Meta-analysis approach for understanding the characteristics of CO2 reduction catalysts for renewable fuel production

The conversion of CO₂ into renewable fuels by thermo-catalytic hydrogenation has been attempted by several researchers as this overcomes two significant problems, namely, greenhouse gas CO₂ mitigation and renewable fuel production. Numerous experimental data have been reported in carbon-neutral methanol production using various copper, copper-zinc, copper-zinc-aluminium and also non-copper based catalysts in the past two decades. However, the lack of comprehensive data extraction and undefined data structure have concealed the property-performance correlations in catalyst design, which may have given a significant breakthrough in scaling up this carbon-neutral technology. This work attempts to review the literature data on this catalytic reaction considering various important catalyst descriptors starting from an elemental composition followed by correlating all the catalyst properties with performance using a meta-analysis approach. Herein, data for CO₂ hydrogenation to C₁ alcohol, methanol, was collected from 110 literature publications, and a comprehensive, structured interpretation of the catalysts' features that enhance their activity in this reaction is interpreted using meta-analysis. All the property groups, which are the key performance indicators, are analysed by testing their statistical significance across a wide range of reaction conditions under which the experimental data are collected from the literature. The developed machine learning model reveals that the promising catalyst must have the functionality to form stable carbonates. The adequacy of the model was validated through experimental data collected over eight different reaction conditions in a laboratory-scale tubular reactor. Thus, the meta-analysis tool is used to its full potential of exploring experimental data reported in past decades for the accurate design of CO₂ reduction catalysts.