Journal of Environmental Engineering Technology•2026•DOI: 10.13205/j.hjgc.202604026
Polycyclic aromatic hydrocarbons (PAHs) are persistent organic pollutants ubiquitously present in soils, posing severe risks to ecosystems and human health. This study synthesized MIL-88A(Fe) via a hydrothermal solvent method and applied it to the photocatalytic degradation of phenanthrene-pyrene (PHE-PYR) composite contaminants in soil, investigating the adsorption-photocatalytic synergy. Results demonstrated that adsorption of PHE-PYR onto MIL-88A(Fe) was dominated by physical and monolayer surface adsorption, with a maximum adsorption capacity of 97.25 mg/kg. This strong adsorption increased pollutant concentration near active sites, accelerating photocatalytic degradation. Under optimal conditions—3% catalyst dosage, 40% soil water content, 60 min visible light irradiation, initial pollutant concentration of 200 mg/kg, and acidic soil—the total degradation efficiency reached 79.20%. Photoelectrochemical characterization revealed significant visible-light response (200–600 nm), a narrow bandgap of 3.04 eV, and favorable band structure facilitating efficient electron-hole separation. Quenching experiments identified superoxide radicals (·O2−) and holes (h+) as primary reactive species. GC-MS analysis of intermediates indicated that PYR undergoes hydroxylation, oxidation, and ring-opening to form PHE, which is further hydroxylated and oxidized, ultimately mineralizing to CO2 and H2O. This work provides an efficient strategy for remediating PAH-contaminated soils.
Journal of Environmental Engineering Technology•2026•DOI: 10.13205/j.hjgc.202606003
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.
Journal of Fuel Chemistry and Technology•2026•DOI: 10.1016/S1872-5813(26)60710-4
Lignin pyrolysis is a promising route for sustainable production of high-value phenolic chemicals, yet the intricate radical reaction network remains a major bottleneck to optimizing product selectivity. This work constructs a standardized DFT computational database that systematically describes the fast pyrolysis of vanillyl alcohol at 823.15 K. The database features three key components: primary reaction pathways, thermodynamic energy barriers, and atomic-level electronic fingerprints. The dataset covers primary reaction pathways, secondary rearrangements, and both global and local reactivity indices of key intermediates. Notably, it innovatively integrates electronic-structure fingerprints, filling the gap in reaction-network–electronic-property correlation data. Standardized computational workflows and rigorous quality control ensure accuracy, consistency, and reproducibility. The public release of this dataset provides a reliable theoretical benchmark for mechanistic studies of lignin pyrolysis and offers foundational data support for rational design of new catalysts and refinement of reaction kinetic models. Ultimately, this database not only provides an important reference for data-driven catalyst development but also lays a theoretical foundation for precise regulation of lignin depolymerization.