In search of initial predictors of fischer-tropsch catalytic activity

All Fischer-Tropsch mechanisms known to date begin with the adsorption of carbon monoxide followed by its dissociation on a given catalysts surface. Understanding how those catalysts modify reactivity descriptors such as CO adsorption and dissociation energies is a key for nanoengineering materials for this type of applications. Cluster models of Co, Fe, Ni, Pd, Pt, and Ru have been explored using the density functional theory, in search of initial descriptors of Fischer-Tropsch activity for pure and binary combinations of those elements. Carbon monoxide adsorption energies were calculated for adsorption on all possible catalytic sites, and the most preferred CO adsorption sites were found in each case. An initial predictor that can be used to anticipate potentially effective catalysts was identified as a percentage difference, using CO adsorption energy results in combination with CO dissociation energies from the previously found sites. A greater catalysts performance is expected when that percentage difference is maximized. On pure clusters, the predictor indicates that ruthenium is expected to be the best catalyst followed by cobalt, in very good agreement with the current knowledge in this field. Thus, this paper presents a mechanism to quickly explore the natural potential of a catalyst material to break the CO bond. The predictor presented here can guide the synthesis of new catalysts, involving modifications of known and currently used catalysts for the Fischer-Tropsch process, and investigations on catalysis in general.