RESUMO
Artificial intelligence (AI) can accelerate catalyst design by identifying key physicochemical descriptive parameters correlated with the underlying processes triggering, favoring, or hindering the performance. In analogy to genes in biology, these parameters might be called "materials genes" of heterogeneous catalysis. However, widely used AI methods require big data, and only the smallest part of the available data meets the quality requirement for data-efficient AI. Here, we use rigorous experimental procedures, designed to consistently take into account the kinetics of the catalyst active states formation, to measure 55 physicochemical parameters as well as the reactivity of 12 catalysts toward ethane, propane, and n-butane oxidation reactions. These materials are based on vanadium or manganese redox-active elements and present diverse phase compositions, crystallinities, and catalytic behaviors. By applying the sure-independence-screening-and-sparsifying-operator symbolic-regression approach to the consistent data set, we identify nonlinear property-function relationships depending on several key parameters and reflecting the intricate interplay of processes that govern the formation of olefins and oxygenates: local transport, site isolation, surface redox activity, adsorption, and the material dynamical restructuring under reaction conditions. These processes are captured by parameters derived from N2 adsorption, X-ray photoelectron spectroscopy (XPS), and near-ambient-pressure in situ XPS. The data-centric approach indicates the most relevant characterization techniques to be used for catalyst design and provides "rules" on how the catalyst properties may be tuned in order to achieve the desired performance.
RESUMO
Addition of small amounts of promoters to solid catalysts can cause pronounced improvement in the catalytic properties. For the complex catalysts employed in industrial processes, the fate and mode of operation of promoters is often not well understood, which hinders a more rational optimization of these important materials. Herein we show for the example of the industrial Cu/ZnO/Al2O3 catalyst for methanol synthesis how structure-performance relationships can deliver such insights and shed light on the role of the Al promoter in this system. We were able to discriminate a structural effect and an electronic promoting effect, identify the relevant Al species as a dopant in ZnO, and determine the optimal Al content of improved Cu/ZnO:Al catalysts. By analogy to Ga- and Cr-promoted samples, we conclude that there is a general effect of promoter-induced defects in ZnO on the metal-support interactions and propose the relevance of this promotion mechanism for other metal/oxide catalysts also.
RESUMO
A Sm-deficient Sm0.96MnO3 perovskite was prepared on a gram scale to investigate the influence of the chemical potential of the gas phase on the defect concentration, the oxidation states of the metals and the nature of the oxygen species at the surface. The oxide was treated at 450°C in nitrogen, synthetic air, oxygen, water vapor or CO and investigated for its properties as a catalyst in the oxidative dehydrogenation of propane both before and after treatment. After treatment in water vapor, but especially after treatment with CO, increased selectivity to propene was observed, but only when water vapor was added to the reaction gas. As shown by XRD, SEM, EDX and XRF, the bulk structure of the oxide remained stable under all conditions. In contrast, the surface underwent strong changes. This was shown by AP-XPS and AP-NEXAFS measurements in the presence of the different gas atmospheres at elevated temperatures. The treatment with CO caused a partial reduction of the metals at the surface, leading to changes in the charge of the cations, which was compensated by an increased concentration of oxygen defects. Based on the present experiments, the influence of defects and concentration of electrophilic oxygen species at the catalyst surface on the selectivity in propane oxidation is discussed.