Frontiers of Chemical Science and Engineering最新文献

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.

(Sc₂O₃)_0.1(CeO₂)_0.01(ZrO₂)_0.89 possesses excellent ionic conductivity among various stabilized ZrO₂ electrolyte materials for solid oxide fuel cells. However, its practical application is limited by susceptibility to phase transition and the high cost of Sc₂O₃ raw material. Herein, we address these challenges by partially replacing Sc₂O₃ in (Sc₂O₃)_0.1(CeO₂)_0.01(ZrO₂)_0.89 with lowcost Yb₂O₃. Quaternary (Yb₂O₃)_x(Sc₂O₃)_0.10−x(CeO₂)_0.01 (ZrO₂)_0.89 (x = 0.04–0.10) electrolyte discs are fabricated by coupling tape casting and in situ solid-state reaction. All Yb₂O₃ doped electrolytes exhibit a single cubic phase structure. With increasing in Yb₂O₃ amount, the grain boundary resistance decreases, leading to improved conductivity at low temperatures. (Yb₂O₃)_0.06(Sc₂O₃)_0.04 (CeO₂)_0.01(ZrO₂)_0.89 exhibits the ionic conductivity of 0.088 and 0.0020 S·cm⁻¹ at 800 and 500 °C, respectively. In addition, both the thermal expansion coefficient and three-point bending strength of the electrolytes increase with higher Yb₂O₃ amount, satisfying the criteria for advanced electrolyte materials in solid oxide fuel cells. A single cell configuration comprising a Ni-Gd_0.2Ce_0.8O_1.9 anode∣200 µm thick (Yb₂O₃)_0.06(Sc₂O₃)_0.04(CeO₂)_0.01 (ZrO₂)_0.89∣La_0.6Sr_0.4Co_0.2Fe_0.8O₃ cathode achieves a peak power density of 0.65 W·cm⁻² at 800 °C and operates stably for 100 h without noticeable degradation. The present findings provide a new approach for the development of cost-effective and highly conductive ZrO₂-based electrolyte for efficient and durable solid oxide fuel cells.

Utilizing artificial intelligence to assist in the development of green processes for alcohol oxidation is a challenging and time-consuming task due to the lack of massive data and adequate optimization objectives. To solve these challenges, our work presents a hybrid surrogate model for iso-octanol oxidation to iso-octanal, integrating data-driven approaches with chemical equations grounded in mass transfer, heat transfer, momentum transfer, and reaction engineering, to enhance problem-solving efficiency. Specifically, a precise mechanistic model based on Aspen Plus generated database is developed to enhance the utility of experimental data, thereby overcoming the challenge of scarce oxidation experimental data caused by long operating cycles and hydrogen safety concerns. Based on this database, integrating machine learning techniques and intelligent optimization algorithms can quickly determine the optimal operating conditions for the iso-octanol oxidation reaction system. Compared to direct process simulation and multi-objective optimization methods, surrogate models exhibit higher efficiency, with computational speeds exceeding 400 times than those of traditional methods. The optimization results reveal significant reductions in both primary energy demand and greenhouse gas emissions, underscoring the effectiveness of the optimized solutions. Our work not only propels realtime optimization of alcohol oxidation production processes but also lays the groundwork for their widespread industrial application.

Structured catalysts hold considerable promise for catalytic distillation due to their enhanced mass transfer, robust mechanical/thermal stability, and facile recyclability. However, conventional synthesis methods suffer from uncontrolled bulk nucleation in the liquid phase, leading to low loading efficiency and limiting practical use. Herein, this study developed a microwave-assisted hydrothermal method for the in situ growth of NaA zeolite coatings on silicon carbide (SiC) foams. The strong microwave absorption of SiC induces localized overheating, which promotes directed crystal growth on the SiC surface while minimizing solution-phase crystallization. A silica sol pretreatment method was employed to address support dissolution and facilitate the rapid construction of a dense zeolite layer, achieving a mass variation of 1.11 after only 5 cycles, which was not attainable with other pretreatment methods under identical conditions. The resulting coating exhibited excellent adhesion, with a minimal mass loss of 0.62% under rigorous ultrasonic and solvent-flushing tests. In aldehydeketone condensation reactions, the structured catalyst maintained a high yield (> 90%) over three cycles. The reusability of the NaA@SiC structured catalysts, combined with uniform crystalline coatings, offers a promising approach to decrease raw materials consumption in future manufacture and applications of structured catalysts.

Soft measurement based on data-driven models is an important method to predict key variables in process industry due to low latency demand and economics costs. However, data-driven models cannot provide accurate prediction on a noisy data set with a small number of samples. In response to the challenge of noisy data and lack of samples, several data-mechanism hybrid driven methods are proposed to improve key variables prediction performances on the basis of three data-driven models including random forest, extreme gradient boosting, and artificial neural network. Simultaneously, the effectiveness of hybrid driven methods proposed is validated via two cases including benzene-toluene-xylene distillation and steam methane reforming process, where data sets feature different sample sizes and noise intensity. The comparison results show that the hybrid driven methods can improve the prediction accuracy to a certain extent. The degree of improvement depends on the noise intensity, sample size, and data-driven model selected. Under conditions of noise intensity at 10%–20% and sample size ranging from 100 to 400 in this work, after adopting the hybrid driven methods, the coefficient of determination for random forest, extreme gradient boosting, and artificial neural network can be improved by 0.3%–5.2%, 0.6%–17.7%, and 0.1%–36.2% compared to corresponding data driven models.

Microplastics, as persistent organic pollutants, are widely present in aquatic environments. Owing to their small size and tendency to adsorb other pollutants, traditional wastewater treatment processes struggle to effectively remove them, and they pose an increasingly serious threat to ecosystems and human health. Therefore, efficient, stable, and feasible treatment technologies to effectively collect or separate microplastics from wastewater are urgently needed. The continuous development of electrochemical technology, with its advantages of high efficiency, ease of operation, and controllability, has garnered significant attention and is being explored as a viable solution to water treatment challenges. Electrochemical technologies have also demonstrated good removal efficiency and potential prospects with regard to their application to remove microplastics from wastewater; however, systematic implementation guidelines to facilitate its commercialization are lacking. This review summarizes existing research on the use of five electrochemical technologies (electrocoagulation, electrooxidation, electroreduction, bioelectrochemistry, and electrosorption) for microplastics removal, and discusses their removal performance, influencing factors, and degradation mechanisms when used to treat microplastics in wastewater. Additionally, the advantages of combining electrochemical technologies with other methods for efficient microplastics removal are briefly described, with the goal of assessing the practical feasibility and future application trends of electrochemical methods for removing microplastics from wastewater.

A rotating gliding arc (RGA) device driven by synergistic flow and magnetic fields was developed for enhanced nitrogen fixation. The effects of flow field distribution, magnetic field intensity, and N₂/O₂ ratio on fixation performance were investigated. A uniform tangential inlet improved arc stability, suppressed reverse breakdown, and extended the operating range of the RGA, resulting in the highest fixation efficiency. At an air flow rate of 3 L·min⁻¹, the device achieved an NO_x concentration of 7623 ppm in the effluent, with an energy cost as low as 3.6 MJ·mol⁻¹. This configuration also enhanced plasma non-equilibrium, promoting nitrogen excitation and reactive species generation. Increasing magnetic field strength improved efficiency up to 200 mT, beyond which gains plateaued. An N₂/O₂ ratio of 6:4 yielded optimal nitrogen excitation and fixation performance.