4.8 Article

Combined electrochemical and X-ray tomography study of the high temperature evolution of Nickel - Yttria Stabilized Zirconia solid oxide fuel cell anodes

Journal

JOURNAL OF POWER SOURCES
Volume 307, Issue -, Pages 604-612

Publisher

ELSEVIER
DOI: 10.1016/j.jpowsour.2015.12.126

Keywords

Solid oxide fuel cell; Anode; Temperature evolution; Model; Three phase boundaries; Polarization resistance

Funding

  1. Global Climate and Energy Project at Stanford University Project [51922]
  2. National Science Foundation [DMR-0907639, DMR-1506925]
  3. U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences [DE-AC02-98CH10886]
  4. Direct For Mathematical & Physical Scien [1506055] Funding Source: National Science Foundation
  5. Direct For Mathematical & Physical Scien
  6. Division Of Materials Research [1506925] Funding Source: National Science Foundation
  7. Division Of Materials Research [1506055] Funding Source: National Science Foundation

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Accelerated ageing of Ni-Yttria Stabilized Zirconia (YSZ) anode functional layers (AFLs) in solid oxide fuel cells (SOFCs) is carried out at 1000-1200 degrees C, the resulting morphological changes are investigated using transmission X-ray microscopy (TXM), and properties are characterized using electrochemical impedance spectroscopy (EIS). Prior to ageing, the as prepared NiO-YSZ AFLs are reduced to Ni-YSZ and then aged at 1100 degrees C for 100 h in order to eliminate early-stage morphological changes. Measured particle size and three phase boundary (TPB) density changes with ageing time and temperature are fit reasonably well using a power-law coarsening model. This model is also used in conjunction with an electrochemical model to predict changes in the anode charge-transfer polarization resistance. The models are used to make predictions of the structural and electrochemical performance evolution of these Ni-YSZ anodes, for cells operated long-term at normal (700-850 degrees C) operating temperatures. Additional experiments to verify the model predictions are suggested. (C) 2016 Elsevier B.V. All rights reserved.

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