A Robust Framework for Generating Adsorption Isotherms to Screen Materials for Carbon Capture

Area of Science:

• Materials Science
• Chemical Engineering
• Computational Chemistry

Background:

• Accurate prediction of material performance is essential for effective carbon capture.
• Current methods for predicting mixture isotherms often rely on fitting experimental data, which can be unreliable.
• Molecular simulations are increasingly used for high-throughput material screening.

Purpose of the Study:

• To develop an efficient and automated workflow for predicting pure component isotherms.
• To evaluate the accuracy and reliability of Ideal Adsorbed Solution Theory (IAST) for predicting mixture isotherms.
• To demonstrate the impact of thermodynamic modeling choices on material ranking for carbon capture processes.

Main Methods:

• Developed an automated workflow for meticulous sampling of pure component isotherms.
• Utilized the Clausius-Clapeyron relation to predict isotherms at various temperatures from a reference isotherm.
• Applied Ideal Adsorbed Solution Theory (IAST) to predict CO2 and N2 mixture isotherms.
• Compared IAST predictions with analytical models like dual-site Langmuir (DSL).

Main Results:

• The automated workflow proved reliable for predicting pure component isotherms of metal-organic frameworks (MOFs) with different guest molecules.
• Coupling the workflow with the Clausius-Clapeyron relation efficiently predicted pure component isotherms at desired temperatures.
• IAST accurately predicted CO2 and N2 mixture isotherms across a range of conditions without fitting experimental data.
• Material rankings for a temperature swing adsorption (TSA) process varied significantly based on the thermodynamic method used for mixture isotherm prediction.

Conclusions:

• The developed workflow provides an accurate, reliable, and robust method for generating isotherm data.
• IAST is a superior numerical tool for predicting binary adsorption uptakes compared to analytical models, bridging raw adsorption data and process modeling.
• Commonly used methodologies for predicting mixture isotherms can lead to incorrect material rankings, misidentifying up to 33% of top-performing materials for low-concentration CO2 capture.