Min Song

Understanding and predicting the reactivity of organic pollutants toward reactive species is crucial for designing efficient and targeted degradation strategies for advanced oxidation processes. However, the structural complexity and chemical diversity of pollutants pose challenges for developing interpretable and high-throughput predictive frameworks. Here, a machine-learning-based approach that integrates quantum chemical descriptors and molecular fingerprints descriptors to predict pollutant reactivity toward sulfate radicals is presented. By combining RDKit and conceptual density functional theory (CDFT) descriptors, key structure–activity features, including E_HOMO(N), electron-donating capacity, ring structures, branching, and molecular surface areas, are identified, and their quantitative reactivity thresholds are established. Additionally, the quantitative read-across structure–activity relationship model incorporating intermolecular similarity expands the applicability domain (AD) to 74.3% across 12 pollutant classes, a 2.1-fold increase in the AD over quantitative structure–activity relationship (QSAR) approaches. Experimental validations across structurally diverse compounds demonstrate strong predictive performance (R² = 0.811). This work provides a transparent and high-throughput predictive framework for reactivity prediction of organic pollutants, facilitating the bottom-up design of an advanced oxidation process tailored to specific pollutant profiles.

Alkali-activated materials have gained increasing popularity in the field of soil barrier materials due to their high strength and low environmental impact. However, barrier materials made from alkali-activated materials still suffer from long setting times and poor barrier performance in acidic, alkaline, and saline environments, which hinders the sustainable development of green alkali-activated materials. Herein, coconut shell biochar, sodium silicate-based adhesives, and polyether polyol/polypropylene polymers were used for multi-stage material modification. The modified materials were evaluated for barrier performance, rapid formation, and resistance to acidic, alkaline, and saline environments, using metrics such as compressive strength, permeability, mass loss, and VOC diffusion efficiency. The results indicated that adhesive modification reduced the material’s setting time from 72 to 12 h. Polymer modification improved resistance to corrosion by 15–20%. The biochar-containing multi-stage modified materials achieved VOC diffusion barrier efficiency of over 99% in both normal and corrosive conditions. These improvements are attributed to the adhesive accelerating calcium silicate hydration and forming strength-enhancing compounds, the polymer providing corrosion resistance, and biochar enhancing the volatile organic compounds (VOC) barrier properties. The combined modification yielded a highly effective multi-stage green barrier material suitable for rapid barrier formation and corrosion protection. These findings contribute to evaluating multi-level modified barrier materials’ effectiveness and potential benefits in this field and provide new insights for the development of modified, green, and efficient alkali-activated barrier materials, promoting the green and sustainable development of soil pollution control technologies.

Low-temperature plasma, generating both reductive electrons and diverse oxidative species, has demonstrated considerable potential for the degradation of perfluorooctanoic acid (PFOA). However, limited understanding of electron propagation mechanisms during discharge has led previous research to focus on hydrated electrons (e_aq^-) while neglecting free electrons (e^-). In this study, a consistent and modeled dielectric barrier discharge (DBD) plasma was employed to degrade PFOA. Contribution analysis indicated that reactions driven by e^- were dominant, with substantial contributions from hydroxyl radical (•OH)-mediated oxidation. By integrating a kinetic model with a streamer solver, a basic discharge unit model was developed. Simulation of e^- streamer propagation identified a high-intensity response electric field formed by the e^- memory effect, with a peak strength of 1.816 × 10⁶ V/m. This electric field facilitated a secondary acceleration of e^-, allowing e^- to penetrate the surface water layer and directly attack PFOA via chain-shortening mechanisms. The delocalized state of e^- restricted degradation primarily to the gas-liquid interface, minimizing interference from the surrounding medium. This study highlights the previously overlooked role of e^- and provides essential theoretical insights for the plasma-based treatment of PFOA-contaminated water.