Data-driven analysis of property-performance correlations in dual functional materials for CO₂ capture and utilization

Property-performance correlation analysis allows to investigate and connect materials performance to its properties and visualize performance trade-offs. Dual functional materials (DFMs), combining the functions of CO₂ sorbent and catalyst, are at the heart of integrated CO₂ capture and utilization (ICCU) processes. They absorb CO₂ from a diluted stream and catalytically convert it into CO using renewable H₂ as reductive feedstock. Through statistical and correlation analysis of experimental data, collected using feedstocks that approximate industrial gases, this work shows that DFMs comprising Ni, Ca, and Ce can be compositionally tuned to optimize the selectivity to CO. Limiting NiO in the DFM (≤1 wt%) is shown to (1) minimize the selectivity to CH₄, reducing the CH₄ space–time yield by 94 % compared to DFMs with higher NiO loading, and (2) maintain a CO₂ capture efficiency around 60 %, CO₂ conversion around 50 % and average CO space–time yield around 220 kg_CO m_DFM^-3 h⁻¹. The decreased CH₄ selectivity leaves more renewable H₂ available for CO₂ conversion to CO. Moreover, DFMs with lower NiO content minimize the inadvertent combustion of H₂ via chemical looping using realistic flue gas compositions that contain 2 vol% O₂. The experimental data and their statistical analysis highlight the importance of material properties for sustainable process development, aiming to minimize consumption of scarce renewable H₂ and effectively stand a chance to arrest growing CO₂ emissions.