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Advanced Materials for Solid Oxide Cells: Performance and Degradation Modeling

A special issue of Materials (ISSN 1996-1944). This special issue belongs to the section "Energy Materials".

Deadline for manuscript submissions: 30 April 2026 | Viewed by 17

Special Issue Editors


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Guest Editor
Department of Mechanical Engineering, Colorado School of Mines, Golden, CO, USA
Interests: solid oxide cells (SOCs); proton exchange membrane fuel cells (PEMFCs); alkaline electrolyzers (AELs); microfluidic fuel cells (MFCs); computational fluid dynamics (CFD)

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Guest Editor
Metallurgical and Materials Engineering, Colorado School of Mines, Golden, CO, USA
Interests: impurity degradation; solid oxide cells (SOCs); operando spectroscopy

Special Issue Information

Dear Colleagues,

Solid oxide cells (SOCs), encompassing both fuel cells (SOFCs) and electrolysis cells (SOECs), are key technologies in the transition to sustainable energy systems, offering high efficiency, fuel flexibility, and reversible operation. However, their widespread deployment is hindered by complex performance-limiting mechanisms and long-term degradation under high-temperature operation. Accurate modeling and prediction of SOC performance and degradation are essential to improving their reliability, longevity, and integration into real-world applications.

This Special Issue, titled “Advanced Materials for Solid Oxide Cells: Performance and Degradation Modeling”, aims to gather cutting-edge research focused on the modeling, simulation, and prediction of SOC behavior. We welcome contributions that use multi-physics and multi-scale modeling approaches to examine degradation mechanisms, predict lifetime prediction, and optimize performance under realistic operating conditions. Topics of interest include, but are not limited to, the following: electrochemical modeling of SOC components; thermal and mechanical stress analysis; degradation modeling of electrodes and electrolytes; modeling of redox cycling, poisoning, and thermal aging; and data-driven or machine learning approaches to performance forecasting. We are interested in research papers but are also keenly interested in perspectives that will help guide future work in these areas.

Dr. Omid Babaie Rizvandi
Dr. Anna Staerz
Guest Editors

Manuscript Submission Information

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Keywords

  • solid oxide cells (SOCs) 
  • performance modeling 
  • degradation mechanisms 
  • electrochemical modeling 
  • multi-physics simulation multi-physics
  • lifetime prediction 
  • thermo-mechanical stress
  • redox cycling
  • electrode degradation
  • data-driven modeling
 

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