Frontiers of Chemical Science and Engineering最新文献

As a cost-effective oil-water separation technology, fiber coalescers rely on a thorough understanding of the droplet coalescence mechanism. However, current research has primarily focused on the single process of sessile-sessile droplet coalescence. Using high-speed imaging and the mask region-based convolutional neural network, this study provided the first quantitative characterization of the complete dynamics of asymmetric pendant-sessile droplet coalescence, a phenomenon more prevalent in industrial settings. It was discovered that this process comprises three stages. In the liquid bridge formation stage (Stage I), the lateral expansion of the liquid bridge was dominated by the capillary pressure difference, and the influence of sessile-to-pendant droplet radius ratios on this process was negligible. The oscillation decay stage (Stage II) exhibited the uniqueness of the asymmetric system, where fiber adhesion accelerated energy dissipation, leading to rapid oscillation decay, while the amplitude of the capillary wave on the pendant droplet side was significantly enhanced with an increasing the size ratio. Ultimately, in the stable morphology formation stage (Stage III), increasing the size ratio to 1.5 could significantly reduce the size of the secondary droplets. These findings provided direct strategies for reducing polydisperse secondary droplets in industrial coalescers and enhancing separation efficiency.

This study develops a two-dimensional fluid model for atmospheric pressure non-equilibrium CO₂-H₂O plasma needle-plate configuration, incorporating a comprehensive set of plasma chemical reactions and photoionization effects. It focuses on investigating the influence of the CO₂/H₂O concentration ratio and quenching pressure on plasma streamer initiation and propagation dynamics. Numerical simulations show that increasing initial water vapor content significantly reduces electron energy and density, causing the discharge channel to contract when the reduced electric field is below 200 Td, due to strong dissociative adsorption reactions between electrons and water molecules. At higher reduced electric fields (above 200 Td), variations in water vapor content have minimal impact on primary electron transport parameters, likely because dissociative and ionizing collisions between electrons and CO₂/H₂O molecules become dominant. Increasing the quenching pressure enhances photoionization, but plasma discharge remains primarily sustained by direct electron-impact ionization. Low initial water vapor content and elevated quenching pressure both accelerate streamer propagation, with the concentration ratio exerting a more significant effect. Finally, the primary reaction pathways for key products (CO, OH, and electrons) are analyzed. These findings contribute to a better understanding of how the reactant concentration ratio and quenching pressure regulate the discharge reaction mechanism in atmospheric pressure non-equilibrium CO₂-H₂O plasma.

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.

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.

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.

The photoreduction of environmental contaminants such as nitrate (NO₃⁻) and carbon dioxide (CO₂) into clean and renewable fuels has emerged as a key strategy for mitigating global environmental challenges, in which perovskite photocatalysts offer a promising, cost-effective, and sustainable solution. In the current research, a novel CuBi₂S₄/Al₂WO₆/Ti₃C₂ MXene Schottky/Z-scheme ternary heterojunction photocatalyst was synthesized and developed for the efficient photoreduction of nitrate and carbon dioxide, as well as photocatalytic water splitting under visible-light irradiation. The nanocomposite integrates three distinct components: (i) zero-dimensional (0D) CuBi₂S₄ quantum dot (QDs) nanoparticles (acting as a metal-assisted sulfide perovskite photocatalyst), (ii) three-dimensional (3D) aluminum tungstate (Al₂WO₆) double perovskite (serving as the central oxide perovskite photocatalyst), and (iii) two-dimensional (2D) Ti₃C₂ MXene (functioning as a non-metallic co-catalyst facilitating interfacial charge transfer). A comprehensive assessment of operating factors revealed their significant influence on the photocatalytic behavior of the CuBi₂S₄/Al₂WO₆/Ti₃C₂ ternary photocatalyst. The CuBi₂S₄/Al₂WO₆/Ti₃C₂ photocatalyst achieved a nitrate reduction efficiency of 80%, with nitrogen gas (N₂) identified as the predominant reduction product (55% selectivity). The same catalyst also exhibited a CO₂ photoreduction efficiency of 70%, in which methane (CH₄) displayed the highest generation rate (13.87 mL·g⁻¹·h⁻1; 619 µmol·g⁻¹·h⁻¹) corresponding to a 50% selectivity. Moreover, the composite demonstrated an impressive hydrogen evolution rate of 16 mL·g⁻¹·h⁻¹ (714 µmol·g⁻¹·h⁻¹) during photocatalytic water splitting with an efficiency of 60%. Furthermore, the ternary heterojunction photocatalyst exhibited excellent reusability and structural stability, retaining its photocatalytic performance over five consecutive cycles.

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 allsolid-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 shortcircuiting. 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.

Real-time monitoring of gas-liquid sulfonation in microchannel reactors is challenging due to complex internal spatiotemporal dynamics and limited data availability, despite the reactors’ excellent heat and mass transfer properties. Therefore, this study proposes a deep learning-based measurement method that directly extracts key spatiotemporal information from reaction image sequences within microchannels, enabling accurate prediction of the yield level of sodium α-olefin sulfonate products. The core of the framework is a convolutional long short-term memory network and combines a TimeDistributed module to efficiently capture and analyze dynamic visual features. To address the issue of data sparsity in experimental studies, we developed a novel frame sampling temporal image augmentation strategy that significantly improves the temporal learning efficiency of the model by mining microscopic dynamic changes under macroscopic stable conditions. On the experimental data set, the augmented convolutional long short-term memory network model achieved an average accuracy of up to 97.44%, outperforming the model without augmentation by 19.66% and a traditional convolutional neural network by 9.94%. These results demonstrate that the proposed method is a robust and effective tool for monitoring microchannel gas-liquid sulfonation, paving the way for intelligent, data-driven control of complex micro-chemical processes.

The start-up performance of proton exchange membrane fuel cells in low-temperature environments directly affects their service life and market promotion prospects. However, it is still challenging to fully understand how different operating parameters synergistically intensify the cold startup efficiency of proton exchange membrane fuel cells. In this study, the cold-start performance of proton exchange membrane fuel cells is optimized via cathode catalytic H₂-O₂ reaction heating, integrated with machine learning for key indicator prediction and multi-objective optimization for operating parameter screening. The proposed strategy achieves a temperature rise exceeding 30 °C without external load at −20 °C, suppressing the peak ice volume fraction in the cathode catalyst layer to 3.28 vol % and ensuring post-start stability. Machine learning models can predict key cold-start indicators with high precision. SHapley Additive exPlanations analysis further reveals the complex nonlinear interactions between parameters and clarifies the key factors affecting cold-start performance. Non-dominated Sorting Genetic Algorithm-II optimization identifies Pareto-optimal solutions, demonstrating enhanced cold-start efficiency via synergistic regulation of reactant supply, temperature elevation, controlled anode back pressure, and coolant flow. These findings provide guidance for the engineering design and parameter regulation of proton exchange membrane fuel cells in cold-climate applications.

Solid-state hydrogen storage is widely recognized as a promising pathway for safe, high-density, and reversible hydrogen utilization, yet its advancement remains hampered by complex thermodynamic, kinetic, and structural constraints. This review highlights the emerging role of big data and machine learning in reshaping the research landscape. Through analyses enabled by the Digital Hydrogen-S platform, recent material development trends and persistent bottlenecks are systematically identified, revealing widespread misalignments with the US Department of Energy targets in storage capacity, operating temperature, and pressure. Data-driven approaches are shown to accelerate property prediction, high-throughput screening, and inverse design, while the integration with high-throughput computation and experimental validation is forming an intelligent closed-loop paradigm. Meanwhile, neural network potentials offer near-first-principles accuracy for probing hydrogen adsorption, dissociation, and diffusion, though challenges in long-range interactions and transferability remain. Looking ahead, establishing open-access multimodal databases (combining numbers, text, spectra, and images), developing multimodal large language models, implementing inverse design strategies, and constructing generalized neural network potentials capable of describing complete absorption-desorption cycles represent critical steps toward intelligent and practical material discovery. This review provides a structured framework to guide future research and accelerate the deployment of solid-state hydrogen storage technologies.