Towards Experimental Handbooks in Catalysis

Topics in Catalysis https://doi.org/10.1007/s11244-020-01380-2 ORIGINAL PAPER Towards Experimental Handbooks in Catalysis Annette Trunschke1 · Giulia Bellini1 · Maxime Boniface1 · Spencer J. Carey1 · Jinhu Dong1 · Ezgi Erdem1,2 · Lucas Foppa3 · Wiebke Frandsen1 · Michael Geske2 · Luca M. Ghiringhelli3 · Frank Girgsdies1 · Rania Hanna1 · Maike Hashagen1 · Michael Hävecker1,4 · Gregory Huff1 · Axel Knop‑Gericke1,4 · Gregor Koch1 · Peter Kraus1 · Jutta Kröhnert1 · Pierre Kube1 · Stephen Lohr5 · Thomas Lunkenbein1 · Liudmyla Masliuk1 · Raoul Naumann d’Alnoncourt2 · Toyin Omojola1 · Christoph Pratsch1 · Sven Richter1 · Christian Rohner1 · Frank Rosowski5 · Frederik Rüther2 · Matthias Scheffler3 · Robert Schlögl1,4 · Andrey Tarasov1 · Detre Teschner1,4 Olaf Timpe1 · Philipp Trunschke6 · Yuanqing Wang1,2 · Sabine Wrabetz1 · Accepted: 19 September 2020 © The Author(s) 2020 Abstract 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. Keywords Standard operation procedure · Best practice · Rigorous protocols · Descriptor · Data science · Machine learning · Artificial intelligence 1 Introduction Electronic supplementary material The online version of this article (https://doi.org/10.1007/s11244-020-01380-2) contains supplementary material, which is available to authorized users. * Annette Trunschke trunschke@fhi‑berlin.mpg.de 1 Department of Inorganic Chemistry, Fritz-Haber-Institut der Max-Planck-Gesellschaft, Faradayweg 4‑6, 14195 Berlin, Germany 2 UniCat‑BASF Joint Lab, Technische Universität Berlin, Sekr. EW K 01, Hardenbergstraße 36, 10623 Berlin, Germany 3 The NOMAD Laboratory, Fritz-Haber-Institut der Max-Planck-Gesellschaft, Faradayweg 4‑6, 14195 Berlin, Germany The application of catalyst technologies in the chemical industry stands for efficient and sustainable production of chemicals and fuels. Catalytic processes contribute to the minimization of waste formation and energy consumption, and are essential in terms of exhaust gas treatment not only in the materials, but also in the energy and transport sectors 4 Max-Planck-Institut für Chemische Energiekonversion, Stiftstr. 34‑36, 45470 Mülheim, Germany 5 BASF SE, Process Research and Chemical Engineering, Heterogeneous Catalysis, Carl‑Bosch‑Straße 38, 67056 Ludwigshafen, Germany 6 Institut für Mathematik, Technische Universität Berlin, Sekretariat MA 5‑3, Straße des 17, Juni 136, 10623 Berlin, Germany 13 Vol.:(0123456789) Topics in Catalysis [1]. More stable and effective catalysts are necessary to enable chemical energy conversion and storage at the required global scale. Only then is a closed carbon economy and the construction of sustainable energy systems possible [2]. As for production of basic chemicals and consumer products in the chemical industry, high selectivity to the desired reaction product allows for the efficient utilization of raw materials, the minimization of energy consumption by avoiding separation and purification steps, and the mitigation of waste formation or emission of greenhouse gases such as CO2. However, development of selective catalysts for reactions with numerous products, including selective oxidation of hydrocarbons [3–8], and synthesis of olefins and oxygenates via hydrogenation of carbon oxides [9–11], are challenging due to their underlying complex organic reaction networks. The limited understanding of relations between catalyst structure and reactivity entails that technology changes are rare in the evolution of heterogeneous catalysis and attractive processes such as the direct synthesis of olefins or methanol from methane [7, 12], the selective oxidation of propane to oxygenates (acrolein or acrylic acid) [13, 14], or the synthesis of higher alcohols from synthesis gas [11], are commercially not yet implemented despite extensive research efforts. Experimentally determined descriptors have been identified to guide catalyst developments in oxidation reactions [15, 16], acid–base reactions [17], or reactions on ceria catalysts [18], just to mention a few examples. Grasselli proposed “Seven Pillars in Oxidation Catalysis”, which comprise lattice oxygen, metal–oxygen bond strength, host structure, redox properties, multifunctionality of active sites, site isolation, and phase cooperation, summarizing the seven most important features that should be taken into account in the design of metal oxides for selective oxidation of hydrocarbons [19]. Artificial intelligence may facilitate the identification of new, high-performance catalysts. Data science applications find renewed interest in heterogeneous catalysis research with the aim to discover selective catalysts for reactions that are influenced by a multitude of parameters, see for example references [20–32]. In the present essay we will explain our viewpoint why the use of artificial intelligence (AI) and data science requires a shift in the current paradigm of data generation and documentation in catalysis research. A definition of standards, rigorous measurement protocols, and best practice procedures that enables quality control and allows for the generation of suitable input data is necessary. Best practice and pitfalls in materials synthesis, kinetic measurements and characterization in both thermal catalysis and electrocatalysis or electrochemistry are permanent topics in the scientific literature [33–46] and in editorials [47–50]. Design issues, databases, and advanced characterization 13 approaches are also discussed in materials science [51, 52]. Nonetheless, all efforts did not lead to any action across the catalysis community such as suggesting a minimum standard in the reporting of workflows and results. In other fields of science, such as crystallography, such structured reporting is now compulsory in each report [53]. Standards do not inhibit scientific creativity as they represent a minimum of experimentation metadata and results while setting no limits to additional work. 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