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Record W2283058056 · doi:10.1149/06602.0267ecst

Stabilization of Ni-YSZ Nanocomposite Anodes by Deposition of a Thin YSZ Overlayer

2015· article· en· W2283058056 on OpenAlexafffund
Aligül Büyükaksoy, Viola Birss

Bibliographic record

VenueECS Transactions · 2015
Typearticle
Languageen
FieldMaterials Science
TopicAdvancements in Solid Oxide Fuel Cells
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of CanadaAlberta Innovates - Technology FuturesUniversity of Calgary
KeywordsMaterials scienceNanocompositeYttria-stabilized zirconiaAnodeElectrodeThin filmOverlayerComposite materialOxideCoatingChemical engineeringDiffusion barrierMetallurgyNanotechnologyCubic zirconiaCeramicLayer (electronics)

Abstract

fetched live from OpenAlex

Lowering the operating temperatures of solid oxide fuel cells (SOFCs) to below 600 °C is projected to enhance the long term stability of these devices by slowing down thermally induced microstructural changes in the electrodes. To minimize the electrode resistance caused by the lowered operating temperatures, the fabrication of nanocomposite Ni-YSZ thin film electrodes with high triple phase boundary (tpb) lengths is a possible approach. However, these nanocomposite electrodes will still undergo microstructural changes that could cause performance degradation. Here, it is shown that the long-term stability of nanocomposite Ni-YSZ anodes can be enhanced by the deposition of a thin, poroous, YSZ coating on the outer surface of the Ni-YSZ electrodes, preventing Ni diffusion out of the pores followed by Ni particle formation. A degradation rate of 0.072 Ω·cm 2 /hour was observed for the standard Ni-YSZ nanocomposite thin film anode, whereas a much lower degradation rate of 0.011 Ω·cm 2 /hour was achieved after applying the YSZ coating.

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 score0.449
Threshold uncertainty score0.490

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 communication0.0000.000
Open science0.0000.000
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 head0.019
GPT teacher head0.265
Teacher spread0.246 · 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

Citations1
Published2015
Admission routes2
Has abstractyes

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