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Record W2315296027 · doi:10.1149/05701.1627ecst

Optimization of Infiltration Techniques Used to Construct Ni/YSZ Anodes

2013· article· en· W2315296027 on OpenAlexafffund
Parastoo Keyvanfar, Viola Birss

Bibliographic record

VenueECS Transactions · 2013
Typearticle
Languageen
FieldMaterials Science
TopicAdvancements in Solid Oxide Fuel Cells
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of Alberta
KeywordsYttria-stabilized zirconiaAnodeMaterials scienceWettingElectrolyteChemical engineeringCubic zirconiaScanning electron microscopePorosityElectrochemistryComposite materialElectrodeChemistryCeramic

Abstract

fetched live from OpenAlex

A range of Ni-containing solutions have been infiltrated into a symmetrical tubular half-cell composed of a slip-casted, porous yttria-stabilized zirconia (YSZ) anode support, Ni-YSZ functional layers, a YSZ electrolyte, and a second, outer porous YSZ anode support layer, aiming at the development of high performance anodes that are tolerant to redox-cycling. A combination of surface wettability experiments and optical and electron microscopy imaging has been used to determine how well these solutions penetrate the porous YSZ matrix, then correlating these results with the electrochemical performance in humidified H 2 environments at 800 ⁰C. It is shown that the addition of the Triton-X-100 surfactant to the infiltration solution results in excellent penetration of the YSZ matrix, while the use of urea as a Ni complexing agent does not give good wettability, thus leaving a Ni-rich layer on the outer anode surface. Overall, the use of a two-step process, involving several infiltrations with Ni nitrate solutions containing Triton-X-100, followed by several infiltrations with urea-containing solutions, leads to the best cell performance as well as the best Ni distribution inside the anode layers.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.559
Threshold uncertainty score0.996

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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.000

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 head0.017
GPT teacher head0.268
Teacher spread0.251 · 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.

Study designBench or experimental
Domainnot available
GenreMethods

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
Published2013
Admission routes2
Has abstractyes

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