Photocatalytic CO₂ reduction using metal-organic frameworks: Recent advances, challenges and machine learning based...

Area of Science:

• Materials Science
• Photocatalysis
• Chemical Engineering

Background:

• Carbon dioxide (CO2) conversion via photocatalysis offers a sustainable route to reduce atmospheric emissions and produce clean energy.
• Metal-organic frameworks (MOFs) are highly effective photocatalysts for CO2 reduction due to their large surface area, structural tunability, and CO2 adsorption capabilities.

Purpose of the Study:

• To review the current state of MOF-based systems for photocatalytic CO2 reduction to valuable products like methane, methanol, formic acid, and carbon monoxide.
• To detail strategies for enhancing MOF photocatalyst performance, including linker functionalization, metal nanoparticle deposition, heterojunction formation, and co-catalyst engineering.

Main Methods:

• Review of literature on MOF-based photocatalytic CO2 reduction systems.
• Analysis of mechanistic aspects including charge transfer, intermediate stabilization, and adsorption phenomena.
• Discussion of strategies for improving MOF photocatalyst activity and selectivity.

Main Results:

• MOFs exhibit photoresponsive properties enabling efficient charge carrier generation and separation for CO2 reduction.
• Specific modifications significantly impact MOF photocatalyst activity and product selectivity.
• Key challenges include charge recombination, low quantum yields, and poor long-term stability.

Conclusions:

• MOFs hold significant potential for solar-driven CO2 valorization.
• Machine learning can accelerate the discovery and optimization of high-performance MOF photocatalysts.
• Further improvements in MOF stability and reactor integration are necessary for practical applications.