Descriptors toward higher alcohols and olefins formation in CO2 hydrogenation on promoted iron catalysts

Developing efficient catalysts for CO 2 hydrogenation to higher alcohols (HA) or olefins remains a long-standing challenge. Iron-based catalysts, with single or multiple promoters, show promise for both product families. However, prior studies often isolate the effects of a few promoters under varied conditions, resulting in fragmented insights. Advancing high-performance catalysts requires a unified framework for understanding promoter effects under standardized conditions. In this work, through a systematic study involving nine alkali/alkaline-earth metals, thirteen metal oxides, and copper, we identified the surface Fe 5 C 2 :(Fe 3 O 4 +Cu) ratio as a key performance descriptor for HA and olefin formation, revealing that productivity to both product families can be simultaneously optimized for 1.0-2.5 values. By optimizing it in the Na 1 Fe 59 Cu 30 Zn 10 catalyst, we doubled reported productivities, namely 0.5 g h −1 g cat −1 for HA and simultaneously 1.1 g h −1 g cat −1 for olefins alongside a combined selectivity of 75%. Deep characterization, including Operando XRD and XAS, revealed how different promoters influence the Fe 5 C 2 :(Fe 3 O 4 +Cu) ratio and its impact on the reaction mechanisms. Alkali metals were found to drive carbide formation, whose amount can be fine-tuned by metal oxides. Additionally, operando DRIFTS studies monitored how copper drives the building up of oxygenated intermediates and their subsequent transformation into HA. Data science was finally used to identify for each type of promoter interpretable descriptors accounting for these effects. This approach may facilitate future multicomponent catalyst design efforts in CO 2 valorization. • Rationalization of promotional effects on iron catalyst in CO 2 hydrogenation • Surface Fe 5 C 2 :(Fe 3 O 4 +Cu) ratio identified as general performance descriptor • Optimized Na 1 Fe 59 Cu 30 Zn 10 catalyst enables record productivity to alcohols and olefins • Operando studies show how promoters dictate the value of the general descriptor • Data science separated the role of each promoter in interpretable descriptors