ACS ES&T engineering最新文献

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”.

The addition of soil amendments to facilitate plant-based remediation of soil contaminated with radioactive nuclides is considered a promising approach. Here, we tested different levels of biochar to help clean uranium-contaminated soil in the potted plants. Adding 1% biochar had the best results in deactivating uranium, increasing soil enzyme activity, and promoting ryegrass growth. Microbiological and metabolomic analysis further revealed that 1 wt % biochar significantly enhanced the abundance of microorganisms such as Actinobacteriota and Myxococcota and accelerated the production of differential metabolites such as lipids and lipid-like molecules, organic acids and derivatives, and organic oxygen compounds. The analysis of biological and nonbiological interaction networks indicates that the coordinated interaction between bacteria, enzymes, and metabolites significantly improves the expression level of the ABC transporter’s metabolic pathway. This enhances the resistance of living cells to uranium and maintains system homeostasis under uranium stress. This study provides an example of the application of biochar-assisted phytoremediation and offers theoretical guidance for the remediation of soil contaminated with radioactive nuclides.

Enzyme catalysis has shown its great power in dealing with global poly(ethylene terephthalate) (PET) waste. However, it is still challenging to design a super enzyme that can treat the sheer volume of worldwide PET waste. Without a complete understanding of the catalytic mechanism, it will be difficult to reach this important goal. Here, we systematically study the PET depolymerization mechanism catalyzed by structurally different hydrolases. The role of fleeting chiral intermediates was proved to be crucial. We observed different prochiral selectivities among these PET hydrolases. While most hydrolases favor Si-face binding, a few hydrolases (e.g., Humicola insolens cutinase) mainly adapt Re-face binding. Interestingly, we found that Si-face binding leads to higher activity than Re-face binding in all of the studied hydrolases. This Si-face selectivity originates from the difficulty of proton transfer from catalytic histidine residue to the substrate and the less stability of the oxyanion hole. Since the Si-face binding ratio ranges from 0 to 95%, we infer that all these hydrolases are not perfectly evolved to degrade PET. Our in silico results highlight that enlarging binding site residues (e.g., Leu66 and Asn69) will enhance enzymatic depolymerization, which was further confirmed by our in vitro experiments where both Leu66Phe and Asn69Phe show significantly increased PET hydrolysis activity. Hopefully, this work will aid the future rational design of super enzymes to fight PET pollution.

Nitridated zero-valent iron (N-ZVI) and its sulfidated counterpart (S–N-ZVI) are promising materials for groundwater remediation. The dechlorination performance of N-ZVI and S–N-ZVI is intricately linked to the specific N and S surface speciation, yet their roles in tuning the physicochemical characteristics, dechlorination reactivity, and electron selectivity of both particles remain unclear. In this study, we synthesized ZVIs using varied N and S agents, leading to the formation of different surface N species (iron nitrides (Fe_xN_y), pyridinic, and graphitic nitrogen) and sulfur species (FeS and FeS₂). The trichloroethylene (TCE) dechlorination rate showed a linear correlation with Fe_xN_y content, indicating Fe_xN_y-mediated ZVI dechlorination. Hydrogen production capacity was, however, linearly correlated with pyridinic N. Electron paramagnetic resonance (EPR) analysis revealed that pyridinic N enhanced proton transfer processes, thereby facilitating atomic hydrogen generation. This was further supported by the reduced H/D kinetic isotope effects (KIEs) in N-ZVI (2.07) and S–N-ZVI (∼1) compared to unmodified ZVI (3.06) and noticeable mitigation of surface passivation in N-ZVI and S–N-ZVI at pH 9. FeS and FeS₂ species minimized the hydrogen evolution reaction and removed the proton transfer limitation in TCE dechlorination. This magnifies the effect of Fe_xN_y and contributes to a synergistic interplay between nitridation and sulfidation in enhancing the dechlorination kinetics.