Recent advances and artificial neural network-response surface methodology-based predictive modeling of CO2 adsorption in 3D triptycene-based nanoporous materials

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J. Mater. Chem. A, 2026, Accepted ManuscriptDOI: 10.1039/D5TA10540D, Review ArticleNazrul Hsan, Santosh Kumar, Imran Atique, Neeladri Das, Joonseok KohTriptycene-based porous materials are promising candidates for CO₂ capture and separation due to their uniqu…

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Recent advances and artificial neural network-response surface methodology-based predictive modeling of CO2 adsorption in 3D triptycene-based nanoporous materials

Abstract

Triptycene-based porous materials are promising candidates for CO₂ capture and separation due to their unique combination of rigid three-dimensional paddlewheel geometry, intrinsic free volume, and tunable functionality. The non-planar triptycene core frustrates close π–π stacking and generates permanently open frameworks with high surface areas, interconnected micropores, and excellent thermal and chemical stability. Recent synthetic advances (Friedel–Crafts polymerization, triazine condensation, tailored crosslinking strategies, etc.) have enabled the systematic tuning of porosity and chemistry, yielding materials with CO₂ adsorption capacities up to ~5.8 mmol g⁻¹ and attractive selectivity over N₂ and CH₄. In parallel, data-driven approaches are increasingly used to rationalize and predict CO₂ adsorption in triptycene-based POPs. In particular, quadratic response surface methodology (RSM) and artificial neural networks (ANNs) have been applied to comprehensive experimental datasets to quantify how the Brunauer–Emmett–Teller surface area, total pore volume, pore diameter, and operating temperature and pressure govern CO₂ uptake. The RSM models provide interpretable polynomial equations and response surfaces that elucidate the key effects and interactions, whereas ANN architectures capture higher-order nonlinearities and deliver enhanced predictive accuracy suitable for screening and optimization. This review summarizes recent advances in the design, synthesis, and gas sorption performance of three-dimensional triptycene-based POPs and membranes. Moreover, these advances are integrated with predictive modeling based on ANN–RSM to build unified structure–property–performance relationships. Finally, we outline the remaining challenges and future opportunities for combining molecular engineering, high-throughput experiments, and machine learning-guided optimization to translate triptycene-derived materials into scalable, process-relevant technologies for carbon capture, utilization, and storage.

• This article is part of the themed collection: Journal of Materials Chemistry A Recent Review Articles