As the global demand for sustainable energy solutions intensifies, the efficiency of devices like metal-air batteries and fuel cells hinges on a critical chemical process: the oxygen reduction reaction (ORR). Historically, platinum group metals have been the gold standard for catalyzing this reaction, but their scarcity and high cost remain significant barriers to widespread commercialization.

This multimodal MRI study shows that hepatic iron deposition, brain iron accumulation, and selective brain atrophy differ between hepatic-type (HWD) and neurological-type Wilson’s disease (NWD). Quantitative susceptibility mapping (QSM) and liver R₂^* measurements correlate with neurological severity and effectively distinguish NWD from HWD, highlighting iron dysregulation as a key contributor to disease heterogeneity.

 Researchers have developed an integrated gray wolf optimization algorithm-based hybrid estimation framework that combines sample entropy, localized voltage area, and fuzzy entropy to accurately estimate the state of health of bipolar lead-acid batteries. Partial charging profiles are utilized to extract and validate battery health feature attributes based on gray relational grades. The proposed hybrid models utilize two pairs of battery health attributes: localized voltage area paired with either fuzzy entropy or sample entropy. The average mean absolute error and average root mean squared error values are below 1.02 percent and 1.5 percent respectively for the localized voltage area and fuzzy entropy health attribute pair.

 Researchers have developed a hybrid surrogate model for iso-octanol oxidation to iso-octanal that integrates data-driven approaches with chemical equations grounded in mass transfer, heat transfer, momentum transfer, and reaction engineering. By establishing a precise mechanistic model based on an Aspen Plus generated database, the team overcame the challenge of scarce oxidation experimental data caused by long operating cycles and hydrogen safety concerns. Compared to direct process simulation and multi-objective optimization methods, surrogate models exhibit computational speeds exceeding 400 times those of traditional methods. The optimization results reveal significant reductions in both primary energy demand and greenhouse gas emissions.

Researchers have developed several data-mechanism hybrid driven methods to improve key variables prediction in process industry. Based on random forest, extreme gradient boosting, and artificial neural network, these methods were validated through benzene-toluene-xylene distillation and steam methane reforming cases. Under noise intensity of 10 to 20 percent and sample sizes of 100 to 400, the coefficient of determination improved by up to 5.2 percent for random forest, 17.7 percent for extreme gradient boosting, and 36.2 percent for artificial neural network compared to data-driven models alone.

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. 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.

 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, their application 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. Crosslinking the 15-crown-5 with Li⁺ boosts Li⁺ transport by weakening the coordination between Li⁺ and polymer chains. The crosslinked 15-crown-5 moves along with Li⁺ to the anode and decomposes to form the Li₂O-rich SEI with faster Li⁺ diffusion kinetics. Therefore, the symmetric Li-Li cell could stably maintain cycling over 1100 h. The LiFePO₄‖Li full battery presents high retention of capacity (92.75%) over 500 cycles at 1 C.

Heteroatom-doped carbon-based materials are acknowledged as a promising approach to enhance catalytic activity. In this study, phosphorus-doped activated carbon-supported zinc catalysts, rich in Lewis acid sites for acetylene acetoxylation, were synthesized. Characterization showed P-doping reduces electron density around zinc, facilitating electron transfer from acetic acid. The optimized Zn/0.01PAC catalyst achieved 80% conversion of acetic acid, demonstrating the critical role of Lewis acid sites.

 Infinite dilution activity coefficient is a key thermodynamic parameter in solvent design for chemical processes. Although conductor-like screening model for segment activity coefficient exhibits strong prior predictive capabilities, its estimations are sometimes only qualitative rather than quantitative. Another limitation of COSMO-SAC arises from the reliance on time-intensive quantum chemistry calculations, which restricts its scalability for large-scale solvent screening. To overcome these issues, this study integrates COSMO-SAC with machine learning for accurate infinite dilution activity coefficient prediction of binary mixtures. By bypassing the necessity for quantum chemistry calculations, the multi-task machine learning model could rapidly predict the surface charge density distribution and molecular cavity volume of molecules and ions, while accurately distinguishing isomers. Four adjustable parameters of COSMO-SAC are optimized using more than 20000 experimental data points of infinite dilution activity coefficient, and residual systematic errors are further corrected with the boosting ensemble strategy to improve the model performance. The resulting hybrid model reduces the mean absolute error from 0.944 to 0.102, representing an 89% improvement, while preserving the physicochemical interpretability of model.

Addressing the growing challenge of oil pollution, this study presents a green and efficient strategy for fabricating biodegradable poly(lactic acid)/poly(butylene adipate-co-terephthalate)/talc composite foams with high volume expansion ratio, excellent compression resilience, and superior oil absorption performance via synergistic melt blending and supercritical CO₂ batch foaming. By strategically incorporating PBAT and talc into the PLA matrix, and by optimizing the foaming temperatures, the melt strength and crystallization behavior were effectively tailored. The resultant foam achieved a volume expansion ratio exceeding 45 and an open-cell content of 85%. Remarkably, the foam exhibited equilibrium oil absorption capacities of 22.2 g·g⁻¹ for silicone oil and 13.4 g·g⁻¹ for cyclohexane, retaining over 85% of its initial absorption capacity after 10 consecutive cycles.