MétaCan
Menu
Back to cohort
Record W4230628528 · doi:10.32920/ryerson.14660682

Residual Stresses in Suspension Plasma Sprayed Electrolytes in Metal-Supported solid Oxide Fuel Cell

2021· preprint· en· W4230628528 on OpenAlexafffund
Alpeshkumar Macwan

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldMaterials Science
TopicAdvancements in Solid Oxide Fuel Cells
Canadian institutionsToronto Metropolitan University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials scienceResidual stressComposite materialMicrostructureYttria-stabilized zirconiaCoatingThermal sprayingFracture toughnessThermal expansionElectrolyteUltimate tensile strengthPorosityToughnessSolid oxide fuel cellCeramicCubic zirconia

Abstract

fetched live from OpenAlex

This study is aimed at identifying the change of residual stresses in suspension plasma sprayed (SPS) 8 mol% YSZ electrolytes on top of porous stainless steel substrate with varying processing parameters and temperatures. The residual stresses in the electrolyte layer are tensile with a value of approximately 90 MPa at room temperature. Porosity, microcracks and segmentation cracks are observed to form in the coating during post-deposition cooling. The decrease of residual stresses with increasing temperature is related to the changes in the Young’s modulus, thermal expansion mismatch, micro-defects and possible creeping of porous stainless steel substrate. The coating fabricated using a torch power of 133 kW and stand-off distance of 90 mm exhibits the highest residual stress due to the formation of a denser microstructure and less cracking. Furthermore, the fracture toughness and interface fracture toughness of the SPS YSZ coating at the optimized condition was determined and discussed.

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)0.000
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 score0.002

Distilled classifier scores by category (both heads)

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)0.0000.000

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 head0.018
GPT teacher head0.283
Teacher spread0.265 · 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

Citations0
Published2021
Admission routes2
Has abstractyes

Explore more

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