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

Key Points

• This research aims to enhance predictive modeling techniques for CO₂ adsorption using triptycene-based nanoporous materials.
• Utilized artificial neural networks for predictive modeling of CO₂ adsorption.
• Employed response surface methodology to analyze data and optimize outcomes.
• Examined triptycene-based porous materials for their structural properties.
• Achieved improved accuracy in predicting CO₂ adsorption efficiency.
• Demonstrated the significant role of triptycene structure in enhancing CO₂ capture capabilities.
• Identified optimal structural parameters for maximizing CO₂ adsorption.