TY  - JOUR
AU  - Schlenz, Hartmut
AU  - Baumann, Stefan
AU  - Meulenberg, Wilhelm Albert
AU  - Guillon, Olivier
TI  - The Development of New Perovskite-Type Oxygen Transport Membranes Using Machine Learning
JO  - Crystals
VL  - 12
IS  - 7
SN  - 2073-4352
CY  - Basel
PB  - MDPI
M1  - FZJ-2022-02779
SP  - 947 -
PY  - 2022
AB  - 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.
LB  - PUB:(DE-HGF)16
UR  - <Go to ISI:>//WOS:000832380100001
DO  - DOI:10.3390/cryst12070947
UR  - https://juser.fz-juelich.de/record/908710
ER  -