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ABSTRACT Porous polymers, particularly polymers of intrinsic microporosity (PIMs), combine high surface areas with tunable functionalities, positioning them as promising materials for post‐combustion CO₂capture technologies. A key physical parameter of these materials is the isosteric heat of adsorption (Q_st), which quantifies the interaction strength between CO₂molecules and the polymer framework. In this work, we demonstrate that data‐driven models constructed from computationally determined physicochemical descriptors can accurately predictQ_stat room temperature using a modestly sized dataset of 75 PIMs. Among the multitude of machine learning models evaluated, Kernel Ridge Regression (KRR) utilizing the radial basis function (RBF) yielded notable training and testing coefficients of determination (R²scores) of 0.9854 and 0.9653, respectively. Additionally, an ensemble model was constructed using the KRR RBF, Lasso Regression, and XGBoost, achieving an average training R²score of 0.9844 and a testing score of 0.9651. By optimizing model parameters through Bayesian methods and interpreting feature importance using SHAP analysis, we identified the molecular characteristics that most strongly influence the prediction of CO₂‐PIMs isosteric heats of adsorption. Using a Lasso model, we screened a synthetic PIM parameter space by varying six thermochemical descriptors, revealing density as the most influential factor and identifying optimal combinations to enhance predicted isosteric heats of adsorption. These results demonstrate that accurate predictive workflows can be developed using relatively small datasets for designing polymeric adsorbents by identifying key molecular descriptors that correlate with the isosteric heat of adsorption. Overall, this adaptable methodology can be extended to future gas‐separation challenges, helping pave the way for faster discovery of advanced polymeric membranes for capturing CO₂. 

The separation of xylene isomers still remains an industrially challenging task. Here, porous purine-based metal–organic frameworks (MOFs) have been synthesized and studied for their potential in xylene separations. In particular, Zn(purine)I showed excellent para -xylene/ ortho -xylene separation capability with a diffusion selectivity of 6 and high equilibrium adsorption selectivity as indicated by coadsorption experiments. This high selectivity is attributed to the shape and size of the channel aperture within the rigid framework of Zn(purine)I. 

We report the synthesis and structure of single-walled aluminosilicate nanotubes with microporous zeolitic walls. This quasi-one-dimensional zeolite is assembled by a bolaform structure-directing agent (SDA) containing a central biphenyl group connected by C 10 alkyl chains to quinuclidinium end groups. High-resolution electron microscopy and diffraction, along with other supporting methods, revealed a unique wall structure that is a hybrid of characteristic building layers from two zeolite structure types, beta and MFI. This hybrid structure arises from minimization of strain energy during the formation of a curved nanotube wall. Nanotube formation involves the early appearance of a mesostructure due to self-assembly of the SDA molecules. The biphenyl core groups of the SDA molecules show evidence of π stacking, whereas the peripheral quinuclidinium groups direct the microporous wall structure.