Data-Driven Evaluation for Multicriteria Selection of Covalent Organic Frameworks for Membrane-Based Hydrogen...

Area of Science:

• Materials Science
• Chemical Engineering
• Computational Chemistry

Background:

• Hydrogen is a key fuel for decarbonization, necessitating efficient separation technologies.
• Porous materials are crucial for hydrogen extraction, but material selection is challenging.
• Covalent organic frameworks (COFs) offer tunable properties for gas separation, particularly at high temperatures.

Purpose of the Study:

• To identify optimal covalent organic framework (COF) features and topologies for selective hydrogen separation.
• To develop a data-driven evaluation method for COF materials.
• To guide the design of advanced materials for hydrogen fuel applications.

Main Methods:

• Utilized the CoRE COF database containing 1242 reported COFs.
• Performed a data-driven evaluation using a scoring approach to quantify COF properties.
• Applied a property-based evaluation model to compare 2D and 3D COF topologies.

Main Results:

• 3D COFs demonstrated higher average potential for hydrogen separation compared to 2D COFs.
• A scoring approach identified specific COF features and topologies with high separation potential.
• Four normalized evaluations highlighted designs with the highest potential for selective hydrogen extraction.

Conclusions:

• A data-driven, interdisciplinary quantification approach effectively identifies promising COF materials.
• 3D COFs are generally more suitable for selective hydrogen separation than 2D COFs.
• This methodology aids in selecting specific COF topologies and advantageous features for hydrogen separation materials.