TY - JOUR
T1 - The Development of New Perovskite-Type Oxygen Transport Membranes Using Machine Learning
AU - Schlenz, Hartmut
AU - Baumann, Stefan
AU - Meulenberg, Wilhelm Albert
AU - Guillon, Olivier
N1 - Funding Information:
Funding: This research was funded by the Helmholtz Innovation Fund (Project No. DB001840).
Funding Information:
This research was funded by the Helmholtz Innovation Fund (Project No. DB001840). We thank the Helmholtz Association of German Research Centers and Forschungs-zentrum Juelich for providing the time and infrastructure necessary to perform the presented work.
Publisher Copyright:
© 2022 by the authors. Licensee MDPI, Basel, Switzerland.
PY - 2022/7/5
Y1 - 2022/7/5
N2 - 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.
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.
KW - ceramic
KW - machine learning
KW - mixed ionic-electronic conducting membrane MIEC
KW - oxygen separation membrane
KW - Pecon.py
KW - perovskite
KW - python programming
KW - valence bond calculations
UR - http://www.scopus.com/inward/record.url?scp=85133834703&partnerID=8YFLogxK
U2 - 10.3390/cryst12070947
DO - 10.3390/cryst12070947
M3 - Article
AN - SCOPUS:85133834703
SN - 2073-4352
VL - 12
JO - Crystals
JF - Crystals
IS - 7
M1 - 947
ER -