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Record W4283731197 · doi:10.1002/fam.3091

Carbon fiber damage evolution under flame attack and the role of impurities

2022· article· en· W4283731197 on OpenAlexafffund
Pablo Chávez‐Gómez, Tanja Pelzmann, Darren R. Hall, Cornelia Chilian, Louis Laberge Lebel, Étienne Robert

Bibliographic record

VenueFire and Materials · 2022
Typearticle
Languageen
FieldMaterials Science
TopicFlame retardant materials and properties
Canadian institutionsPolytechnique Montréal
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsImpurityMaterials scienceCatalysisScanning electron microscopePolyacrylonitrileMicrostructureAlkali metalChemical engineeringCarbon fibersChemistryComposite materialOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract Carbon fibers (CFs) are prone to extensive oxidation under fire attack, for instance, in an aircraft fire scenario. This work addresses the damage mechanisms observed on polyacrylonitrile (PAN)‐based CFs with different microstructure exposed to open flames. A fixed‐point technique was developed to follow‐up individual CFs by means of time‐controlled insertion into premixed methane/air flames, followed by scanning electron microscopy (SEM) and energy‐dispersive X‐ray spectroscopy (EDS) analyses. Besides diameter reduction, three localized damage mechanisms were discerned in the presence of impurities, which were quantified by neutron activation analysis (NAA). Severe pitting was ascribed to catalytic oxidation mainly caused by alkali and alkaline earth metals. After an initial period where catalytic reactions between impurities and the carbon surface dominate, the flame stoichiometry governed the CF gasification process, with lean flames being much more aggressive than rich ones. A second mechanism, channeling, was caused by mobile metallic impurities. Some impurities showed an opposite effect, lowering reactivity and thus preventing further catalysis. Amorphous damage with a skin‐peeling effect is believed to be the result of localized impurities at high concentrations and microstructural variations. Hindrance or synergistic effects between impurities are discussed. Finally, apparent axial pit growth rates were determined and compared with other carbonaceous materials, revealing a strong influence of impurities and the flame reactive atmosphere on CF oxidation.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
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 score0.021
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0020.000

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 head0.009
GPT teacher head0.199
Teacher spread0.190 · 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.

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

Citations6
Published2022
Admission routes2
Has abstractyes

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