On the Predictions of Carbon Deposition on the Nickel Anode of a SOFC and Its Impact on Open-Circuit Conditions
Source abstract
Abstract
Previous thermodynamic analyses of carbon formation in SOFCs assumed that graphite could be used to represent the properties of carbon formed in the anode. It is generally observed, however, that catalytically grown carbon nanofibers (CNF) are more likely to form in the SOFC anode with nickel catalysts. The energetic and entropic properties of CNF are different from those of graphite. We compare equilibrium results based on thermochemical properties for graphite, to new results based on a previously reported value of an empirically determined Gibbs free energy for carbon fibers grown on a nickel support (with fitted values of H ° CNF = 54.46 kJ/mol and S ° CNF = 68.90 J/mol/K for a nickel crystal size of 5.4 nm). There is little difference in predictions of carbon formation under open-circuit conditions between the two carbon types for methane mixtures, with graphite predicted to form at lower temperatures than CNF. There is a much bigger difference in predictions for methanol mixtures, especially at low steam-carbon ratios. The differences for propane are even more pronounced, and the improved predictions assuming CNF are in closer agreement with past observations. We show a strong dependence of CNF formation and "coking threshold" on nickel crystallite size, supporting previous reports that the nickel particle size is a dominating parameter for controlling filament growth. If both carbon types are included in the calculations, only the thermodynamically favored form (i.e., the type having the lowest formation energy) exists. Predicted Nernst potentials are more-or-less independent of the carbon type and in agreement with measured open-circuit voltages.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Advancements in Solid Oxide Fuel Cells · Electrocatalysts for Energy Conversion · Fuel Cells and Related Materials
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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