ACS ES&T engineering最新文献

This study investigated the material and ion transport properties of TiO₂ nanopores as a function of solution conditions and applied electrode potentials. Zeta potential measurements revealed that the TiO₂ surface charge was highly dependent on solution conditions, which was attributed to protonation/deprotonation of surface functional groups and adsorption of ions. Ion rejection followed the absolute magnitude of the membrane surface charge and was pH-dependent, reflecting the amphoteric nature of TiO₂. The rejection of NaCl was approximately symmetrical about the point of zero charge of the membrane, with the highest rejection at acidic and basic conditions. Specific adsorption of SO₄^2– and Mg²⁺ under acidic and basic conditions, respectively, neutralized the membrane charge and significantly reduced ion rejection. A mathematical transport model was fit to experimental data, and the model-determined membrane charge densities as a function of solution conditions agreed with experimental zeta potential measurements. Model results also revealed that rejection was primarily attributed to the Donnan exclusion mechanism. The application of both anodic and cathodic potentials directly to the TiO₂ membrane caused permselective transport under specific solution conditions.

With the sharp increase in electronic and electrical equipment as well as concomitant electronic waste, it is imperative to recover precious metals from secondary resources from the perspective of environment protection and sustainable development. Herein, a free-standing, dual-cross-linking polydopamine (PDA) in conjunction with poly(imide dioxime) (PIDO) porous membrane (denoted as PDA/PIDO) is fabricated via a facile interfacial polymerization method for gold recovery. As expected, the constructed PDA/PIDO membrane features a hierarchical porous structure, ample active sites, and excellent hydrophilicity, endowing it with an ultrahigh gold capture capacity (3368 mg g^–1), fast equilibrium time (35 min), superior recovery selectivity (separation factor of Au/Cu = 5.4 × 10⁵, Au/Ni = 3.9 × 10⁵), high flux (1050 L m² h^–1), and high retention rate (98%). Furthermore, the proposed PDA/PIDO membrane is also competent for selective gold recovery from the central processing unit leachate with remarkable efficiency in a continuous-flowing filtration system, highlighting its huge potential in practical large-scale gold recovery from e-waste.

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.