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Record W2102095137 · doi:10.5254/rct.13.87916

SERVICE LIFE DETERMINATION OF NITRILE O-RINGS IN HYDRAULIC FLUID

2013· article· en· W2102095137 on OpenAlexaff
Richard J. Pazur, John G. Cormier, K. Korhan-Taymaz

Bibliographic record

VenueRubber Chemistry and Technology · 2013
Typearticle
Languageen
FieldMaterials Science
TopicPolymer Science and PVC
Canadian institutionsDepartment of National Defence
Fundersnot available
KeywordsCompression setThermogravimetric analysisHydraulic fluidNitrileMaterials scienceNitrile rubberUltimate tensile strengthComposite materialNatural rubberChemistryOrganic chemistryThermodynamics

Abstract

fetched live from OpenAlex

ABSTRACT The service life of nitrile O-rings exposed to hydraulic fluid was determined by accelerated aging at nine temperatures and four immersion times. Tensile mechanical properties (elongation and low strain modulus), volume swell, compression set, and chemical crosslink density by solvent swell were measured. Calculated activation energies based on Arrhenius rate behavior ranged from 52 to 65 kJmol−1, approximately 20–30 kJmol−1 lower than nitrile rubber heat aged in air environments. Using a 50% loss of elongation as a failure criterion, an estimate of 15 yr of service life at 23 °C was calculated. This corresponds to a compression set of 50% and an increase of approximately 30% of the chemical crosslink density. Replacement of the plasticizer with the mineral oil and its additives increased total inorganic levels in degraded O-rings as measured by thermogravimetric analysis. Besides additional sulfur and sodium, energy dispersive spectroscopy identified the presence of phosphorous, chlorine, and potassium. Hydraulic oil additives are likely responsible in facilitating the O-ring degradation through lower energy pathways that accelerate nitrile rubber hardening.

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 head0.006
GPT teacher head0.207
Teacher spread0.201 · 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

Citations20
Published2013
Admission routes1
Has abstractyes

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