Developing efficient, stable, and inexpensive catalysts for the preferential CO oxidation in H₂ (CO-PROX) over a wide temperature range in the presence of CO₂ and H₂O is indispensable for the hydrogen purification process. Herein, CuO was introduced to the CeMnO₂-supported Pt catalyst to modulate the oxygen activation capacity and provide the available number of active sites in CO-PROX. One part of the CuO species doped into CeO₂ strongly interacts with Ce, thus enhancing the oxygen transfer capacity of the catalyst. The other part of CuO species located on the surface of the catalyst provides extra Cu⁺ sites available for low-temperature CO adsorption. This synergistic interaction with Pt sites further enhances CO and O₂ activation, broadening the temperature window of high activity. The optimal Pt-10CuO/CeMnO₂ catalyst exhibits complete CO conversion (CO/O₂ ratio of 1:1) within the practical temperature range of 130–190 °C, even in the presence of CO₂ and H₂O, and remains stable at 150 °C for 76 h testing without any deactivation. This work will give a novel approach for the design of highly efficient inexpensive catalysts for industrial preferential oxidation of CO in H₂, especially in the presence of CO₂ and H₂O.

We investigated the pivotal role of active center symmetry on the stability of reactant adsorption and the transition state dynamics within the context of acetylene hydrochlorination. Our innovative approach involved the integration of phosphorus into a nitrogen-doped carbon framework and introduced the single Cu center, culminating in the development of novel copper-nitrogen-phosphorus-carbon (Cu-NPC) catalysts. These catalysts are distinguished by their asymmetrical Cu₁-N₃-P-C chemical environment. Our kinetic studies shed light on the underlying mechanisms contributing to the superior performance of the Cu-NPC catalysts. These catalysts not only enhance the reaction rate by moderating the adsorption strength of reactants, thereby optimizing the reaction kinetics, but also demonstrate an outstanding ability to mitigate the risk of carbon deposition, a common challenge that compromises catalyst longevity and efficiency. This is evidenced by a notably low deactivation rate of 0.027 h⁻¹ at a high C₂H₂ weight hourly space velocity (WHSV) of 1.4 ${\text{g}}_{{\text{C}}_2{\text{H}}_2}\cdot {{\text{g}}_{\text{cat}}}^{-1}\cdot {\text{h}}^{-1}$. This research not only advances our understanding of the critical influence of active center symmetry on catalyst performance but also paves the way for the rational design of advanced catalysts tailored for specific industrial applications.

With the continuous development of the chemical industry, the concept of advocating green development has become increasingly popular. Glycolic acid (GA), serving as the monomer for biodegradable plastic polyglycolic acid, plays a crucial role in combating plastic pollution and fostering an eco-friendly society. The selective oxidation of ethylene glycol (EG) to produce GA represents a novel green production technology. Controlling reaction parameters to achieve multi-objective optimization of product distribution and direct CO₂ emissions is crucial for scaling up the process. With the advent of the big data era, the integration of the chemical industry with artificial intelligence to achieve engineering scale-up is an important trend. This study proposes a neural network model for production prediction and optimization. The model is trained using experimental data, reaction mechanism data, and physical information, enabling rapid prediction of GA production. After validating with 40% of experimental data and 16% of reaction mechanism data, the model's prediction error was within ±5%, and the linear correlation coefficient R² between the predicted values and actual values was 0.998. Furthermore, this study integrated a multi-objective optimization algorithm based on the model, enabling surrogate optimization of reaction parameters during production. After optimization, the direct CO₂ emissions were reduced by over 99% and overall greenhouse gas emissions were reduced by 4.6%. The research paradigm proposed in this research can offer guidance and technical support for the optimized operation of EG selective oxidation to GA.

Activated carbon-supported HgCl₂ catalysts have seriously impeded the development of the polyvinyl chloride (PVC) industry due to the sublimation of Hg species and environmental pollution problems. Herein, the template-free and organic solvent-free strategy was devised to synthesize non-metallic based nitrogen-doped carbon (U-NC) sphere catalyst for acetylene hydrochlorination. This green strategy via ultrasonic chemistry initiates resin crosslinking reactions between aminophenol and formaldehyde resin by free radicals, leading to the ultra-rapid formation of U-NC with remarkably high pyrrolic N content in only 5 min. This U-NC catalyst exhibited an outstanding space-time-yield (1.6 g_VCM·g_cat⁻¹·h⁻¹), even comparable to the reported metallic catalyst. By combining kinetic analysis, advanced characterizations, and density functional theory, it is found that the amount of pyrrolic N is in linear with C₂H₂ conversion, and pyrrolic N in U-NC can effectively improve acetylene hydrochlorination performance by mediating HCl adsorption. This work sheds new light on rationally constructing metal-free catalyst for acetylene hydrochlorination.

Upgrading of vacuum residue is of prime industrial significance due to the increasing demand for light oils. Elucidating the effect of catalyst morphology on vacuum residue hydrotreating performance by kinetic modeling is therefore of great importance. Herein, kinetic analysis of hydrodemetallization (HDM) and hydrodeconradson-carbon-residue (HDCCR) performances on industrial Ni–Mo/Al₂O₃ catalysts with spherical and cylindrical morphologies in ebullated-bed were evaluated for more than 1600 h. It was found that the percentage of light impurities easier to be removed on spherical catalysts were 78.20% and 39.43% in HDM and HDCCR reactions, respectively, higher than 65.20% and 17.50% on cylindrical catalysts. This suggests that catalyst morphology affects the impurity removal ability and the impurity properties, resulting in better hydrotreating performance of spherical catalysts. This work not only combines catalyst morphology with impurity removal capability through kinetic modeling, but also provides new insights into the design of efficient hydrotreating catalysts.