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.

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.

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.

Particle formulation engineering stands as a focal point of research and a critical trajectory within the chemical industry. In response to the challenges associated with antigen/drug delivery, our research group has proposed a suite of strategies centered on micro/nanoparticle platforms. This review integrates our investigations into the applications of particles across various dimensions in biomedical delivery systems. Specifically, it delineates the mechanisms by which particles augment vaccine-induced immune responses, notably through antigen cross-presentation, and the pivotal roles they play in facilitating drug-mediated targeting of cancer cells via confined mass transfer. This review also encompasses recent advancements in particle formulations, offering prospective insights into the utilization of chemical engineering principles in the design of next-generation biomedical delivery systems.

Four different chelating agents, ethylenediamine tetraacetic acid, citric acid, glucose, and sucrose, were selected to synthesize MnCr₂O₄ catalysts (spinel structure) with sol-gel method. Among the prepared catalysts, MnCr₂O₄-S-700, which had the largest specific surface area, showed the best catalytic performance, with a T80 temperature window of 200–260 °C and a denitrification rate of up to 91.6% at 220 °C. Hydrogen temperature programmed reduction, ammonia temperature programmed desorption, and X-ray photoelectron spectroscopy results showed that MnCr₂O₄-S-700 possessed more chemisorbed oxygen O_α as well as active sites (Mn³⁺ + Mn⁴⁺) and (Cr³⁺ + Cr⁵⁺), which improved acidity and redox capacity. There was abundant electron transfer between Mn and Cr elements (Cr⁵⁺ + Mn³⁺ → Cr³⁺ + Mn⁴⁺), enhancing the redox capacity of catalysts. According to the in situ diffuse reflectance infrared transform spectroscopy spectra, it could be concluded that the MnCr₂O₄-S-700 catalyst followed not only the Langmuir-Hinshelwood mechanism but also the Eley-Rideal mechanism. This work displays the effect of the complexation mechanism of chelating agents on the SCR reaction with NH₃ over spinel catalysts.

The need for efficient energy storage systems has promoted the development of supercapacitors. Researchers have recently focused on building hybrid supercapacitors and synthesizing electrode materials using ecological and easily scalable methods. This work presents the development of hybrid supercapacitors based on cobalt ferrite-carbon composite. The spinel ferrite was synthesized by co-precipitation followed by heat treatment, and a ferrite-glucose precursor was used to obtain a mesoporous composite with a specific surface area of 41.195 m²·g^–1. Adding carbon does not structurally modify the cobalt ferrite but significantly improves the electrochemical properties. The electrochemical characterization in a three-electrode cell yielded a maximum specific capacitance of 548.1 F·g^–1 at a current density of 14.5 A·g^–1. The composite was mixed with sustainable activated carbon in different proportions to assemble solid-state hybrid supercapacitors. A maximum specific capacitance and energy of 69.8 F·g^–1 and 27.9 Wh·kg^–1 were obtained with a symmetric 1.2 V device, corresponding to a specific power of 94 W·kg^–1. These results show that it can develop hybrid supercapacitors based on the CoFe₂O₄-C composite, synthesized by a simple, low-cost, and environmentally friendly method.

Single-atom alloy catalysts represent a novel and advanced category of materials in heterogeneous catalysis, attracting considerable interest in electrochemical power storage and utilization because of the distinctive structural attributes and remarkable catalytic capabilities. By establishing atomically precise arrangements of catalytic centers on metallic surfaces, single-atom alloy create highly efficient active sites with near-perfect atomic utilization. The robust electronic coupling and geometric interactions between the atomic-scale precision sites and the supporting metal matrix impart exceptional catalytic properties, such as improved kinetic performance, precise molecular recognition, and prolonged operational durability. In essence, the structural integrity of the isolated metal active sites in single-atom alloy, combined with their precisely tunable coordination environments, substantially boosts the electrochemical performance and catalytic efficiency. This review begins by introducing and discussing the fundamental concepts and inherent attributes of single-atom alloy. The methodological framework for single-atom alloy development was systematically examined, encompassing architectural design principles, fabrication methodologies, and analytical characterization techniques. Following this, the comprehensive summarization was conducted regarding the implementation of single-atom alloy catalysts in energy transformation technologies, with specific emphasis on fuel cells and environmentally electrochemical processes. Finally, forward-looking insights and perspectives are presented on the current challenges facing the development of single-atom alloy catalysts.

This study evaluates the techno-economic feasibility and environmental implications of integrating first-generation (1G) and second-generation (2G) bioethanol co-production using wheat grain and wheat straw (WS) as feedstocks. Three pretreatment methods—formic acid, sodium chlorite, and alkaline hydrogen peroxide (AHP)—were investigated, with AHP identified as the most industrially viable due to its mild conditions, high cellulose retention (73%), and reduced wastewater generation. The results indicated that the integrated 1G + 2G process exhibited high bioethanol production capacity (241300 t·y^–1) and mass yield (22.74%) under the conditions of 1200 t·d^–1 of wheat and 2000 t·d^–1 of WS. Furthermore, an energy recovery potential of 60.51%, alongside a 60.65% reduction in CO₂ emissions could be achieved. 1G + 2G process has a competitive minimum ethanol selling price (MESP: $431·t^–1), high internal rate of return (37%), and return on investment (76%). Life cycle assessment highlighted terrestrial ecotoxicity potential (35%) and freshwater ecotoxicity potential (32%) as dominant environmental impacts, driven by nitrogen fertilizer use and fuel combustion efficiency. Sensitivity analysis showed feedstock costs and ethanol pricing as critical economic drivers, while reducing nitrogen fertilizer application and optimizing combustion efficiency were key to mitigating environmental burdens. This work provides actionable insights for advancing integrated biorefineries with enhanced yield, economic viability, and sustainability.