# Synergizing advanced materials and artificial intelligence for next-generation carbon capture, utilization, and storage (CCUS): a review

## TL;DR

This review explores how advanced materials and AI can improve carbon capture, utilization, and storage to help reduce CO2 emissions and support climate goals.

## Contribution

The paper highlights the synergy between advanced materials and AI/ML for enhancing CCUS technologies.

## Key findings

- Biochar and nanomaterials show high CO2 capture potential due to their tunable porosity and surface area.
- MOFs, graphene catalysts, and SACs are effective for CO2 electrochemical reduction into fuels.
- AI and ML improve CCUS through material screening, predictive modeling, and system optimization.

## Abstract

The increasing rate of global carbon dioxide (CO2) emissions, mainly resulted from the industrial and energy sectors is a serious global challenge for climate stability. Carbon Capture, Utilization, and Storage (CCUS) technologies are being considered as important route to achieve the decarbonization objectives established in the Paris Agreement through reduction of CO2 levels in the atmosphere while allowing for its conversion to useful products. This review presents advancements in materials and technologies that are used to enhance the efficiency of CCUS process. Adsorbents based on biochar and nanomaterials, including carbon nanotubes, graphene derivatives, cellulose nanofibers, and nanoporous carbon, have significant CO2 capture potential, due to their tunable porosity and large surface area. In utilization metal–organic frameworks (MOFs), graphene-based catalysts, and single-atom catalysts (SACs) have promising selectivity in the electrochemical reduction of CO2 into fuels and chemicals in a closed carbon economy. For long-term storage, routes for secure and versatile sequestration include mineral carbonation, hydrate formation, and mixed-matrix membranes. Artificial Intelligence (AI) and Machine Learning (ML) enabled technology is increasingly crucial to the effectiveness of CCUS, not only in high-throughput material screening and predictive modeling for catalytic activity and plume migration forecasting, but also in system optimization. New digital tools, including digital twins, IoT-enabled monitoring, and life cycle assessments, increase the reliability, scalability, and sustainability of CCUS deployment. While there are many challenges remaining, especially with respect to cost, stability, and industrial scalability, CCUS can be seen as an emerging transformative technology towards net-zero energy transitions with advances occurring rapidly in synergy with materials science and digital intelligence.

This review provides insights into several material and AI/ML tools for CCUS technology.