Covalent organic frameworks for CO₂ capture: from laboratory curiosity to industry implementation

Area of Science:

• Materials Science
• Chemical Engineering
• Environmental Science

Background:

• Atmospheric CO2 concentrations have risen significantly, necessitating advanced carbon capture technologies.
• Adsorption and membrane processes are key for low-cost CO2 capture, relying heavily on sorbent and membrane materials.
• Covalent organic frameworks (COFs) offer high surface area, tunable pores, and stability, making them promising for CO2 capture.

Purpose of the Study:

• To review the development of COFs for CO2 capture, addressing the gap between materials science and engineering processes.
• To outline strategies for fabricating COFs suitable for industrial carbon capture implementation.
• To highlight the role of machine learning in integrating molecular and engineering simulations for CO2 capture.

Main Methods:

• Review of recent advancements in COF synthesis and characterization for CO2 adsorption.
• Analysis of engineering processes and industrial status of carbon capture technologies.
• Exploration of machine learning applications in materials design and process optimization for CO2 capture.

Main Results:

• COFs exhibit significant potential as efficient sorbents for CO2 capture due to their unique properties.
• Synergistic development between COF materials and engineering processes is crucial for industrial viability.
• Machine learning can accelerate the discovery and optimization of COFs and processes for carbon capture.

Conclusions:

• Bridging materials development (COFs) with engineering processes is essential for achieving net-zero emissions through effective CO2 capture.
• Fabrication strategies for industrial-scale COF application require further investigation.
• Interdisciplinary collaboration, leveraging machine learning, can accelerate the practical implementation of COF-based carbon capture solutions.