Engineering CO ₂ -to-CO Conversion: Integrating Catalyst Design, Mechanistic Investigation, Reactor Fundamentals, TEA–LCA Evaluations, and AI-Driven Optimization

Converting CO2 to CO represents a promising energy conversion approach that reduces greenhouse gas emissions by repurposing CO2 into eco-friendly fuel. CO and H2, collectively known as synthesis gas (syngas), serve as key feedstocks in the industrial Fischer–Tropsch process that enables the conversion of gaseous reactants to liquid hydrocarbons and a wide range of chemicals. In this context, CO serves as a key intermediate for gas-to-liquid conversion from aqueous CO2. However, its highly endothermic nature, along with catalyst deactivation and undesired side reactions, makes the Reverse Water Gas Shift (RWGS) process significantly challenging for researchers to ensure its long-term stability and practical viability. The hunt continues to discover a catalyst that not only achieves a high conversion rate but also exhibits enhanced selectivity and long-term stability under demanding operating conditions, paving the way for efficient and carbon-neutral catalytic processes. Beyond catalyst design, an optimized reactor design approach plays a crucial role in maximizing catalyst efficiency and enhancing overall process performance. Above all, Life Cycle Assessment (LCA) and Technoeconomic Analysis (TEA) should be considered as essential tools for evaluating both the impact on the environment and the economic feasibility of the process, ensuring its viability in real-world applications. In the modern era, Machine Learning (ML) approaches have emerged as powerful tools to discover catalysts by leveraging existing data sets. By reducing experimentation time and enhancing predictive accuracy, ML enables the development of high-performance catalysts, surpassing the traditional trial-and-error methodology. This review has discussed all these points, encompassing advanced catalyst design in recent times, in-depth mechanistic insights, innovative reactor configurations, comprehensive LCA–TEA evaluations, and the integration of cutting-edge AI and Machine Learning (ML) approaches in accelerating the catalyst design process.