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ZJ
Verified CAS / Academic Author2 Decoded Studies

Prof. ZHENG Junjian

School of Life and Environmental Sciences, Guilin University of Electronic Technology, Guilin 541004, China

Co-Affiliations:Guilin University of Technology / Guilin University of Electronic Technology

Research Publications & English Decoded Briefs

Showing 2 publications
Chinese Journal of Environmental Engineering2026DOI: 10.12030/j.cjee.202506064

Electrocatalytic Oxidation Performance and Mechanism of Porous Active Metal Oxide Coated Anode for Congo Red Degradation

To address the challenges of high salinity, recalcitrance, limited mass transfer, and coating detachment in traditional anodes for textile wastewater treatment, a porous RuO2@r-TiO2 nanotube array (NTA) anode was fabricated via anodic oxidation, electrochemical reduction, and thermal decomposition. A flow-through electrochemical oxidation system was constructed using this anode and a graphite felt cathode. The material's morphology and physicochemical properties were characterized by SEM, XRD, and XPS. Congo red (CR) was used as a model pollutant to evaluate degradation performance under various conditions. Optimal conditions were identified as current density 5 mA·cm−2, permeate flux 480 L·(m2·h)−1, initial CR concentration 0.15 mmol·L−1, and NaCl concentration 75 mmol·L−1. Under these conditions, the system achieved 91% decolorization within 20 min and 82% mineralization within 60 min. Mass transfer tests showed a rate constant of 2.23×10−4 m·s−1 in flow-through mode, three times higher than conventional mode, with active chlorine and H2O2 production increased by 32.8% and 66.7%, respectively. Radical quenching experiments indicated that singlet oxygen (1O2) was the primary reactive species. The degradation mechanism was proposed based on quenching and UV spectral analysis. The system achieved >90% decolorization for five typical dye pollutants with an energy consumption of only 0.16 kWh·m−3. Cyclic voltammetry confirmed long-term stability. These findings provide theoretical support for applying electrochemical advanced oxidation to high-salinity textile wastewater.

Journal of Environmental Engineering Technology2026DOI: 10.13205/j.hjgc.202606003

Machine Learning-Driven Development of Membrane Materials for Optimized Lithium Recovery Performance

Membrane separation technology, offering high separation efficiency, low energy consumption, and operational flexibility, is promising for lithium recovery. However, selective lithium extraction from complex matrices such as salt lake brines and battery leachates remains challenging. Traditional membrane development relies on empirical trial-and-error, suffering from low efficiency and the permeability-selectivity trade-off. This review systematically delineates machine learning (ML)-based frameworks for membrane material development, including high-throughput rational screening, inverse design of synthesis protocols, and high-fidelity performance prediction. We elucidate how advanced ML algorithms decipher structure-activity relationships at the molecular level, enabling breakthroughs in performance ceilings and guiding bottom-up fabrication of next-generation membranes. Critical challenges are assessed: scarcity of high-quality standardized datasets, limited model interpretability, and poor generalizability to industrial scales. Future directions emphasize physics-informed hybrid models, open-source global databases, and full-process system optimization to bridge laboratory innovation and industrial deployment.