Frontiers of Chemical Science and Engineering最新文献

In recent years, an extensive study has focused on the effects of various factors associated with the membrane support layer such as the size of the pores, porosity, thickness, hydrophobicity, and hydrophilicity, through both theoretical and empirical approaches. Along with numerical and analytical modeling, these variables are described by various two- and three-dimensional models, which have also developed for these parameters and variables. For engineering the selective layer, different categories of materials based on various morphologies, dimensions, or porosity were used as interlayers. Regarding the interlayers, there are relatively inconsistent reports in the literature and publications, primarily due to a lack of research and modeling. By modeling the influence of interlayers in thin film composite membranes, an innovative insight could be provided for optimizing other membrane processes. As a result, this research emphasizes the modeling and discussion of interlayers and their performance, particularly in the forward osmosis process, where scientific data and modeling are lacking. In addition to discussing the funnel and gutter effect carried out by the interlayers present in all membrane processes, modeling the impacts of the interlayer in the forward osmosis process will provide novel perspectives that could influence other processes.

With the advent of the fourth technological revolution, the new generation of artificial intelligence (AI) has imparted new significance and opportunities to the modeling of momentum, heat, and mass transfer, as well as chemical reaction processes with the realm of chemical engineering. AI techniques are being widely employed in the chemical industry and are constantly evolving to offer more effective solutions for tackling practical challenges. This review delves the transformation of the chemical industry from traditional digital simulations to advanced AI-based approaches, targeting high efficiency and low carbon emissions across the scale from molecules to factories. Particular emphasis is mainly placed on the research carried out within the research group of Weifeng Shen. At the molecular level, the intelligent capture of molecular characteristics and the precise determination of structure-property relationships have reached a mature stage. Furthermore, multifunction-driven reverse molecular design for solvents, reaction reagents, and other substances has been accomplished through AI-based high-throughput screening and generative models. To improve the safety, environmental friendliness, and carbon reduction performance of chemical separation processes, a series of innovative reinforcement strategies have been put forward, with a primary focus on the systematic optimization of solvent design. On the process scale of actual production, it frequently occurs that the constructed mechanism model fails to align with the actual system behavior, thereby restricting the industrial application of the model. To solve this issue, mechanism-data hybrid-driven frameworks have been successfully developed, leveraging AI-enhanced prediction, diagnosis, optimization, and control for complex separation systems in practice. Finally, as a bridge connecting big data intelligent technology and actual industrial processes, dynamic digital twin modeling is discussed for its potential to boost efficiency and sustainability in the chemical industry.

Overuse of fossil fuels led to energy crises and pollution. Thus, alternative energy sources are needed. Hydrogen, with its clean and high-density traits, is seen as a future energy carrier. Producing hydrogen from electricity can store renewable energy for a sustainable hydrogen economy. While much research on water electrolysis hydrogen production systems exists, comprehensive reviews of engineering applications are scarce. This review sums up progress and improvement strategies of common water electrolysis technologies (alkaline water electrolysis, proton exchange membrane water electrolysis, solid oxide water electrolysis, and anion exchange membrane water electrolysis, etc.), including component and material research and development. It also reviews these technologies by development and maturity, especially their engineering applications, discussing features and prospects. Bottlenecks of different technologies are compared and analyzed, and future directions are summarized. The aim is to link academic material research with industrial manufacturing.

For over a decade, regenerative engineering has been defined as the convergence of advanced materials sciences, stem cell sciences, physics, developmental biology, and clinical translation for the regeneration of complex tissues. Recently, the field has made major strides because of new efforts made possible by the utilization of another growing field: artificial intelligence. However, there is currently no term to describe the use of artificial intelligence for regenerative engineering. Therefore, we hereby present a new term, “Regenerative Engineering AI”, which cohesively describes the interweaving of artificial intelligence into the framework of regenerative engineering rather than using it merely as a tool. As the first to define the term, regenerative engineering AI is the interdisciplinary integration of artificial intelligence and machine learning within the fundamental core of regenerative engineering to advance its principles and goals. It represents the subsequent synergetic relationship between the two that allow for multiplex solutions toward human limb regeneration in a manner different from individual fields and artificial intelligence alone. Establishing such a term creates a unique and unified space to consolidate the work of growing fields into one coherent discipline under a common goal and language, fostering interdisciplinary collaboration and promoting focused research and innovation.

The main challenge in the oxidative coupling of methane to C₂H₆/C₂H₄ (C₂-hydrocarbons) lies in the low selectivity to the desired products due to their high reactivity to form carbon oxides. Herein, we report that the selectivity in chemical looping oxidative coupling of methane over supported Mn-Na₂WO₄-based catalysts can be significantly increased by catalyst promotion with Li₂CO₃ and performing the reaction with co-fed steam. The selectivity reaches 89% (about 60% C₂H₄ selectivity) at a methane conversion of 19%. The best-performing catalyst showed durable within 90 reaction/reoxidation cycles. With the aid of sophisticated catalyst characterization studies combined with temporal analysis of products, the origins of the enhancing effects of the promoter and steam have been elucidated and can be applied for the development of selective catalysts in various alkane oxidation reactions.

The integration of high-throughput experimental technologies with artificial intelligence is transforming catalyst research and development. This study explores the synergistic convergence of artificial intelligence and high-throughput experimentation in chemical catalysis, highlighting both current and emerging experimental techniques. It examines how AI-driven methodologies enhance data analysis, automate complex decision-making processes, and optimize catalyst design for industrial applications. The future of research laboratories is envisioned as autonomous, self-driven environments that streamline and accelerate the transition from conceptualization to practical implementation. Key challenges, including data quality, model interpretability, and the scalability of industrial applications, are critically analyzed. Future research should focus on addressing these challenges through strategic methodologies, establishing a systematic framework to fully harness the potential of artificial intelligence and high-throughput experimentation. These advancements will enhance research efficiency and drive innovation in catalysis.

Data-driven process monitoring methods are widely used in industrial tasks, with visual monitoring enabling operators to intuitively understand operational status, which is vital for maximizing industrial safety and production efficiency. However, high-dimensional industrial data often exhibit complex structures, making the traditional 2D visualization methods ineffective at distinguishing different fault types. Thus, a visual process monitoring method that combines supervised uniform manifold approximation and projection with a label assignment strategy is proposed herein. First, the proposed supervised projection method enhances the visualization step by incorporating label information to guide the nonlinear dimensionality reduction process, improving the degrees of class separation and intraclass compactness. Then, to address the lack of label information for online samples, a label assignment strategy is designed. This strategy integrates kernel Fisher discriminant analysis and Bayesian inference, assigning different label types to online samples based on their confidence levels. Finally, upon integrating the label assignment strategy with the proposed supervised projection method, the assigned labels enhance the separability of online projections and enable the visualization of unknown data to some extent. The proposed method is validated on the Tennessee Eastman process and a real continuous catalytic reforming process, demonstrating superior visual fault monitoring and diagnosis performance to that of the state-of-the-art methods, especially in real industrial applications.

Natural esters exhibit excellent flame retardant and biodegradability, which help minimize power accidents and reduce environmental impact. These qualities make natural esters a promising alternative to conventional transformer insulating oils. However, the practical applications of natural esters in power equipment have been significantly restricted by their inherent limitations, including elevated viscosity, high dielectric loss, and poor oxidative stability. Nano-modification technologies present a novel methodological approach to solve these inherent constraints. A systematic analysis of the latest research developments in nano-modified natural ester transformer oils is provided in this review. The properties of various natural esters are examined, and their suitability as base fluids is evaluated, while the modification effects and mechanisms of typical nano-additives are comprehensively reviewed. The key role of nano-modification technology in improving the overall performance of natural esters is elucidated through detailed analysis of how nanoparticles influence physical properties, dielectric properties, and oxidative stability. In addition, the practical challenges facing nano-modification technology are addressed, providing valuable theoretical guidance for future developments in this field.

Zinc-bromine flow batteries are considered as one of the most promising energy storage devices with high energy density and low production price. However, its practical application is hampered by the short cycle life, which is mainly due to the uneven zinc deposition and the shuttle effect of bromide ions. Modification of membranes, an important part of zinc-bromine flow batteries, is a common approach to address these issues. In this study, inspired by the adhesion mechanism of filament proteins secreted by marine mussels, we propose a novel method for modifying polyethylene membranes with polydopamine. The self-polymerization of dopamine on a polyethylene membrane surface is simple and mild compared to traditional methods. This dopamine-based modification enhances the hydrophilicity of polyethylene membrane, improves ion transport, and reduces the pore size of the membranes, effectively blocking bromine ion shuttling. Additionally, polydopamine modification promotes uniform zinc deposition, further improving the battery performance. Consequently, the resulting PDA@PE-24 membrane demonstrates a significant improvement in both voltage and energy efficiencies, reaching 83.5% and 79.7%, respectively, under 20 mA·cm⁻², compared to the 80.3% and 76.5% voltage and energy efficiencies, respectively, for unmodified polyethylene membranes. Furthermore, the cycle life of a single cell increased 4-fold, operating continuously for more than 2000 h.

In this work, a novel nitrogen-doped biochar-supported nanoscale ferrous sulfide composite (nFeS@NBC) was fabricated by pyrolyzing corn straw pretreated with Mohr’s salt through a one-step carbothermic reduction process, which was applied in the efficient disposal of hexavalent chromium (Cr(VI))-containing wastewater. The key effects of impregnation ratio and pyrolysis temperature on the properties and removal performance of nFeS@NBC for Cr(VI) were subsequently investigated. The properties of nFeS@NBC were characterized through a series of techniques. It indicated that FeS nanoparticles were successfully loaded and −NH₂ functional groups effectively formed on the biochar surface, which enhanced the removal performance of nFeS@NBC for Cr(VI) from wastewater. The removal performance of nFeS@NBC for Cr(VI) was systemically evaluated at different experimental conditions and in the presence of major co-existing ions. Adsorption kinetics was best suited to the pseudo-second-order model. Additionally, Langmuir isotherms model could well explain the adsorption experiment data for the removal of Cr(VI) by nFeS@NBC with the highest adsorption capacity of 373.85 mg·g⁻¹. According to the thermodynamic study, nFeS@NBC dominated the adsorption of Cr(VI) through an endothermic and spontaneous process. The adsorption and reduction served as the main removal mechanisms of nFeS@NBC for aqueous Cr(VI). nFeS@NBC could be used repetitively for its regeneration. Thus, the above results showed that it was feasible and efficient to remove Cr(VI) by nFeS@NBC, providing a potential green material for environmental remediation.