Abstract
It is a present-day challenge to design and develop oxygen-permeable solid oxide fuel cell (SOFC) electrode and electrolyte materials that operate at low temperatures. Herein, by performing high-throughput density functional theory calculations, oxygen vacancy formation energy, Evac, data for a pool of all-inorganic ABO3 and AI0.5AII0.5BO3 cubic perovskites is generated. Using Evac data of perovskites, the area-specific resistance (ASR) data, which is related to both oxygen reduction reaction activity and selective oxygen ion conductivity of materials, is calculated. Screening a total of 270 chemical compositions, 31 perovskites are identified as candidates with properties that are between those of state-of-the-art SOFC cathode and oxygen permeation components. In addition, an intuitive approach to estimate Evac and ASR data of complex perovskites by using solely the easy-to-access data of simple perovskites is shown, which is expected to boost future explorations in the perovskite material search space for genuinely diverse energy applications.
| Original language | English |
|---|---|
| Pages (from-to) | 4160-4165 |
| Number of pages | 6 |
| Journal | Journal of Physical Chemistry Letters |
| Volume | 12 |
| Issue number | 17 |
| DOIs | |
| Publication status | Published - 6 May 2021 |
Bibliographical note
Funding Information:The work presented in this Letter is part of the European project KEROGREEN, which has received funding from the European Union’s Horizon 2020 Research and Innovation Programme under Grant Agreement No. 763909. S.E. acknowledges funding from the initiative “Computational Sciences for Energy Research” of Shell and The Netherlands Organization for Scientific Research (NWO) Grant No. 15CSTT05. This work was sponsored by NWO Exact and Natural Sciences for the use of supercomputer facilities.
Funding
The work presented in this Letter is part of the European project KEROGREEN, which has received funding from the European Union’s Horizon 2020 Research and Innovation Programme under Grant Agreement No. 763909. S.E. acknowledges funding from the initiative “Computational Sciences for Energy Research” of Shell and The Netherlands Organization for Scientific Research (NWO) Grant No. 15CSTT05. This work was sponsored by NWO Exact and Natural Sciences for the use of supercomputer facilities. The work presented in this Letter is part of the European project KEROGREEN, which has received funding from the European Union's Horizon 2020 Research and Innovation Programme under Grant Agreement No. 763909. S.E. acknowledges funding from the initiative "Computational Sciences for Energy Research" of Shell and The Netherlands Organization for Scientific Research (NWO) Grant No. 15CSTT05. This work was sponsored by NWO Exact and Natural Sciences for the use of supercomputer facilities.