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Verified CAS / Academic Author2 Decoded Studies

Prof. WANG Tianyu

College of Environmental Science and Engineering, Guilin University of Technology; Center for Water and Ecology, Tsinghua University

Research Publications & English Decoded Briefs

Showing 2 publications
Chinese Journal of Environmental Engineering2026DOI: 10.12030/j.cjee.202512045

Electrocatalytic Degradation of Phenol by Sn-Sb Co-doped Ti/SnO2 Electrode: Performance and Mechanism

To optimize the anode structure of Ti/SnO2-based electrodes in electrochemical advanced oxidation processes (EAOPs) and enhance their electrocatalytic activity and stability, Sn-Sb co-doped Ti/SnO2 electrodes were fabricated via a sol-gel method. The degradation performance and mechanism were evaluated using phenol as a model pollutant. Three electrodes were prepared with different Sn/Sb molar ratios: Ti/SnO2 (10:0), Ti/Sb (0:10), and Ti/SnO2-Sb (9:1). Characterization by XRD, SEM, and electrochemical tests revealed that the Sn-Sb co-doped electrode exhibited a dense surface, higher oxygen evolution potential (OEP), larger electrochemically active surface area, and lower charge transfer resistance compared to single-doped counterparts. In constant-current electrolysis experiments (20 mA·cm−2, pH=5, 0.1 mol·L−1 Na2SO4), the co-doped electrode achieved superior phenol and TOC removal efficiencies and higher apparent rate constants, with the lowest specific energy consumption per unit TOC removal. Radical quenching and intermediate analysis indicated that hydroxyl radicals (·OH) were the dominant reactive species. The degradation pathway involved aromatic ring hydroxylation, ring opening, and further mineralization of short-chain carboxylic acids. Sn-Sb co-doping enhanced the generation of ·OH by increasing surface adsorbed oxygen and defect site density. This synergistic doping strategy significantly improved the electrocatalytic activity and service life of Ti/SnO2-based anodes, providing a basis for the rational design of anode materials for EAOPs in treating refractory organic wastewater.

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.