Frontiers of Chemical Science and Engineering最新文献

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.

Traditional quantitative structure-property relationship (QSPR) methods rely on molecular descriptors to quantify molecular structures and establish correlations with physical properties. In this study, we propose an approach that incorporates complete molecular structures to refine traditional QSPR methods and improve predictive accuracy. The supercritical properties used for modeling are collected from the literature. Molecular structures are optimized using density functional theory, from which molecular descriptors are derived. Both the structures and descriptors serve as inputs to the models developed in this work. Three models are constructed: a traditional artificial neural network model, a ResNet model, and a convolutional neural network (CNN)-enhanced model. Comparison with the JOBACK method shows that the CNN-enhanced model achieves higher predictive accuracy, whereas the ResNet model, which relies solely on molecular structures, suffers from pronounced overfitting.

The push for electrification in chemical engineering is accelerating the development of efficient technologies for external field intensification, such as microwave. These technologies aim to maximize the utilization of matter and energy. However, the emergence of fluid structure at nano-/microscopic levels, combined with the complex interactions between interfacial effects and microwave, poses significant challenges to existing theoretical frameworks. Traditional thermodynamic models, which rely on macroscopic experimental data within a phenomenological approach, may not accurately capture the precise variations in fluid structures at interfaces with microwave applied. In this perspective, we begin with quantum mechanics and propose the concept of equivalent potential, providing a fundamental principle to unify the impacts of interface and microwave. Meanwhile, the importance of fluid structure regulation within the framework of equivalent potential has been discussed, promoting deeper exploration of both thermal and nonthermal microwave effects. Looking ahead, the ongoing development and application of novel theoretical methods that decouple interfacial effects from external field effects, alongside advancements in in situ spectral characterization technologies, are expected to establish a paradigm based on the microscopic fluid structure regulation that better facilitates the utilization of microwaves in modern chemical engineering.

The design and industrial application of autothermal reactors for CO₂-to-methanol synthesis are constrained by multi-scale transport, multi-stability, and scale-up challenges, which complicate both modeling and experimental studies. Virtual and Digital Twin approaches provide a pathway toward new autothermal reactors with optimized performance for CO₂-to-methanol synthesis.