Predictive Model for Catalytic Methane Pyrolysis

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Predictive Model for Catalytic Methane Pyrolysis. / Pototschnig, Ulrich; Matas, Martin; Scheiblehner, David et al.
in: Journal of physical chemistry C (C, Nanomaterials and interfaces), Jahrgang 128.2024, Nr. 22, 25.05.2024, S. 9034-9040.

Publikationen: Beitrag in FachzeitschriftArtikelForschung(peer-reviewed)

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@article{b802ac0996594a5281e73509ad452d31,
title = "Predictive Model for Catalytic Methane Pyrolysis",
abstract = "Methane pyrolysis provides a scalable alternative to conventional hydrogen production methods, avoiding greenhouse gas emissions. However, high operating temperatures limit economic feasibility on an industrial scale. A major scientific goal is, therefore, to find a catalyst material that lowers operating temperatures, making methane pyrolysis economically viable. In this work, we derive a model that provides a qualitative comparison of possible catalyst materials. The model is based on calculations of adsorption energies using density functional theory. Thirty different elements were considered. Adsorption energies of intermediate molecules in the methane pyrolysis reaction correlate linearly with the adsorption energy of carbon. Moreover, the adsorption energy increases in magnitude with decreasing group number in the d-block of the periodic table. For a temperature range between 600 and 1200 K and a normalized partial pressure range for H 2 between 10 -1 and 10 -5, a total of 18 different materials were found to be optimal catalysts at least once. This indicates that catalyst selection and reactor operating conditions should be well-matched. The present work establishes the foundation for future large-scale studies of surfaces, alloy compositions, and material classes using machine learning algorithms.",
author = "Ulrich Pototschnig and Martin Matas and David Scheiblehner and David Neuschitzer and Robert Obenaus-Emler and Helmut Antrekowitsch and David Holec",
note = "Publisher Copyright: {\textcopyright} 2024 The Authors. Published by American Chemical Society.",
year = "2024",
month = may,
day = "25",
doi = "10.1021/acs.jpcc.4c01690",
language = "English",
volume = "128.2024",
pages = "9034--9040",
journal = "Journal of physical chemistry C (C, Nanomaterials and interfaces)",
issn = "1932-7447",
publisher = "American Chemical Society",
number = "22",

}

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TY - JOUR

T1 - Predictive Model for Catalytic Methane Pyrolysis

AU - Pototschnig, Ulrich

AU - Matas, Martin

AU - Scheiblehner, David

AU - Neuschitzer, David

AU - Obenaus-Emler, Robert

AU - Antrekowitsch, Helmut

AU - Holec, David

N1 - Publisher Copyright: © 2024 The Authors. Published by American Chemical Society.

PY - 2024/5/25

Y1 - 2024/5/25

N2 - Methane pyrolysis provides a scalable alternative to conventional hydrogen production methods, avoiding greenhouse gas emissions. However, high operating temperatures limit economic feasibility on an industrial scale. A major scientific goal is, therefore, to find a catalyst material that lowers operating temperatures, making methane pyrolysis economically viable. In this work, we derive a model that provides a qualitative comparison of possible catalyst materials. The model is based on calculations of adsorption energies using density functional theory. Thirty different elements were considered. Adsorption energies of intermediate molecules in the methane pyrolysis reaction correlate linearly with the adsorption energy of carbon. Moreover, the adsorption energy increases in magnitude with decreasing group number in the d-block of the periodic table. For a temperature range between 600 and 1200 K and a normalized partial pressure range for H 2 between 10 -1 and 10 -5, a total of 18 different materials were found to be optimal catalysts at least once. This indicates that catalyst selection and reactor operating conditions should be well-matched. The present work establishes the foundation for future large-scale studies of surfaces, alloy compositions, and material classes using machine learning algorithms.

AB - Methane pyrolysis provides a scalable alternative to conventional hydrogen production methods, avoiding greenhouse gas emissions. However, high operating temperatures limit economic feasibility on an industrial scale. A major scientific goal is, therefore, to find a catalyst material that lowers operating temperatures, making methane pyrolysis economically viable. In this work, we derive a model that provides a qualitative comparison of possible catalyst materials. The model is based on calculations of adsorption energies using density functional theory. Thirty different elements were considered. Adsorption energies of intermediate molecules in the methane pyrolysis reaction correlate linearly with the adsorption energy of carbon. Moreover, the adsorption energy increases in magnitude with decreasing group number in the d-block of the periodic table. For a temperature range between 600 and 1200 K and a normalized partial pressure range for H 2 between 10 -1 and 10 -5, a total of 18 different materials were found to be optimal catalysts at least once. This indicates that catalyst selection and reactor operating conditions should be well-matched. The present work establishes the foundation for future large-scale studies of surfaces, alloy compositions, and material classes using machine learning algorithms.

UR - http://www.scopus.com/inward/record.url?scp=85194271427&partnerID=8YFLogxK

U2 - 10.1021/acs.jpcc.4c01690

DO - 10.1021/acs.jpcc.4c01690

M3 - Article

VL - 128.2024

SP - 9034

EP - 9040

JO - Journal of physical chemistry C (C, Nanomaterials and interfaces)

JF - Journal of physical chemistry C (C, Nanomaterials and interfaces)

SN - 1932-7447

IS - 22

ER -