Year-end Sale % OFF 
Abstract
Decades of catalysis research have created vast amounts of experimental data. Within these data, new insights into property-performance correlations are hidden. However, the incomplete nature and undefined structure of the data has so far prevented comprehensive knowledge extraction. We propose a meta-analysis method that identifies correlations between a catalyst’s physico-chemical properties and its performance in a particular reaction. The method unites literature data with textbook knowledge and statistical tools. Starting from a researcher’s chemical intuition, a hypothesis is formulated and tested against the data for statistical significance. Iterative hypothesis refinement yields simple, robust and interpretable chemical models. The derived insights can guide new fundamental research and the discovery of improved catalysts. We demonstrate and validate the method for the oxidative coupling of methane (OCM). The final model indicates that only well-performing catalysts provide under reaction conditions two independent functionalities, i.e. a thermodynamically stable carbonate and a thermally stable oxide support.
Highlights
Decades of catalysis research have created vast amounts of experimental data
Neither did these studies identify new structure–activity relationships nor could they provide simple chemical explanations for the observed statistical effects. The shortcomings of these reports can be attributed, e.g., to (1) rather small datasets being used, (2) statistical learning methods that search exploratively for relationships in the data, and (3) failure to incorporate existing chemical knowledge to inform and direct the statistical work. Another challenge faced in heterogeneous catalysis results (4) from the unsystematic heterogeneity of the available data
The dataset provides for the oxidative coupling of methane (OCM) information on catalyst composition, reaction conditions and catalyst performance
Summary
Decades of catalysis research have created vast amounts of experimental data. Within these data, new insights into property-performance correlations are hidden. Efforts to collect and analyze sets of literature data have been reported primarily for the water gas shift reaction (WGS)[7], CO-oxidation[8,9,10], transesterification in biodiesel production[11], and electro-catalytic oxidation of alcohols in direct alcohol fuel cells[12] Neither did these studies identify new structure–activity relationships nor could they provide simple chemical explanations for the observed statistical effects. The shortcomings of these reports can be attributed, e.g., to (1) rather small datasets being used (data from 85 publications or less7–12), (2) statistical learning methods that search exploratively for relationships in the data, and (3) failure to incorporate existing chemical knowledge to inform and direct the statistical work.
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
