Synergizing advanced materials and artificial intelligence for next-generation carbon capture, utilization, and...

Area of Science:

• Environmental Science
• Materials Science
• Chemical Engineering

Background:

• Rising carbon dioxide (CO2) emissions from industrial and energy sectors pose a significant threat to global climate stability.
• Carbon Capture, Utilization, and Storage (CCUS) technologies are critical for achieving decarbonization goals outlined in the Paris Agreement.
• CCUS aims to reduce atmospheric CO2 levels and convert captured carbon into valuable products.

Purpose of the Study:

• To review recent advancements in materials and technologies that enhance the efficiency of CCUS processes.
• To highlight the role of novel adsorbents, catalysts, and storage methods in CCUS.
• To explore the integration of Artificial Intelligence (AI) and Machine Learning (ML) in optimizing CCUS operations.

Main Methods:

• Review of literature on advanced materials for CO2 capture, including biochar, nanomaterials (carbon nanotubes, graphene derivatives, cellulose nanofibers, nanoporous carbon).
• Analysis of materials for CO2 utilization, such as metal-organic frameworks (MOFs) and single-atom catalysts (SACs) for electrochemical reduction.
• Examination of sequestration techniques for CO2 storage, including mineral carbonation, hydrate formation, and mixed-matrix membranes.
• Assessment of AI/ML applications in material screening, predictive modeling, and system optimization for CCUS.
• Evaluation of digital tools like digital twins and IoT for CCUS reliability and scalability.

Main Results:

• Biochar and nanomaterials show high CO2 capture potential due to their porosity and surface area.
• MOFs, graphene-based, and SACs demonstrate promising selectivity for electrochemical CO2 conversion.
• Mineral carbonation, hydrate formation, and membranes offer secure CO2 sequestration routes.
• AI and ML significantly enhance CCUS through predictive modeling and optimization.
• Digital tools improve the reliability, scalability, and sustainability of CCUS deployment.

Conclusions:

• CCUS technologies, driven by material science and digital intelligence, are transformative for net-zero energy transitions.
• Despite challenges in cost, stability, and scalability, CCUS advancements are rapidly emerging.
• The synergy between materials innovation and AI/ML is crucial for the future of CCUS.