMn_1-x-yO_2 (NCM) (x > 0.97) in Li-ion batteries can distinctively boost energy density through enhanced discharge capacity. However, capacity and thermal stability deteriorate as Ni content approaches the limit. Here, we propose a facile strategy by introducing high-valence tungsten (W) into ultrahigh-nickel polycrystalline LiNi_0.98Co_0.01Mn_0.01O_2 (PCNCM98). W-doped PCNCM98 (W-PCNCM98) exhibits refined, compactly stacked primary particles, whereas PCNCM98 shows equiaxial, non-uniform larger particles. The refined microstructure enhances mechanical strength: average particle hardness of W-PCNCM98 is 104 MPa, 1.5 times higher than PCNCM98 (68 MPa). This improved mechanical property suppresses lattice volume changes and relieves microcrack formation from H2–H3 phase transition. Consequently, cycling performance in pouch-type full cells is significantly enhanced, with capacity retention of 73% after 2000 cycles at 1 C and 25 °C, 54% higher than PCNCM98. Enhanced structural stability and strong electron affinity of W6+ also improve thermal stability: exothermic peak for W-PCNCM98 is postponed to 203 °C with heat generation of 1287 J g−1, versus 190 °C and 1528 J g−1 for PCNCM98. This high-valent doping strategy stabilizes ultrahigh-nickel NCM cathodes, accelerating large-scale EV applications.

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.