Project Description

This Ph.D. project aims to develop novel catalytic materials for the selective conversion of CO₂ into C₂+ fuels and chemicals. While significant progress has been made in converting CO₂ to C₁ products (such as CO and methane), achieving high selectivity toward C₂+ products remains a major scientific and technological challenge due to the complexity of C-C coupling reactions, competing pathways, and limited control over reaction intermediates. Addressing this challenge is critical for enabling carbon-neutral fuel production and sustainable chemical synthesis.

This project will focus on a data-driven rational design of advanced catalytic materials, including transition-metal nanoparticles and multi-metallic alloys encapsulated in zeotype materials and metal-organic frameworks (MOFs). Special emphasis will be placed on tuning catalyst composition, nanoparticle surface structure, electronic properties, and active-site environments to promote efficient C-C coupling while suppressing undesired side reactions. Strategies such as defect engineering, heterostructure formation, and confinement effects will be explored to enhance catalytic performance.

Advanced characterization techniques, including in situ and operando spectroscopy, will be employed to probe active sites and validate mechanistic insights. The project will integrate experimental investigations with kinetic modeling to develop detailed reaction mechanisms and identify key intermediates, rate-limiting steps, and the dominant pathways governing C₂+ formation. These models will guide catalyst design, optimize operating conditions, and enable predictive evaluation of catalytic performance across different catalytic systems.

To accelerate catalyst discovery and process optimization, an AI/ML-driven research workflow will be utilized and adapted. Machine learning models will be applied to analyze experimental and modeling data, uncover structure-property-performance relationships, and prioritize promising candidates for synthesis and validation. This data-driven approach will significantly enhance the efficiency and systematic exploration of catalyst development.

This research will enable a mechanism-informed and data-driven framework spanning a wide parameter space to design highly selective catalytic materials for CO₂-to-C₂+ conversion, advancing scalable solutions for sustainable fuel and chemical production.