Effects of moisture on (La, A)MnO₃ (A = Ca, Sr, and Ba) solid oxide fuel cell cathodes: a first-principles and experimental study

Abstract

In solid oxide fuel cells (SOFCs), cathode degradation in the presence of moisture is a major concern at higher temperatures. We provide a comprehensive picture of the interaction between moisture and (La, A)MnO3 based SOFC cathodes.

Surface Segregation in Solid Oxide Cell Oxygen Electrodes: Phenomena, Mitigation Strategies and Electrochemical Properties

“…For example, moisture can accelerate SrO segregation on LSM. Sharma et al [241] reported that exposure to 20% moisture enhanced SrO segregation on LSM after annealing at 800 °C for 50 h. Hu et al [242] Kim et al [243] observed La 2 O 3 segregation on LSM surfaces annealed at 800 °C for 5 h in 20% and 40% humidified air that was different as compared with annealing in dry air. Liu et al [197] also reported that exposure to 20% moisture at 800 °C for 200 h led to the partial decomposition of LSM into La 2 O 3 and MnO x .…”

High-throughput density functional perturbation theory and machine learning predictions of infrared, piezoelectric, and dielectric responses

“…Moreover, the synergy among multidisciplinary sciences, along with rapid advancements in the electronic-structure methods, computational resources, and experimental techniques has made the process of materials design faster and far more efficient in some cases. As a result, the community is gradually migrating towards systematic computation-driven materials selection paradigms 9,10,[13][14][15][16][17][18][19][28][29][30][31][32][33][34][35] , where functional materials are screened by establishing a direct link between the macroscopic functionality and the atomic-scale nature of the material. We are in a data-rich, modeling-driven era where trial and error approaches are gradually being replaced by rational strategies 9,[36][37][38] , which couple predictions not only from specific electronic-structure calculations of a given property but also by learning from the existing data using machine learning.…”

“…As a result, the community is gradually migrating towards systematic computation-driven materials selection paradigms, 4,5,[8][9][10][11][12][13][14][21][22][23][24][25][26][27][28][29] where functional materials are screened by establishing a direct link between the macroscopic functionality and the atomic-scale nature of the material. We are in a data-rich, modeling-driven era where trial and error approaches are gradually being replaced by rational strategies, 24,[30][31][32] which couple predictions not only from specific electronicstructure calculations of a given property, but also by learning from the existing data using machine learning.…”

Enhanced Catalytic Activity of LaMnO3 by A-Site Substitution as Air Electrode of Zn–Air Batteries with Attractive Durability

“…Meanwhile, the oxidation state of manganese and the oxygen vacancy concentration can be regulated by the insertion of an external cation into the LaMnO 3 matrix. For instance, according to first-principles calculations, Sharma et al proved that the surface of Ca-, Sr-, and Ba-substituted LaMnO 3 (A = Ca, Sr, and Ba) was more prone to oxygen vacancies, which further accelerated the segregation of cations and affected the surface chemistry of the cathode material . Hu et al reported La 1– x Ca x MnO 3 perovskite–graphene composites as catalysts and discovered that the composites exhibited the highest electrocatalytic activity with a electron transfer number of 3.6 while the doping content of calcium was equal to x = 0.4 .…”