The safe and reliable usage of compact bipolar lead-acid batteries requires accurate estimation of their state of health. Designing state of health estimation frameworks based on the initial charging segments of a battery presents a significant challenge. In this study, an integrated gray wolf optimization algorithm-based hybrid estimation framework in combination with sample entropy, localized voltage area, and fuzzy entropy is developed to accurately estimate the state of health of bipolar lead-acid batteries. Partial charging profiles of bipolar lead-acid battery are utilized to extract and validate the useful battery health feature attributes based on gray relational grades to study battery health deterioration. The study also validates the better performance of the suggested hybrid model. The proposed hybrid models are developed utilizing two pairs of battery health attributes, such as localized voltage area paired with either fuzzy entropy or sample entropy. The average means absolute error and average root mean squared error values are below 1.02% and 1.5%, respectively, for the localized voltage area and fuzzy entropy health attribute pair. This confirms the effectiveness of the hybrid model as a health status estimation framework for the bipolar lead-acid batteries.

Microplastics, as persistent organic pollutants, are widely present in aquatic environments. Owing to their small size and tendency to adsorb other pollutants, traditional wastewater treatment processes struggle to effectively remove them, and they pose an increasingly serious threat to ecosystems and human health. Therefore, efficient, stable, and feasible treatment technologies to effectively collect or separate microplastics from wastewater are urgently needed. The continuous development of electrochemical technology, with its advantages of high efficiency, ease of operation, and controllability, has garnered significant attention and is being explored as a viable solution to water treatment challenges. Electrochemical technologies have also demonstrated good removal efficiency and potential prospects with regard to their application to remove microplastics from wastewater; however, systematic implementation guidelines to facilitate its commercialization are lacking. This review summarizes existing research on the use of five electrochemical technologies (electrocoagulation, electrooxidation, electroreduction, bioelectrochemistry, and electrosorption) for microplastics removal, and discusses their removal performance, influencing factors, and degradation mechanisms when used to treat microplastics in wastewater. Additionally, the advantages of combining electrochemical technologies with other methods for efficient microplastics removal are briefly described, with the goal of assessing the practical feasibility and future application trends of electrochemical methods for removing microplastics from wastewater.

Utilizing artificial intelligence to assist in the development of green processes for alcohol oxidation is a challenging and time-consuming task due to the lack of massive data and adequate optimization objectives. To solve these challenges, our work presents a hybrid surrogate model for iso-octanol oxidation to iso-octanal, integrating data-driven approaches with chemical equations grounded in mass transfer, heat transfer, momentum transfer, and reaction engineering, to enhance problem-solving efficiency. Specifically, a precise mechanistic model based on Aspen Plus generated database is developed to enhance the utility of experimental data, thereby overcoming the challenge of scarce oxidation experimental data caused by long operating cycles and hydrogen safety concerns. Based on this database, integrating machine learning techniques and intelligent optimization algorithms can quickly determine the optimal operating conditions for the iso-octanol oxidation reaction system. Compared to direct process simulation and multi-objective optimization methods, surrogate models exhibit higher efficiency, with computational speeds exceeding 400 times than those of traditional methods. The optimization results reveal significant reductions in both primary energy demand and greenhouse gas emissions, underscoring the effectiveness of the optimized solutions. Our work not only propels real-time optimization of alcohol oxidation production processes but also lays the groundwork for their widespread industrial application.

Heteroatom-doped carbon-based materials are acknowledged as a promising approach to enhance catalytic activity through modifications to their electronic structure and chemical characteristics. In this study, phosphorus-doped activated carbon (PAC)-supported zinc catalysts, rich in Lewis acid sites for acetylene acetoxylation, were synthesized using a cost-effective and sustainable method. Characterization showed P-doping reduces electron density around zinc, facilitating electron transfer from acetic acid to zinc and enhancing its adsorption. The electronegativity difference between phosphorus and carbon generates weak and Lewis acid sites, significantly boosting catalytic performance. PAC doping enhanced resistance to carbon deposits and slowed zinc loss, thereby improving catalyst stability and activity. The optimized Zn/0.01PAC catalyst achieved 80% conversion of acetic acid, demonstrating the critical role of Lewis acid sites. This work provides an efficient solid acid catalyst and establishes a universal strategy for precisely tuning activated carbon surface acidity, advancing industrial application prospects.

Polymer-based solid-state electrolytes with high flexibility and excellent processability present great prospects in all-solid-state lithium batteries. However, when encountering interface stability problems, the application of polymer-based solid-state electrolytes in all-solid-state lithium batteries is puzzling. In this work, we proposed a lithium crosslinking strategy to regulate the interfacial chemistry by tailoring an effective Li₂O-rich solid electrolyte interphase layer attributed to introducing 15-crown-5 into the polymer matrix. Specifically, crosslinking the 15-crown-5 with Li⁺ in polymer-based solid-state electrolytes boosts the Li⁺ transport by weakening the coordination between Li⁺ and polymer chains. The crosslinked 15-crown-5 moves along with the Li⁺ to the anode and decomposes to form the Li₂O-rich solid electrolyte interphase with faster Li⁺ diffusion kinetics, resulting in uniform lithium deposition and suppressing the dendrite penetration. Therefore, the symmetric Li-Li cell could stably maintain cycling over 1100 h without short-circuiting. The LiFePO₄||Li full battery presents high retention of capacity (92.75%) over 500 cycles at 1 C. Also, the NCM811||Li full battery can be well-operated in 300 cycles with the capacity retention of 81.44% at 1 C. This study inspires the development of high-performance all-solid-state lithium batteries by rationally tailoring interface chemistry components by regulating the coordinated structure of Li⁺ at the molecular level.

Due to their molecular-like ability to form directional bonds and self-assemble into complex architectures, patchy particles represent a promising frontier in the design of novel functional colloids. However, developing efficient strategies for synthesizing such intricate structures remains a significant challenge. Most current research has focused on the spatial control of patch placement, which is already difficult. Yet far fewer studies have addressed the more demanding goal of producing particles with chemically distinct patches. In this study, we present a new multistep approach to creating two distinct patches on silica particles using metallic layers of controlled thickness as sacrificial masks. Selective dissolution of these masks enables sequential functionalization of predefined surface areas, resulting in bi-patchy particles with two clearly differentiated functional patches, as confirmed by fluorescence microscopy. Overall, this work paves the way for fabricating colloidal building units that can form multiple directional bonds via orthogonal chemical functionalization.