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Record W4327595100 · doi:10.1002/fuce.202200069

Tailoring the solid oxide fuel cell anode support composition and microstructure for low‐temperature applications

2023· article· en· W4327595100 on OpenAlexaff
Sajad Vafaeenezhad, Amir Reza Hanifi, Mark Cuglietta, Mohtada Sadrzadeh, Partha Sarkar, Thomas H. Etsell

Bibliographic record

VenueFuel Cells · 2023
Typearticle
Languageen
FieldMaterials Science
TopicAdvancements in Solid Oxide Fuel Cells
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsYttria-stabilized zirconiaAnodeMaterials scienceSolid oxide fuel cellMicrostructureNon-blocking I/OCathodeOxideChemical engineeringCubic zirconiaNickelNickel oxideLayer (electronics)PorosityMetallurgyComposite materialElectrodeChemistryCeramicCatalysis

Abstract

fetched live from OpenAlex

Abstract In this research, the performance of a tubular fuel cell based on a nickel oxide–yttria‐stabilized zirconia (Ni‐YSZ) anode support containing 90 wt% NiO ≈ 82 vol.% of Ni (Ni82) is compared with a cell containing the conventional Ni‐YSZ support with 50 vol.% Ni. A Ni‐YSZ buffer layer with a tailored microstructure was added to the Ni82 support layer to provide intermediate porosity and to reduce the thermal expansion mismatch with the anode functional layer. Both cells were tested using infiltrated Nd 2 NiO 4+δ cathodes. High peak power densities of 790 and 478 mW/cm 2 were achieved at 600 and 550°C, respectively, for the Ni82 cell which was 25% and 87% higher than the performances for the conventional cell at respective temperatures. In addition, no degradation was found during four redox cycles at 550°C, making this support an attractive candidate for low‐temperature solid oxide fuel cell applications.

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 score0.069
Threshold uncertainty score0.899

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)0.0000.001

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 head0.011
GPT teacher head0.264
Teacher spread0.253 · 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

Citations5
Published2023
Admission routes1
Has abstractyes

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