Machine-learning-assisted prediction of long-term performance degradation on solid oxide fuel cell cathodes induced by chromium poisoning
Kaichuang Yang, Jiapeng Liu, Yuhao Wang, Xiangcheng Shi, Jingle Wang, Qiyang Lu, Francesco Ciucci, Zhibin Yang
Abstract
We implement the machine-learning-assisted (MLA) method to predict the long-term stability of Solid Oxide Fuel Cell (SOFC) cathodes under the influence of Cr poisoning.
Topics & Concepts
Solid oxide fuel cellOxideCathodeChromiumTerm (time)Fuel cellsDegradation (telecommunications)Materials scienceChemical engineeringStability (learning theory)Computer scienceMetallurgyChemistryEngineeringMachine learningElectrical engineeringPhysicsTelecommunicationsElectrolyteElectrodePhysical chemistryQuantum mechanicsMachine Learning in Materials ScienceAdvancements in Solid Oxide Fuel CellsFuel Cells and Related Materials