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Record W4294830300 · doi:10.31399/asm.cp.itsc2006p0409

Thermal Spray Deposition of Fuel Cell Interconnect Material

2006· article· en· W4294830300 on OpenAlexaff
E. Garcı́a, Thomas W. Coyle

Bibliographic record

VenueThermal spray · 2006
Typearticle
Languageen
FieldEngineering
TopicHigh-Temperature Coating Behaviors
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsThermal sprayingMaterials scienceMicrostructureDeposition (geology)Electrical resistivity and conductivityGas dynamic cold sprayPlasma torchScanning electron microscopeNozzleMetallurgySolid oxide fuel cellPlasmaComposite materialAnalytical Chemistry (journal)CoatingChemistryAnodeElectrical engineering

Abstract

fetched live from OpenAlex

Abstract In this work two different thermal spray techniques were used to deposit La0.9Sr0.1CrO3 interconnect material: high velocity oxy-fuel (HVOF) using a modified nozzle and atmospheric plasma spray (APS). Two different APS torches were employed: A commercial torch that uses Ar/H2 as plasma forming gases and a new torch design that uses CO2 gas mixtures. A substitute powder with similar physical properties to La0.9Sr0.1CrO3 was employed for the development and optimization of the process parameters to achieve the highest density before the deposition of the La0.9Sr0.1CrO3 on zirconium oxide substrates. The microstructures observed by scanning electron microscopy (SEM) and the phase composition of the coatings obtained from X-ray diffraction analysis are correlated to the spraying characteristics of the different techniques employed. The electrical resistivity of the as-sprayed coatings is discussed in terms of microstructure features and the phase composition. Post-deposition heat treatments were studied in order to reduce the electrical resistivity.

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 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 score0.010
Threshold uncertainty score0.865

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 communication0.0000.000
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 head0.005
GPT teacher head0.188
Teacher spread0.184 · 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

Citations2
Published2006
Admission routes1
Has abstractyes

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