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.

The “Seven Pillars” of oxidation catalysis proposed by Robert K. Grasselli represent an early example of phenomenological descriptors in the field of heterogeneous catalysis. Major advances in the theoretical description of catalytic reactions have been achieved in recent years and new catalysts are predicted today by using computational methods. To tackle the immense complexity of high-performance systems in reactions where selectivity is a major issue, analysis of scientific data by artificial intelligence and data science provides new opportunities for achieving improved understanding. Modern data analytics require data of highest quality and sufficient diversity. Existing data, however, frequently do not comply with these constraints. Therefore, new concepts of data generation and management are needed. Herein we present a basic approach in defining best practice procedures of measuring consistent data sets in heterogeneous catalysis using “handbooks”. Selective oxidation of short-chain alkanes over mixed metal oxide catalysts was selected as an example.

The crystal structure of perovskites can incorporate a wide variety of cations, which makes this class of materials so interesting for studies of links between solid-state chemistry and catalysis. Perovskites are known as typical total combustion catalysts in hydrocarbon oxidation reactions. The fundamental question that we investigate here is whether surface modifications of perovskites can lead to the formation of valuable reaction products in alkane oxidation. We studied the effect of segregated two-dimensional surface nanostructures on selectivity to propene in the oxidative dehydrogenation of propane. Manganese-based perovskites AMnO 3 (A = La, Sm) were prepared by combustion and hydrothermal synthesis. Bulk and surface structures were investigated by X-ray diffraction, temperature-programmed reduction, aberration-corrected scanning transmission electron microscopy (STEM), multiwavelength Raman, and ambient-pressure X-ray photoelectron spectroscopy (AP-XPS) in combination with near-edge X-ray absorption fine structure (NEXAFS) spectroscopy. Surface oxygen species responsible for C−H activation were distinguished by AP-XPS on the basis of a rigorous in situ analysis of the O 1s spectra recorded under a broad range of reaction conditions. Signals at 529.2, 530.1, 530.9, 531.2, and 531.8 eV were attributed to lattice O, defect-affected O, surface O, oxygen in carbonates, and hydroxyl groups, respectively. Operando AP-XPS revealed critical surface features, which occur under catalyst operation. The catalyst performance depends on the synthesis technique and the reaction conditions. In presence of a two-dimensional MnO x surface phase, addition of steam to the feed resulted in an increase in selectivity to the partial oxidation product propene to practically relevant values. The selectivity increase is related to the presence of Mn in a low oxidation state (2+/3+), an increased concentration of hydroxyl groups, and a higher abundance of adsorbed activated oxygen species on the catalyst surface. The surface analysis of a working catalyst highlights the importance of the termination layer of polycrystalline perovskites as a genuine property implemented by catalyst preparation. Such a termination layer controls the chemical properties and reactivity of perovskites. The information provides input for the development of realistic models that can be used by theory to predict functional properties.

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.

All thiocyanate anions bind with both ends to different cations. The coordination network expands in plane layers parallel (1 1 0). The water molecule coordinates one of the two independent Bi 3+ cations. Dehydration sets in at 100 °C, followed by stepwise thermal decomposition to Bi 2 S 3 .