Atomic connectivity group contribution (ACGC) is a method developed by atomic adjacent group (AAG), shape factors and atomic connectivity factors (ACFs) for property prediction. As a crucial parameter for dealing with the challenge of accurately predicting properties of isomers, ACFs are defined for describing the global position of each group in a molecule. In this work, ACFs plus (ACFs+) is proposed to describe the local position of a group by considering the contribution of the core atom in AAG and nearby atoms. As such, ACGC plus (ACGC+) models are developed with ACFs+ to predict key phase transition properties of organic compounds (i.e., Δ_fusS, Δ_vapH°, Δ_subH°, Δ_fusH, T_b, T_m, T_c, P_c, and V_c). Both predictability and robustness are rigorously validated using external validation and cross-validation. The R²_test values for phase transition entropy and enthalpy range from 0.906 to 0.992, the R²_test values for critical properties are greater than 0.989, and the R²_test values for T_b and T_m are 0.979 and 0.845, respectively, which indicate high predictability of ACGC + models. The R² values for all properties are close to the R²_train values, which further validates the stability of the ACGC+ model. Furthermore, the mean absolute errors of the ACGC+ models decreased by 1.44%–7.91% compared to the ACGC models. These results demonstrate that the ACGC+ method provides high accuracy in predicting the properties of phase transitions.

Foam generation is a common occurrence in industrial processes involving liquids, gases, surfactants under agitation. In fermentation, uncontrolled foaming poses significant challenges, including broth loss, microbial contamination, and reduced product yields. Foam control agents (FCAs) are commonly employed to mitigate these issues without adversely affecting microbial viability. However, the performance of FCAs is highly sensitive to both manufacturing and application conditions, necessitating evaluation methods that closely replicate real-world fermentation environments. In this study, a commercial fermentation broth was used as the foaming medium, with continuous airflow applied using FOAMSCAN^TM equipment to simulate industrial conditions. The foam suppression efficiency and microbial compatibility of various polyglycol based FCAs—differing in molecular shape, cloud point, surface tension, viscosity, and specific gravity—were systematically investigated. Predictive models were developed to accurately estimate foam volume and microbial growth, offering valuable insights into the performance and biological safety of FCAs in fermentation processes.

CO₂ cycloaddition with epoxides for producing cyclic carbonates is a promising route that aligns with environmental sustainability and economic feasibility. Metal oxyhalides, which contain inherent lattice halogen ions that can act as built-in nucleophiles, offer a distinctive strategy of designing efficient, heterogeneous catalysts for CO₂ cycloaddition. Herein, a series of highly dispersed Ce-doped BiOI (Ce_xBi_1–xOI) catalysts were developed via a one-pot solvothermal method. The introduction of oxyphilic Ce ions enhances the adsorption and activation of epoxides, thus improving the catalytic performance in CO₂ cycloaddition. Experimental characterizations reveal that Ce doping facilitates the formation of oxygen vacancy-mediated Lewis acid-base pairs (Bi–O_v–Ce³⁺···I⁻). In this configuration, the O_v–Ce³⁺···I⁻ strengthens the epoxides adsorption and subsequent epoxy ring-opening, while the Bi–O_v supplies CO₂ adsorbed site. This synergistic interaction reduces the apparent activation energy (70.67 kJ∙mol^–1 for Ce_0.1Bi_0.9OI vs. 108.09 kJ∙mol^–1 for BiOI) of the CO₂ cycloaddition with butylene oxide. Under solvent- and cocatalyst-free conditions, the optimized Ce_0.1Bi_0.9OI catalyst achieved a butylene carbonate yield of 91%. This work provides a feasible strategy for designing efficient catalysts of CO₂ conversion by constructing reinforced synergistic Lewis acid-base sites.

The performance degradation of modular devices during scaling up necessitates rational design of the integration structure. However, its complex structure makes it challenging to reveal the mechanism of the effect of hierarchical multi-scale structural parameters on performance. This study proposes a data-driven framework to analyze structure-performance relationships and identify optimal scale-up patterns, using a CO₂ reduction microreactor as a case study. A quantitative relationship between structure and performance is established using extreme gradient boosting tree combined with the Shapley additive explanations analysis, elucidating the regulatory mechanisms of structural parameters on performance. While a classification model is utilized to define the criteria for identifying optimal structures. Additionally, optimal scale-up design patterns under various scenarios are uncovered using K-means clustering. The results indicate that Small-sized few-stack parallel structures and large-sized single-stack structures s are the scaling-up patterns that can balance cost and performance. This approach provides important insights for the industrial scale design of modular devices.

Plasma-based gas conversion has emerged as increasingly prominent sustainable technology for chemical production, offering significant advantages such as mild operating conditions, instantaneous control, and flexibility in scales. However, the inherent complexity of its multidimensional parameter space makes traditional experimental optimization resource-intensive. Machine learning (ML) presents a transformative method to efficiently explore such intricate scientific phenomena, yet its application in the field remains in its infancy. Current efforts are constrained by fragmented, small-scale experimental datasets that lack standardization across different reactor configurations and measurement protocols. Data quality issues, inconsistent reporting of performance metrics, and the absence of critical plasma and catalyst descriptors further hinder model development. Consequently, most ML studies are limited to simple predictive models that interpolate within narrow operational domains, offering little generalizability or mechanistic insight. This critical review provides a comprehensive analysis of ML methodologies applied to plasma-based gas conversion, using CO₂ conversion as a base case. We outline the general ML workflow and key algorithms, discuss their applications with state-of-the-art examples, and critically evaluate current limitations. Finally, we identify emerging challenges and future opportunities to guide the field toward more robust, generalizable, and physically as well as chemically meaningful ML applications.

Aqueous zinc-ion hybrid capacitors (ZIHCs) are promising for sustainable energy storage, but their specific capacity is severely limited by the kinetic and capacity mismatch between capacitive carbon cathodes and zinc anodes. Herein, we present a synergistic space-confinement and activation strategy to synthesize hierarchical porous carbon nanosheets (CMK-x, x represents the pyrolysis temperature) derived from coal tar pitch. The optimized CMK-700 features a high specific surface area of 2223.9 m²·g⁻¹ and a maximized ultramicropores volume of 0.4836 cm³·g⁻¹. Combined ex-situ characterization and molecular dynamics simulations, a unique dual-ion relay storage mechanism enabled by spatial domain separation across different pore structures was unraveled. Specifically, the larger [Zn(H₂O)₆]²⁺ ions are stored in larger micropores (> 1 nm) at a voltage window of 0.3–1.9 V, while smaller hydrated protons ([H₃O]⁺) act as ‘charge relays’ to penetrate the ultramicropores below 0.3 V. Such a proton accommodation through reversible chemical hydrogen adsorption/desorption contributes significant surface pseudocapacitance. Consequently, the assembled ZIHCs deliver an impressive high specific capacity of 368.1 mAh·g⁻¹ at 0.5 A·g⁻¹ and outstanding long-term stability with 86.39% capacity retention after 21000 cycles. This work provides new insights into designing high-performance ZIHC cathodes through the strategic integration of a dual-ion relay storage mechanism.