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.

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.