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Towards automation of operando experiments: A case study in contactless conductivity measurements

MPS-Authors
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Kraus,  Peter
Inorganic Chemistry, Fritz Haber Institute, Max Planck Society;
School of Molecular and Life Sciences,Curtin University;

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Wolf,  Elisabeth
Inorganic Chemistry, Fritz Haber Institute, Max Planck Society;
Research Department Schlögl, Max Planck Institute for Chemical Energy Conversion, Max Planck Society;

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Prinz,  Charlotte
Molecular Physics, Fritz Haber Institute, Max Planck Society;

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Bellini,  Giulia
Inorganic Chemistry, Fritz Haber Institute, Max Planck Society;

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Trunschke,  Annette
Inorganic Chemistry, Fritz Haber Institute, Max Planck Society;

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Schlögl,  Robert
Inorganic Chemistry, Fritz Haber Institute, Max Planck Society;
Research Department Schlögl, Max Planck Institute for Chemical Energy Conversion, Max Planck Society;

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Citation

Kraus, P., Wolf, E., Prinz, C., Bellini, G., Trunschke, A., & Schlögl, R. (2022). Towards automation of operando experiments: A case study in contactless conductivity measurements. Digital Discovery, 1(3), 241-254. doi:10.33774/chemrxiv-2021-mh17g.


Cite as: http://hdl.handle.net/21.11116/0000-0009-6D21-8
Abstract
Automation of experiments is a key component on the path of digitalisation in catalysis and relatedsciences. Here we present the lessons learned and caveats avoided during the automation of our con-tactless conductivity measurement set-up, capable of operando measurement of catalytic samples. Webriefly discuss the motivation behind the work, the technical groundwork required, and the philosophyguiding our design. The main body of this work is dedicated to the detailing of the implementationof the automation, data structures, as well as the modular data processing pipeline. The open-sourcetoolset developed as part of this work allows us to carry out unattended and reproducible experiments,as well as post-process data according to current best practice. This process is illustrated by implement-ing two routine sample protocols, one of which was included in theHandbook of Catalysis, providingseveral case studies showing the benefits of such automation, including increased throughput and higherdata quality. The datasets included as part of this work contain catalytic and operando conductivity1 data, and are self-consistent, annotated with metadata, and are available on a public repository in amachine-readable form. We hope the datasets as well as the tools and workflows developed as part ofthis work will be an useful guide on the path towards automation and digital catalysis.