A meta-analysis of catalytic literature data reveals property-performance correlations for the OCM reaction
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 me...
Main Authors: | , , , , , |
---|---|
Format: | Article in Journal/Newspaper |
Language: | English |
Published: |
[London] : Nature Publishing Group UK
2019
|
Subjects: | |
Online Access: | https://oa.tib.eu/renate/handle/123456789/10300 https://doi.org/10.34657/9336 |
id |
ftleibnizopen:oai:oai.leibnizopen.de:3hN2DYsBBwLIz6xGk-XH |
---|---|
record_format |
openpolar |
spelling |
ftleibnizopen:oai:oai.leibnizopen.de:3hN2DYsBBwLIz6xGk-XH 2023-11-05T03:41:13+01:00 A meta-analysis of catalytic literature data reveals property-performance correlations for the OCM reaction Schmack, Roman Friedrich, Alexandra Kondratenko, Evgenii V. Polte, Jörg Werwatz, Axel Kraehnert, Ralph 2019 application/pdf https://oa.tib.eu/renate/handle/123456789/10300 https://doi.org/10.34657/9336 eng eng [London] : Nature Publishing Group UK CC BY 4.0 Unported https://creativecommons.org/licenses/by/4.0/ Nature Communications 10 (2019) carbonic acid methane oxide catalysis catalyst chemical model correlation analysis data analysis hypothesis literature oxidative coupling thermodynamics thermostability 500 article Text 2019 ftleibnizopen https://doi.org/10.34657/9336 2023-10-08T23:26:26Z 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. publishedVersion Article in Journal/Newspaper Carbonic acid LeibnizOpen (The Leibniz Association) |
institution |
Open Polar |
collection |
LeibnizOpen (The Leibniz Association) |
op_collection_id |
ftleibnizopen |
language |
English |
topic |
carbonic acid methane oxide catalysis catalyst chemical model correlation analysis data analysis hypothesis literature oxidative coupling thermodynamics thermostability 500 |
spellingShingle |
carbonic acid methane oxide catalysis catalyst chemical model correlation analysis data analysis hypothesis literature oxidative coupling thermodynamics thermostability 500 Schmack, Roman Friedrich, Alexandra Kondratenko, Evgenii V. Polte, Jörg Werwatz, Axel Kraehnert, Ralph A meta-analysis of catalytic literature data reveals property-performance correlations for the OCM reaction |
topic_facet |
carbonic acid methane oxide catalysis catalyst chemical model correlation analysis data analysis hypothesis literature oxidative coupling thermodynamics thermostability 500 |
description |
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. publishedVersion |
format |
Article in Journal/Newspaper |
author |
Schmack, Roman Friedrich, Alexandra Kondratenko, Evgenii V. Polte, Jörg Werwatz, Axel Kraehnert, Ralph |
author_facet |
Schmack, Roman Friedrich, Alexandra Kondratenko, Evgenii V. Polte, Jörg Werwatz, Axel Kraehnert, Ralph |
author_sort |
Schmack, Roman |
title |
A meta-analysis of catalytic literature data reveals property-performance correlations for the OCM reaction |
title_short |
A meta-analysis of catalytic literature data reveals property-performance correlations for the OCM reaction |
title_full |
A meta-analysis of catalytic literature data reveals property-performance correlations for the OCM reaction |
title_fullStr |
A meta-analysis of catalytic literature data reveals property-performance correlations for the OCM reaction |
title_full_unstemmed |
A meta-analysis of catalytic literature data reveals property-performance correlations for the OCM reaction |
title_sort |
meta-analysis of catalytic literature data reveals property-performance correlations for the ocm reaction |
publisher |
[London] : Nature Publishing Group UK |
publishDate |
2019 |
url |
https://oa.tib.eu/renate/handle/123456789/10300 https://doi.org/10.34657/9336 |
genre |
Carbonic acid |
genre_facet |
Carbonic acid |
op_source |
Nature Communications 10 (2019) |
op_rights |
CC BY 4.0 Unported https://creativecommons.org/licenses/by/4.0/ |
op_doi |
https://doi.org/10.34657/9336 |
_version_ |
1781697535225626624 |