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Record W2076522001 · doi:10.1149/1.2729145

Electrochemical Testing of Solid Oxide Fuel Cells with Sol Gel Impregnated Plasma Sprayed Electrolytes

2007· article· en· W2076522001 on OpenAlexafffund
Lars Rose, Olivera Kesler, Zhaolin Tang, Alan Burgess

Bibliographic record

VenueECS Transactions · 2007
Typearticle
Languageen
FieldMaterials Science
TopicAdvancements in Solid Oxide Fuel Cells
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsElectrolyteMaterials scienceYttria-stabilized zirconiaAnodeElectrochemistryChemical engineeringSolid oxide fuel cellOxideCathodeSol-gelCubic zirconiaPolarization (electrochemistry)Open-circuit voltageComposite materialElectrodeNanotechnologyCeramicChemistryMetallurgyVoltage

Abstract

fetched live from OpenAlex

Solid Oxide Fuel Cells (SOFCs) have been fabricated on tape cast anode substrates with plasma sprayed (PS) electrolytes and screen printed cathodes. To reduce the porosity in the PS electrolyte layers, spin coating was used to impregnate the as-sprayed electrolytes with yttria stabilized zirconia (YSZ) sols and YSZ composite sol powder suspensions. Fuel cells with electrolytes impregnated with different techniques were electrochemically tested and compared to fuel cells with no sol gel coatings to correlate the sol gel processing parameters with the resulting electrochemical performance changes. The sol gel impregnation significantly increased the open circuit voltage, slightly decreased the series resistance, and substantially decreased the polarization resistance compared to the cells with as-sprayed electrolytes. Impregnation of electrolytes offers the possibility of producing thinner electrolytes in simpler low-energy plasmas (100% nitrogen, no hydrogen) while lowering gas permeability through the electrolyte and improving fuel cell performance.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.011
GPT teacher head0.249
Teacher spread0.238 · 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 source (direct Gemma or distilled Codex), 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
Published2007
Admission routes2
Has abstractyes

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Same venueECS TransactionsSame topicAdvancements in Solid Oxide Fuel CellsFrench-language works237,207