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Author: Kraehnert, Ralph × WGL Institute: LIKAT ×

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    A Unified Research Data Infrastructure for Catalysis Research – Challenges and Concepts
    (Weinheim : Wiley-VCH, 2021) Wulf, Christoph; Beller, Matthias; Boenisch, Thomas; Deutschmann, Olaf; Hanf, Schirin; Kockmann, Norbert; Kraehnert, Ralph; Oezaslan, Mehtap; Palkovits, Stefan; Schimmler, Sonja; Schunk, Stephan A.; Wagemann, Kurt; Linke, David
    Modern research methods produce large amounts of scientifically valuable data. Tools to process and analyze such data have advanced rapidly. Yet, access to large amounts of high-quality data remains limited in many fields, including catalysis research. Implementing the concept of FAIR data (Findable, Accessible, Interoperable, Reusable) in the catalysis community would improve this situation dramatically. The German NFDI initiative (National Research Data Infrastructure) aims to create a unique research data infrastructure covering all scientific disciplines. One of the consortia, NFDI4Cat, proposes a concept that serves all aspects and fields of catalysis research. We present a perspective on the challenging path ahead. Starting out from the current state, research needs are identified. A vision for a integrating all research data along the catalysis value chain, from molecule to chemical process, is developed. Respective core development topics are discussed, including ontologies, metadata, required infrastructure, IP, and the embedding into research community. This Concept paper aims to inspire not only researchers in the catalysis field, but to spark similar efforts also in other disciplines and on an international level. © 2021 The Authors. ChemCatChem published by Wiley-VCH GmbH
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    A meta-analysis of catalytic literature data reveals property-performance correlations for the OCM reaction
    ([London] : Nature Publishing Group UK, 2019) Schmack, Roman; Friedrich, Alexandra; Kondratenko, Evgenii V.; Polte, Jörg; Werwatz, Axel; Kraehnert, Ralph
    Decades of catalysis research have created vast amounts of experimental data. Within these data, new insights into property-performance correlations are hidden. However, the incomplete nature and undefined structure of the data has so far prevented comprehensive knowledge extraction. We propose a meta-analysis method that identifies correlations between a catalyst’s physico-chemical properties and its performance in a particular reaction. The method unites literature data with textbook knowledge and statistical tools. Starting from a researcher’s chemical intuition, a hypothesis is formulated and tested against the data for statistical significance. Iterative hypothesis refinement yields simple, robust and interpretable chemical models. The derived insights can guide new fundamental research and the discovery of improved catalysts. We demonstrate and validate the method for the oxidative coupling of methane (OCM). The final model indicates that only well-performing catalysts provide under reaction conditions two independent functionalities, i.e. a thermodynamically stable carbonate and a thermally stable oxide support.