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Record W2619890513 · doi:10.14447/jnmes.v12i4.201

Effect of Sintering Temperature on the Mechanical Properties of Film Gd 0.2 Ce 0.8 O 1.9 Electrolyte for SOFCs Using Nanoindentation

2009· article· en· W2619890513 on OpenAlexvenueno aff
M. Morales, J.J. Roa, X.G. Capdevila, M. Segarra, S. Piñol

Bibliographic record

VenueJournal of New Materials for Electrochemical Systems · 2009
Typearticle
Languageen
FieldMaterials Science
TopicAdvancements in Solid Oxide Fuel Cells
Canadian institutionsnot available
Fundersnot available
KeywordsNanoindentationMaterials scienceElectrolyteSinteringScanning electron microscopeComposite materialMicrostructureElastic modulusField emission microscopyThin filmIndentationNanotechnologyOpticsChemistry

Abstract

fetched live from OpenAlex

The mechanical properties of thin film gadolinia doped ceria (Gd0.2Ce0.8O1.9, GDC) electrolyte, for solid oxide fuel cells (SOFCs), with different levels of sintering density were investigated by the nanoindentation technique. Electrolyte thin film supported on Ni-GDC cermet was made by co-sintering at several temperatures between 1350 and 1450 oC. The microstructures of the electrolyte films and the cells performances were studied by scanning electron microscope (SEM) and current-voltage tests, respectively. In order to determine the mechanical properties, a Berkovich indenter was used at different applied loads (30, 50 and 100 mN). Plastic deformation took place, so Oliver and Pharr equations must be applied to evaluate the hardness and Youngrs modulus of the electrolyte film. The residual nanoindentations were observed by optical microscope (M.O.) and field emission scanning electron microscope (FE-SEM). The present study reveals that the nanoindentation is a non-destructive and ideal technique to determinate the quality and the mechanical properties of the thin film of a SOFC. The results also show that the hardness decreases with the increasing of the applied load,  which is attributed to the indentation size effect.

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)0.002
metaresearch head score (Gemma)0.001
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 score0.006
Threshold uncertainty score0.633

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.021
GPT teacher head0.289
Teacher spread0.268 · 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

Citations11
Published2009
Admission routes1
Has abstractyes

Explore more

Same venueJournal of New Materials for Electrochemical SystemsSame topicAdvancements in Solid Oxide Fuel CellsFrench-language works237,207