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Record W2249639918 · doi:10.5254/rct.15.84873

COMPATIBILITY STUDY OF LOW-TEMPERATURE–CAPABLE FLUOROELASTOMERS IN JET FUELS

2015· article· en· W2249639918 on OpenAlexaff
Richard J. Pazur, John G. Cormier

Bibliographic record

VenueRubber Chemistry and Technology · 2015
Typearticle
Languageen
FieldMaterials Science
TopicEngineering and Material Science Research
Canadian institutionsDepartment of National Defence
Fundersnot available
KeywordsMaterials scienceFluorineComposite materialUltimate tensile strengthElongationSwellVolume (thermodynamics)Glass transitionChemical engineeringMetallurgyThermodynamicsPolymer

Abstract

fetched live from OpenAlex

ABSTRACT A series of four peroxide-cured fluoroelastomer (FKM) samples, varying in fluorine level and low-temperature resistance, were assessed in jet fuels JP-8 and JP-8+100 at 125 °C from 1 to 5 weeks. The key factors were fluorine level, FKM chemical structure, and the jet fuel +100 additive package. FKM stiffened upon aging with a corresponding loss in both tensile and elongation at break properties. Volume swell increased with immersion time, whereas any changes in chemical cross-linking density by equilibrium swelling were minor. The +100 additive package had a mild effect in causing property deterioration through stiffening and volume swell increases. Loss of fluorine from attack at the vinylidene fluoride group by basic additives is likely taking place. Lower fluorine containing FKM with flexible side chains was less affected by the additive package and provided the lowest glass-transition temperature (Tg; approaching −30 °C). The volume swells (<15%) are within the realm of an acceptable material to be in contact with jet fuels.

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 score0.003

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)0.0010.000

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 head0.013
GPT teacher head0.263
Teacher spread0.250 · 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

Citations5
Published2015
Admission routes1
Has abstractyes

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