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Artificial intelligence (AI) can accelerate materials design by identifying the key parameters correlated with the performance. However, widely used AI methods require big data, and only the smallest part of the available data in heterogeneous catalysis 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, in order to measure 55 physicochemical parameters as well as the reactivity of 12 catalysts towards ethane, propane, and n-butane oxidation. These catalyst materials are based on vanadium or manganese redox-active elements (RAEs) and present diverse phase compositions, crystallinities, and catalytic behaviors. By applying the sure-independence-screening-and-sparsifying-operator (SISSO) approach to the consistent data set, we identify nonlinear property-function relationships depending on several key parameters, reflecting the intricate interplay of underlying processes governing selective oxidation. This approach indicates the most relevant characterization techniques and shows how the catalyst properties may be tuned in order to achieve the desired performance. For example, to achieve high olefin yields, the catalyst must have a high specific surface area, a low concentration of surface RAE, and the ability to change the surface RAE oxidation states under reaction conditions with respect to vacuum. These parameters are measured by N2 adsorption, x-ray photoelectron spectroscopy (XPS), and near-ambient-pressure in situ XPS. They reflect the relevance of local transport, site isolation, surface redox activity, and the materials dynamical restructuring under reaction conditions. Although the relationship describing the even more challenging oxygenate yields shares similarities with that for olefin yields, a parameter reflecting the importance of specific surface sites, derived from the analysis of the carbon 1s XPS spectra, is additionally identified as key for high selectivity to oxygenates.

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.

Isostructural orthorhombic oxides of the general formula (Mo,V,Te,Sb,Nb,Ta)Ox are an important class of solids, which are interesting as catalysts for oxidation of light alkanes. We investigated relations between the electronic properties of MoV-oxide (orthorhombic M1 phase) and its catalytic performance in the oxidation of ethane, propane, and n-butane. Operando conductivity and permittivity measurements were performed and complemented by near-ambient-pressure X-ray photoelectron spectroscopy. In contrast to the n-type MoVTeNb-oxide, MoV-oxide showed p-type semiconducting behavior. The conductivity of the sample adapted sensitively to the surrounding atmosphere, not only to alkane chain lengths but also to reactant conversion levels. However, no measurable change in band bending depending on the alkane chain length was observed, indicating that the gas-phase-dependent surface potential barrier, which controls the charge transfer between reactants and catalyst, is less pronounced or missing in dry alkane oxidation feeds. The addition of steam in propane oxidation led to a decrease of its conductivity and work function. Steam significantly influenced the surface layer on MoV-oxide, resulting in an enrichment of covalently bonded V5+ species and surface hydroxylation. A small change in the surface potential barrier induced by wet propane oxidation feed can contribute to a modification of the bulk–surface charge transfer and improved selectivity to acrylic acid.

Abstract The performance in heterogeneous catalysis is an example of a complex materials function, governed by an intricate interplay of several processes (e.g., the different surface chemical reactions, and the dynamic restructuring of the catalyst material at reaction conditions). Modeling the full catalytic progression via first-principles statistical mechanics is impractical, if not impossible. Instead, we show here how a tailored artificial-intelligence approach can be applied, even to a small number of materials, to model catalysis and determine the key descriptive parameters (“materials genes”) reflecting the processes that trigger, facilitate, or hinder catalyst performance. We start from a consistent experimental set of “clean data,” containing nine vanadium-based oxidation catalysts. These materials were synthesized, fully characterized, and tested according to standardized protocols. By applying the symbolic-regression SISSO approach, we identify correlations between the few most relevant materials properties and their reactivity. This approach highlights the underlying physicochemical processes, and accelerates catalyst design. Impact statement Artificial intelligence (AI) accepts that there are relationships or correlations that cannot be expressed in terms of a closed mathematical form or an easy-to-do numerical simulation. For the function of materials, for example, catalysis, AI may well capture the behavior better than the theory of the past. However, currently the flexibility of AI comes together with a lack of interpretability, and AI can only predict aspects that were included in the training. The approach proposed and demonstrated in this IMPACT article is interpretable. It combines detailed experimental data (called "clean data") and symbolic regression for the identification of the key descriptive parameters (called "materials genes") that are correlated with the materials function. The approach demonstrated here for the catalytic oxidation of propane will accelerate the discovery of improved or novel materials while also enhancing physical understanding.

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 AMnO3 (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 MnOx 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. Copyright © 2020 American Chemical Society.

The structures of solids can locally differ from the macroscopic picture obtained by structural averaging techniques. This difference significantly influences the performance of any functional material. Measurements of these local structures are challenging. Thus, the description of defects is often disregarded. However, in order to understand the functionality, such irregularities have to be investigated. Here, we present a high resolution scanning transmission electron microscopic (STEM) study revealing local structural irregularities in open structured oxides using catalytically active orthorhombic (Mo,V,Te,Nb)Ox as a complex example. Detailed analysis of annular dark field- and annular bright field-STEM images reveal site specific local structural displacements of individual framework and channel sites in the picometer range. These experimental observables can be considered as an important structural addendum for theoretical modelling and should be implemented into the existing data in order to quantify site specific potential energies and stresses. This information can further be used to describe the impact of the structure on the catalytic performance in greater detail.

Microstructure, structure, and compositional homogeneity of metal oxide nanoparticles can change dramatically during catalysis. Considering the different stabilities of cobalt and iron ions in the MgO host lattice [M. Niedermaier et al., J. Phys. Chem. C 123, 25991 (2019)], we employed MgO nanocube powders with or without transition metal admixtures for the oxidative coupling of methane (OCM) reaction to analyze characteristic differences in catalytic activity and sintering behavior. Undoped MgO nanocrystals exhibit the highest C2 selectivity and retain the nanocrystallinity of the starting material after 24 h time on stream. For the Co–Mg–O nanoparticle powder, which exhibits the highest activity and COx selectivity and where OCM-induced coarsening is strongest, we found that the Co2+ ions remain homogeneously distributed over the MgO lattice. Trivalent Fe ions migrate to the surface of Fe–Mg–O nanoparticles where they form a magnesioferrite phase (MgFe2O4) with a characteristic impact on catalytic performance: Fe–Mg–O is initially less selective than MgO despite its lower activity. An increase in C2 selectivity and a decrease in the CO2/CO ratio with time on stream are attributed to the increasing fraction of coarsened particles that become depleted in redox active Fe. Surface water is a by-product of the OCM reaction, favors mass transport across the particle surfaces, and serves as a sintering aid during catalysis. The characteristic changes in size and morphology of MgO, Co-doped, and Fe-doped MgO particles can be consistently explained by activity and C2 selectivity trends. The original morphology of the nanocubes as a starting material for the OCM reaction does not impact the catalytic activity.

Mixed oxides composed of the four metals Mo, V, Te, and Nb are known to be efficient catalysts in selective oxidation of lower alkanes. The outstanding catalytic performance of such mixed oxides is attributed to the presence of the so-called M1 crystal phase that contains all four elements in the metal positions of the structure. In the present work, an M1 phase composed only of Mo and V has been prepared by hydrothermal synthesis. High crystallinity was achieved by applying a synthesis temperature of 200 °C. The phase-pure mixed MoV oxide was studied as catalyst in the oxidation of propane. In contrast to previous reports, the desirable oxidation product acrylic acid is formed over the Te-free M1 structure in significant amounts, implying that Te is not necessarily required as a component of the active ensemble responsible for selective oxygen insertion. The MoV M1 oxide is, however, less selective compared to that of the M1 structure composed of the four metals Mo, V, Te, and Nb. The reason has been determined by applying a combination of synchrotron-based single crystal structure analysis and near-ambient-pressure X-ray photoelectron spectroscopy. Determination of the crystal structure of MoV M1 oxide reveals partial occupation of sites in the hexagonal channels of the M1 structure by V, which are occupied by Te in MoVTeNb M1 oxide. Hydrolysis of the M-O bonds (M = V, Te) under reaction conditions leads to migration of the metal in the hexagonal channels to the surface. Accumulation of more than 50 at % V on the surface of the MoV M1 oxide most likely causes postcombustion of formed acrylic acid, whereas enrichment of Te at the surface of MoVTeNb M1 oxide results in dilution of surface V5+ species and, consequently, high selectivity.

The catalytic performance of 1) crystalline MoVTeNb oxide that exhibits the electronic properties of a n-type semiconductor, 2) submonolayer vanadium oxide supported on meso-structured silica (SBA-15) as an insulating support, and 3) surface-functionalized carbon nanotubes that contain neither a redox active metal nor bulk oxygen, but only surface oxygen species have been compared in the oxidative dehydrogenation of ethane and propane under equal reaction conditions. The catalytic results indicate similarities in the reaction network over all three catalysts within the range of the studied reaction conditions implying that differences in selectivity are a consequence of differences in the rate constants. Higher activity and selectivity to acrylic acid over MoVTeNb oxide as compared to the other two catalysts are attributed to the higher density of potential alkane adsorption sites on M1 and the specific electronic structure of the semiconducting bulk catalyst. Microcalorimetry has been used to determine and quantify different adsorption sites revealing a low Vsurface/C3H8 ads ratio of 4 on M1 and a much higher ratio of 150 on silica-supported vanadium oxide. On the latter catalyst less than one per cent of the vanadium atoms adsorb propane. Barriers of propane activation increase in the order P/oCNT (139 kJ mol−1)≤M1 (143 kJ mol−1)−1), which is in agreement with trends predicted by theory.

We address the question of the nature of Au NP activation and through a combination of experimental and theoretical techniques. In‐situ XPS measurements of Au/TiO₂ during CO oxidation show high catalytic activity can be associated with the formation of an ionic Au species. DFT calculations performed on Au/TiO₂ show that the formation of such ionic Au is due to a strong metal‐support interaction between Au and reduced and defective TiO₂. TEM supports these findings, indicating the formation of an overlayer of transition metal oxide support on Au NPs after CO oxidation. These results suggest TiO₂ lattice oxygen is involved directly in CO oxidation, which was confirmed with labeled ¹⁸O₂ experiments.

The oxidation of propane has been studied over silica-supported vanadium oxide and polycrystalline, bulk MoVTeNb oxide with M1 structure. Temperature-programmed reaction experiments were performed, and the reactivity of propane molecules labeled with deuterium and 13C, respectively, was analyzed under steady-state conditions. The measurement of kinetic isotope effects reveals fundamental differences in the activation of propane over the two catalysts. The reaction network of consecutive and parallel reactions of the formed propylene is comparable. However, oxygen insertion into the CHO group of acrolein under formation of acrylic acid is faster over M1 than oxidation at the CH2 group and decarbonylation to acetaldehyde. In contrast, the latter process is preferred over silica-supported vanadium oxide resulting in lower selectivity to unsaturated oxygenates.

Highly dispersed molybdenum oxide supported on mesoporous silica SBA-15 has been prepared by anion exchange resulting in a series of catalysts with changing Mo densities (0.2-2.5 Mo atoms nm(-2) ). X-ray absorption, UV/Vis, Raman, and IR spectroscopy indicate that doubly anchored tetrahedral dioxo MoO4 units are the major surface species at all loadings. Higher reducibility at loadings close to the monolayer measured by temperature-programmed reduction and a steep increase in the catalytic activity observed in metathesis of propene and oxidative dehydrogenation of propane at 8 % of Mo loading are attributed to frustration of Mo oxide surface species and lateral interactions. Based on DFT calculations, NEXAFS spectra at the O-K-edge at high Mo loadings are explained by distorted MoO4 complexes. Limited availability of anchor silanol groups at high loadings forces the MoO4 groups to form more strained configurations. The occurrence of strain is linked to the increase in reactivity.

Durch Ionenaustausch wurde MoO3 hochdispers (0.2–2.5 Mo-Atome nm−2) auf die Oberflache von mesoporosem SiO2 (SBA-15) aufgetragen. Rontgenabsorption, UV/Vis-, Raman- und IR-Spektroskopie zeigten, dass zweifach verankerte tetraedrische MoO4-Dioxo-Einheiten unabhangig von der Beladung dominieren. Eine verbesserte Reduzierbarkeit bei Beladungen nahe der Monolage, die mittels temperaturprogrammierten Reduktion gemessen wurde, und der sprunghafte Anstieg der Reaktivitat in der Metathese von Propen oder der oxidativen Dehydrierung von Propan bei 8 % Mo/SBA-15 werden auf die Frustration von MoO4-Spezies und laterale Wechselwirkungen zuruckgefuhrt. DFT Modellrechnungen ermoglichten eine Zuordnung der Rontgenabsorptionsspektren an der O-K-Kante zu geometrisch verzerrten MoO4-Spezies, die in hochbeladenen Katalysatoren auftreten. Die eingeschrankte Verfugbarkeit von Silanol-Ankergruppen fuhrt hier zur Bildung verzerrter Konfigurationen, denen die hohe katalytische Aktivitat zugeschrieben wird.

In situ Raman spectroscopy allows insight into molecular processes under hydrothermal conditions during synthesis of complex nanostructured MoVTeNb oxides (see picture: Nb yellow, Mo blue, V/Mo pale blue, Te red). Based on the knowledge acquired, the synthesis can be more efficiently directed towards the desired product with improved functionality.

Oxygen core excitations in different molecular molybdena–silica models are evaluated using density-functional theory (DFT). These results can be compared with in situ X-ray absorption fine structure (NEXAFS) measurements near the O K-edge of molybdena model catalysts supported on SBA-15 silica, used for exploratory catalytic activity studies. The comparison allows an analysis of structural details of the molybdena species. The silica support is found to contribute to the NEXAFS spectrum in an energy range well above that of the molybdena units, allowing a clear separation between the corresponding contributions. Different types of oxygen species, O(1) in terminal M═O bonds, O(2) in interphase Mo—O—Si bridges and in Mo—O—Mo linkages, as well as O(2) in terminal Mo—O—H groups can be distinguished in the theoretical spectra of the molybdena species with molybdenum in tetrahedral (dioxo species), pentahedral (monooxo species), and octahedral coordination. The experimental NEXAFS spectra exhibit a pronounced d...

One of the main goals in catalysis is the characterization of solid/gas interfaces in a reaction environment. The electronic structure and chemical composition of surfaces become heavily influenced by the surrounding environment. However, the lack of surface sensitive techniques that are able to monitor these modifications under high pressure conditions hinders the understanding of such processes. This limitation is known throughout the community as the “pressure gap”. We have developed a novel experimental setup that provides chemical information on a molecular level under atmospheric pressure and in presence of reactive gases and at elevated temperatures. This approach is based on separating the vacuum environment from the high-pressure environment by a silicon nitride grid–that contains an array of micrometer-sized holes–coated with a bilayer of graphene. Using this configuration, we have investigated the local electronic structure of catalysts by means of photoelectron spectroscopy, and in presence of gases at 1 atmosphere. The reaction products were monitored on-line by mass spectrometry and gas chromatography. The successful operation of this setup was demonstrated with three different examples: the oxidation/reduction reaction of iridium (noble metal) and copper (transition metal) nanoparticles and with the hydrogenation of propyne on Pd black catalyst (powder).