Recent advances and artificial neural network-response surface methodology-based predictive modeling of CO 2 adsorption in 3D triptycene-based nanoporous materials

Journal of Materials Chemistry A 2026

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Authors: Nazrul Hsan, Santosh Kumar, Imran Atique, Neeladri Das, Joonseok Koh
DOI: 10.1039/d5ta10540d
Cited by: 3
References: 135

Abstract

Triptycene-based materials combine rigid 3D geometry, intrinsic free volume, and tunable functionality for CO 2 capture and separation. ANN–RSM modeling links porosity and operating conditions to uptake and guides predictive materials design.

Fields of Study & Subjects

Covalent Organic Framework Applications Carbon Dioxide Capture Technologies Metal-Organic Frameworks: Synthesis and Applications

Publication details

Journal / source
Journal of Materials Chemistry A
Publisher
Royal Society of Chemistry (RSC)
Volume
14
Issue
32
Pages
20714–20746
Publication date
1 January 2026
Language
EN