Frontiers of Chemical Science and Engineering最新文献

The carbonation of K₂CO₃ to KHCO₃ is an interesting CO₂ capture process due to its low material cost, high selectivity, and substantial CO₂ capacity. Traditionally, KHCO₃ is regenerated into K₂CO₃ through thermal decomposition. However, plasma-assisted decomposition presents a promising alternative, enabling not only CO₂ desorption but also the concurrent production of valuable products such as H₂ and CO. In this study, KHCO₃ particles in a size range of 250–355 µm were packed in a dielectric barrier discharge reactor and exposed to plasma. It was found that the decomposition of KHCO₃ in the plasma reactor is mainly driven by a thermal mechanism, and the decomposition rate was controlled by temperature increase via plasma heating. The energy consumption for decomposition is more than one order of magnitude higher compared to the thermal approach reported in the literature. However, production of CO and H₂ was achieved during plasma treatment, highlighting the potential advantage of an integrated CO₂ capture and utilization process, and the best CO₂ conversion and energy efficiency achieved were 9.0% ± 0.2% at 3.0% ± 0.1% with a syngas ratio of 0.35 ± 0.01.

Infinite dilution activity coefficient (γ^∞) is a key thermodynamic parameter in solvent design for chemical processes. Although conductor-like screening model for segment activity coefficient (COSMO-SAC) exhibits strong prior predictive capabilities, its estimations are sometimes only qualitative rather than quantitative. Another limitation of COSMO-SAC arises from the reliance on time-intensive quantum chemistry calculations, which restricts its scalability for large-scale solvent screening. To overcome these issues, this study integrates COSMO-SAC with machine learning for accurate γ^∞ prediction of binary mixtures. By bypassing the necessity for quantum chemistry calculations, the multi-task machine learning model could rapidly predict the surface charge density distribution (σ-profiles) and molecular cavity volume (V_COSMO) of molecules and ions, while accurately distinguishing isomers. Four adjustable parameters of COSMO-SAC are optimized using more than 20000 experimental data points of γ^∞, and residual systematic errors are further corrected with the boosting ensemble strategy to improve the model performance. The resulting hybrid model reduces the mean absolute error from 0.944 to 0.102 (R² = 0.969), representing an 89 % improvement, while preserving the physicochemical interpretability of model. This accurate and efficient approach broadens the practical applicability of σ-profiles and V_COSMO prediction, as well as γ^∞ calculations based on COSMO-SAC, facilitating the high-throughput solvent screening for diverse chemical engineering applications.

This study investigates the co-pyrolysis behavior and product distribution of peanut straw and polyethylene film blends through thermogravimetric analysis and gas chromatography-mass Spectrometry. Thermogravimetric analysis results revealed distinct pyrolysis temperature intervals: 247–356 °C for peanut straw, 448–505 °C for polyethylene film, and an extended range of 247–510 °C for their mixtures. Synergistic effects, quantified through experimental-theoretical deviations, demonstrated enhanced mass conversion rates and accelerated pyrolysis kinetics in blended systems. As the mass ratio of peanut straw to polyethylene increases from 1:1 to 1:7, the bio-oil yield increased from 62.1% to 76.86%, accompanied by elevated alkane from 20.84% to 31.41% and olefin from 24.73% to 42.89%. HZSM-5 catalyst further optimized product profiles, achieving 77.08% bio-oil yield with enhanced hydrocarbon selectivity (alkanes: 35.69%; olefins: 46.16%) while suppressing oxygenates from 20.07% to 8.85%. Carbon chain distribution analysis indicated a polyethylene ratiodependent shift toward short-chain alkanes (C6–C19), with HZSM-5 intensifying this trend through selective cracking of long-chain species (C20+). These findings establish that co-pyrolysis with catalytic intervention effectively promotes hydrocarbon production and inhibits oxygenated compounds, providing strategic insights for agricultural plastic waste valorization.

Metal-organic networks (MONs) have gained much attention due to their high surface areas and tunable structures, which underpin their potential for gas adsorption and separation. However, the stability issue remains a significant bottleneck that severely restricts their broader practical deployment. Therefore, exploring strategies to address this issue is of great significance but full of challenges. Herein, we highlight recent advances in combining MONs with polymers through surface polymerization, an approach that effectively enhances stability without sacrificing porosity, thereby enabling operation under harsher environments. Beyond stability, polymer incorporation imparts additional functions, including improved gas separation and photo-responsiveness that are inaccessible to the individual components. Finally, we propose the promising research directions of the construction of molecular sieves or stimuli-responsive functional polymer layers that leverage the merits of surface polymerization for applying MONs.

High-entropy alloys are described as materials that have equiatomic and multi-element compositions. Their unique atomic structure may provide alternative electrocatalysts for water electrolysis over traditional and expensive noble metal-based catalysts, delivering superior catalytic activity and stability. Among various high-entropy alloys synthesis methods, electrodeposition stands out as a versatile and cost-effective approach due to its mild conditions and precise control over composition and deposition properties. This review focuses on noble metalfree high-entropy alloys prepared by electrodeposition, with applications in water electrolysis. The impacts of alloying elements and electrodeposition parameters on the morphology, composition, and electrochemical performance of the resulting coatings for hydrogen evolution reaction and oxygen evolution reaction are also examined. The roles of key alloying elements are discussed, including their individual contributions during the electrodeposition process, interactions within the bath, and effects on the final coating. The review also discusses critical deposition parameters such as bath chemistry, pH value, current density, temperature, and bath agitation, and their influences on properties and electrochemical activity of electrodeposited coatings. Finally, future research directions and recommendations in several key areas are outlined to address important knowledge gaps for further advancing the optimization and application of electrode-posited high-entropy alloys as effective electrocatalysts for water electrolysis.

The co-reaction of methanol with C₅–C₁₆ n-alkanes was investigated over microsphere catalysts with varying surface acidity and ZSM-5 as the active components. The results indicate that, as the carbon number of alkanes increases, the formation of C₁–C₄ alkanes decreases while the production of C₂–C₄ alkenes increases on the catalyst with weak outer surface acidity. This suggests that side reactions such as alkene aromatization and hydrogen transfer are suppressed. Conversely, on the catalyst with strong outer surface acidity, further reaction of olefins significantly increases, leading to a gradual decrease in light olefin yield and a corresponding increase in benzene, toluene, xylene, and heavy aromatics. Additionally, it is observed that long-chain n-alkanes (the kinetic diameter of n-hexadecane exceeds the pore size of ZSM-5 zeolite, the active component in the microspherical catalyst) cannot enter the internal pores of ZSM-5, resulting in primary cracking due to the acidic sites on the outer surface. However, long-chain n-alkanes can adjust their molecular orientation on pure ZSM-5 zeolites and enter the pore structure, leading to alkane cracking influenced by both internal and external surface acidity. These findings provide valuable guidance for the design of industrial catalysts, particularly in terms of pore size and acidity.

Plasma catalysis technology is emerging as a promising approach for addressing energy and environmental challenges in sustainability. This review provides an overview of plasma technology and summarizes recent advances in plasma catalysis from both experimental and theoretical perspectives. Current laboratory-scale studies have demonstrated the versatility of plasma catalysis in various processes, including carbon conversion, hydrogen production, and the removal of volatile organic compounds. The inherently complex environment of plasma catalysis requires in situ characterization and theoretical modeling to elucidate the underlying reaction mechanisms, which in turn guide the rational design of efficient catalysts and optimized reactor configurations. These advances are vital for enhancing the economic feasibility and accelerating the commercialization of this technology. Nevertheless, the scale-up and practical deployment of plasma-catalytic systems from laboratory to industrial scales remain challenging. In this review, we critically examine the current state of plasma catalysis research and its applications across a wide range of reactions. Particular attention is given to in situ mechanistic studies, reactor design, catalyst development, process scale-up, and theoretical modeling. Finally, we provide a forward-looking perspective on the opportunities and future directions to address existing challenges and harness the potential of plasma catalysis toward sustainable development.

In the past decades, the advancement of sensor technology has enabled the development and application of reliable process analytical technology in pharmaceutical manufacturing for process monitoring and control. Soft sensors as mathematical models are often coupled with process analytical technology tools to monitor critical quality attributes such as concentrations and particle sizes. Crystallinity and polymorphism changes are prevalent phenomena in pharmaceutical manufacturing. These changes may impact drug manufacturability, drug quality, and patients’ safety. Therefore, assessment of impact of crystallinity and polymorphism change has been one critical aspect of pharmaceutical chemistry, manufacturing, and control review and inspection. Chemistry, manufacturing, and control risk assessment has been largely qualitative in nature and has heavily relied on past knowledge and experience. The potential use of soft sensors to monitor such changes and establish a science supported risk-based control strategy is not widely discussed. In this work, by applying soft sensor models to key unit operations, we presented case studies to discuss the capability and potential of soft sensors in predicting critical quality attributes for key pharmaceutical unit operations, including crystallinity and polymorphism changes in selected drugs with moderate to high solid-state risk levels in drug product manufacturing. Population balance models, semi-empirical models, and statistical correlations are applied to wet granulator, fluidized bed dryer, mill, and tablet press to enable soft sensing. Sensitivity analysis using the established models is conducted to quantitatively assess the impacts of process inputs onto different outputs to support a risk-based control strategy with regulatory insights. Knowledge, experience, and discussion in these aspects can contribute to the future development of advanced technologies and the implementation of modeling tools toward advanced manufacturing.

Medium- and low-temperature thermochemical energy storage materials are vulnerable to deliquescence, agglomeration, and structural fracturing under hyperhumid conditions, yet the fundamental origins of excess environmental moisture within reactors remain insufficiently characterized. This study systematically elucidates water vapor transport mechanisms between air and physical adsorption materials in thermochemical reactors, with emphasis on transient humidity transfer phenomena during incomplete charging and discharging cycles. Moisture saturation was defined as the key parameter for standardized humidity analysis. Results indicate that uncontrolled saturation arises from thermally driven vapor depression, in which water vapor desorbed from materials or transported by inlet air undergoes progressive condensation during downstream migration. Moisture saturation dynamics were governed by coupled effects of inlet air temperature, flow velocity, and relative humidity. Reverse charging was shown to effectively reduce maximum moisture saturation in cases where materials remained incompletely hydrated after prior discharging. Optimization of inlet air conditions through controlled transitions from low-temperature, high-velocity states to a predesigned charging protocol achieved a 45.7% reduction in maximum moisture saturation (from 1.38 to 0.75). In addition, preheating prior to discharging significantly suppressed reactor moisture saturation, thereby mitigating material failure risks.