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Record W4404069620 · doi:10.3390/ma17225408

Evaluation of Pore-Former Size and Volume Fraction on Tape Cast Porous 430 Stainless Steel Substrates for Plasma Spraying

2024· article· en· W4404069620 on OpenAlexafffund
Olivera Kesler

Bibliographic record

VenueMaterials · 2024
Typearticle
Languageen
FieldEngineering
TopicHigh-Temperature Coating Behaviors
Canadian institutionsUniversity of Toronto
FundersNational Research Council CanadaNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials sciencePorosityVolume fractionComposite materialVolume (thermodynamics)MetallurgyPlasmaFraction (chemistry)ChromatographyChemistry

Abstract

fetched live from OpenAlex

Porous 430L stainless steel disks made by tape casting with various pore-former sizes and volume fractions were evaluated as substrates for solid oxide cell (SOC) fabrication by plasma spraying. This work reports the substrate properties relevant to the SOC operation of disks made by using extra fine metal powder with dense sintering to minimize the fine porosity between particles. In contrast, the coarse porosity is introduced by the pore former. We found that the 60 μm pore former at a 45 vol% fraction has the best application fit; it gives an adequate gas permeability of 3.11 × 10−13 m2 and an average open pore size of 45.90 μm. Compared to a commercial substrate with a similar porosity perimeter/steel area ratio, the porosity and gas permeability are 1.6 and 3 times higher, respectively. The detected maximum surface pore is 49 μm, allowing gas-tight electrolytes fabricated by plasma spray deposition.

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 score0.002

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 head0.026
GPT teacher head0.281
Teacher spread0.255 · 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

Citations3
Published2024
Admission routes2
Has abstractyes

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