Journal Article

FZJ-2022-02779

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png The Development of New Perovskite-Type Oxygen Transport Membranes Using Machine Learning

Schlenz, H. (Corresponding author)FZJ* ; Baumann, S.FZJ* ; Meulenberg, W. A.FZJ* ; Guillon, O.FZJ*

2022
MDPI Basel

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Please use a persistent id in citations: http://hdl.handle.net/2128/31505  doi:10.3390/cryst12070947

Abstract: The aim of this work is to predict suitable chemical compositions for the development of new ceramic oxygen gas separation membranes, avoiding doping with toxic cobalt or expensive rare earths. For this purpose, we have chosen the system Sr1−xBax(Ti1−y−zVyFez)O3−δ (cubic perovskite-type phases). We have evaluated available experimental data, determined missing crystallographic information using bond-valence modeling and programmed a Python code to be able to generate training data sets for property predictions using machine learning. Indeed, suitable compositions of cubic perovskite-type phases can be predicted in this way, allowing for larger electronic conductivities of up to σe = 1.6 S/cm and oxygen conductivities of up to σi = 0.008 S/cm at T = 1173 K and an oxygen partial pressure pO2 = 10−15 bar, thus enabling practical applications.

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Contributing Institute(s):

1. Werkstoffsynthese und Herstellungsverfahren (IEK-1)
2. JARA-ENERGY (JARA-ENERGY)

Research Program(s):

1. 1232 - Power-based Fuels and Chemicals (POF4-123) (POF4-123)

Appears in the scientific report 2022

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