ACS ES&T engineering最新文献

Recent progress has brought carbon-confined transition metal catalysts to the forefront as effective agents for Fenton-like reactions. However, achieving a stable integration of densely loaded and well-dispersed transition metals onto carbon support poses significant challenges. Herein, we introduce a plant polyphenol-driven polymerization-confinement method for the synthesis of a highly dispersed FeCo bimetallic catalyst (FeCo@NGB). Utilizing the chelating effect of tea polyphenols with metal ions and their subsequent polymerization and confinement offers a durable solution for stabilizing the FeCo bimetallic sites. The resulting FeCo@NGB demonstrates exceptional performance in activating peroxymonosulfate (PMS) for the swift degradation of tetracycline (TC), with a 99.5% reduction achieved in just 30 min, predominantly through a singlet oxygen (¹O₂)-driven pathway. Experimental and theoretical calculations highlight the pivotal role of atomically dispersed FeN₄–CoN₃ sites in facilitating rapid electron transfer between the catalyst and PMS, thereby enhancing ¹O₂ production. This work not only advances the development of high-performance multiphase catalysts but also introduces a compelling strategy for water purification leveraging nonradical oxidative pathways.

Atomic force microscopy (AFM), as a type of scanning probe microscopy (SPM), possesses formidable capabilities for nanoscale imaging and force spectroscopy. Due to its advantages such as high resolution, nondestructive detection, minimal environmental restrictions, strong versatility, and real-time in situ analysis, AFM has become an indispensable tool in surface science and materials research, finding extensive applications in the study of the membrane separation and fouling processes. The tremendous advantages of AFM in characterization applications stem from its diverse tip functionalization techniques. This review encompasses the preparation of AFM probe tips and the modification techniques of special tips, including carbon nanotube (CNT) probes, metal nanowire probes, colloidal probes, and single-cell/molecule probes. Furthermore, it highlights the applications and advancements of AFM and probe modification techniques in membrane technology research. With the continuous development of tip modification techniques, the analytical capabilities of AFM will be further expanded, promising broader prospects for its application in the study of membrane fouling mechanisms and the development of antifouling membrane materials.

Flow-electrode capacitive deionization (FCDI) has created a breakthrough toward a more stable desalination performance by adopting a flow-electrode compared to existing capacitive deionization and membrane capacitive deionization as a promising electrochemical water treatment technology. However, the FCDI technology requires investigation of various mechanisms pertaining to flow-electrode materials to achieve system optimization. Further, studies on applying machine learning to the FCDI technology have been scarcely reported. Our study aims to explore optimal algorithms via machine learning for predicting the salt adsorption capacity of FCDI processes and evaluate the feasibility of optimization applications. Concurrently, a comparative analysis was conducted through the performance model indicators of mean absolute error (MAE), mean squared error, and R² for support vector machine, random forest, and artificial neural network (ANN) algorithms. Herein, we demonstrated that the optimal ANN-based model exhibited the highest predictive performance, achieving R² and MAE values of 0.996 and 0.21 mg/g, respectively. Additionally, the Shapley additive explanations (SHAP) confirmed a trend in the contribution of influent concentration, aligning closely with the results of statistical analysis. Specifically, the change in voltage of the FCDI process serves as a key factor in determining salt adsorption efficiency. Moreover, a parallel comparison of the Pearson correlation coefficient and SHAP analyses suggests that the impact of voltage entails a nonlinear contribution within the realm of machine learning. Finally, to deploy a machine learning-driven ANN model system, we present multiple factors (e.g., weight of flow-electrodes, influent concentration, and voltages) as a reinforcement learning model for decision-making. This offers valuable insights and guidance for future operations of the FCDI process.

Herein, a ball-milled composite mineral derived from shell powder and phosphate rock (BSPR) was innovatively coupled with smooth vetch (SV) planting to remediate Cd-polluted soil. The results showed that BSPR coupled with SV could reduce the bioavailability of Cd in soil by 68.1%, which was 2.2 and 3.4 times those of BSPR (31.1%) and SV (20.0%) alone, respectively. The outstanding passivation performance was confirmed to be due to the synergistic effect of BSPR and SV. Specifically, the employment of BSPR not only increased the biomass of SV but also promoted its decomposition, which directly led to the conversion of SV into more humic acid (HA). Under the action of HA, more calcium and phosphorus were released by BSPR and combined with Cd to form stable compounds (e.g., Cd₃(PO₄)₂, Cd₅(PO₄)₃Cl, and Cd_nCa_5–n(PO₄)₃OH). Amazingly, due to the excellent performance of synergetic BSPR and SV on Cd passivation and soil improvement, the Cd content in pepper fruit decreased by 38.2% (6.3 times that of SV), and the fresh weight and Ca content of pepper fruit were enhanced by 28.2% and 43.4% (6.7 and 22.8 times that of SV, respectively). Overall, this study provided a novel strategy, i.e., green manure coupled with mineral, for simultaneous remediation of Cd-contaminated farmland and enhancement of pepper’s yield and quality, thus achieving “killing three birds with one stone”.