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Record W2585718467 · doi:10.1002/fuce.201600110

Performance of Non‐Destructive Compressive Seals for Reversible Solid Oxide Cells

2017· article· en· W2585718467 on OpenAlexafffund
J. Aicart, Joel Kuhn, Olivera Kesler

Bibliographic record

VenueFuel Cells · 2017
Typearticle
Languageen
FieldMaterials Science
TopicAdvancements in Solid Oxide Fuel Cells
Canadian institutionsCanada Research ChairsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials scienceCompressive strengthSolid oxide fuel cellOxideChemical engineeringFuel cellsComposite materialChemistryMetallurgyPhysical chemistryElectrode

Abstract

fetched live from OpenAlex

Abstract Contrary to hardened glass ceramics, compressive non‐destructive seals for solid oxide cells (SOCs) could enable stack repairs without utilizing destructive separation methods. In light of increasing interest for high temperature steam electrolysis and energy storage, the performances of two commercially available compressive materials were investigated in humidified gas conditions: Thermiculite‐866 and AFS‐170 alumina felts. Mass loss and thermogravimetric (TGA) experiments carried out in inert, oxidizing, and reducing atmospheres at 800 °C showed no influence of the atmosphere on the alumina felts, and a reversible redox behavior for Thermiculite‐866. Thermiculite‐866 showed better cyclability compared to AFS‐170 during SOC thermal cycling experiments. Initial open circuit voltages in dry H2 were measured and compared to those obtained with commercial mica seals. The highest were recorded with Thermiculite‐866, followed by mica and AFS‐170. The species outgassed by Thermiculite‐866 were quantified, and fluorine was found to be the main component released at 800 °C.

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)0.0000.000
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)0.0010.000

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 head0.021
GPT teacher head0.297
Teacher spread0.276 · 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

Citations6
Published2017
Admission routes2
Has abstractyes

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