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Record W2520098529 · doi:10.1149/200507.1244pv

Enhancing the Redox Tolerance of Anode Supported Solid Oxide Fuel Cells by Microstructural Modification

2005· article· en· W2520098529 on OpenAlexaff
D. Waldbillig

Bibliographic record

VenueECS Proceedings Volumes · 2005
Typearticle
Languageen
FieldMaterials Science
TopicAdvancements in Solid Oxide Fuel Cells
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsAnodeCermetSolid oxide fuel cellMaterials scienceRedoxYttria-stabilized zirconiaElectrolyteOxideChemical engineeringMicrostructureCubic zirconiaComposite materialMetallurgyElectrodeChemistryCeramic

Abstract

fetched live from OpenAlex

The most commonly used solid oxide fuel cell (SOFC) anode material is a two phase, nickel and yttria stabilized zirconia (Ni/YSZ) cermet. During fuel cell operation, this material is exposed to a reducing environment and thus remains a cermet. However, the metallic component of the anode may reoxidize in a commercial SOFC system due to situations such as seal leakage, fuel supply interruption or system shutdown. The reduction and oxidation of nickel will result in large bulk volume changes, which may have a significant effect on the integrity of interfaces within a fuel cell and thus result in performance degradation. Following an initial study of the redox kinetics and dimensional changes after reduction and oxidation, as well as a baseline characterization of the electrochemical performance degradation and microstructural changes after redox cycling, two modifications to the anode microstructure were made in order to enhance cell redox tolerance. The Ni content of the AFL was functionally graded in order to produce an AFL layer with minimal expansion during oxidation near the electrolyte and good electronic conductivity and thermal expansion match near the anode substrate. An oxidation barrier layer was printed on the bottom of the cell in order to restrict the ability of oxygen to flow into the anode. Both types of microstructural modification significantly improved the cell redox tolerance compared with standard baseline redox tests.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.910

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.009
GPT teacher head0.256
Teacher spread0.247 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations4
Published2005
Admission routes1
Has abstractyes

Explore more

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