Towards Experimental Handbooks in Catalysis
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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 fü
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