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Record W2332653571 · doi:10.1021/ie500214e

Manufacturing Process for in Situ Reaction-Bonded Porous SiC Ceramics Using a Combination of Graft Polymerization and Sol–Gel Approaches

2014· article· en· W2332653571 on OpenAlexaff
Omid Ebrahimpour, Charles Dubois, Jamal Chaouki

Bibliographic record

VenueIndustrial & Engineering Chemistry Research · 2014
Typearticle
Languageen
FieldMaterials Science
TopicAdvanced ceramic materials synthesis
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsMaterials scienceMulliteSinteringPorosityCeramicComposite materialPolymerizationMicrostructureGreen bodySol-gelFlexural strengthChemical engineeringPolymerNanotechnology

Abstract

fetched live from OpenAlex

A novel process was successfully developed to manufacture mullite-bonded porous SiC ceramics. In this technique, SiC particles were coated with a sintering additive (alumina) and pore former (high density polyethylene) in a two-step process composed of sol–gel and in situ polymerization techniques, respectively. After the formation of a green body, the porous samples were produced by pressureless sintering under air. The microstructures, phase components, and physical and mechanical properties of the developed porous ceramics were examined and compared with materials obtained by the traditional process. The results interpretation revealed that the flexural strength (35%) and porosity (8%), as well as the pore size of the samples fabricated by the novel process significantly increased as compared to regular ceramic parts. Further characterization indicated that the enhanced properties of the porous ceramics came from the good dispersion of the sintering additive and pore former into the SiC particles and from the higher mullite formation.

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)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.110
GPT teacher head0.323
Teacher spread0.213 · 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

Citations7
Published2014
Admission routes1
Has abstractyes

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