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Record W2936535170 · doi:10.3390/jcs3020048

Butyl Rubber-Based Composite: Thermal Degradation and Prediction of Service Lifetime

2019· article· en· W2936535170 on OpenAlexafffund
Phuong Nguyen‐Tri, Ennouri Triki, Tuan Anh Nguyen

Bibliographic record

VenueJournal of Composites Science · 2019
Typearticle
Languageen
FieldMaterials Science
TopicPolymer Nanocomposites and Properties
Canadian institutionsMcGill UniversityUniversité de Montréal
FundersInstitut de Recherche Robert-Sauvé en Santé et en Sécurité du Travail
KeywordsButyl rubberMaterials scienceComposite materialNatural rubberComposite numberCeramic

Abstract

fetched live from OpenAlex

Butyl rubber-based composite (BRC) is one of the most popular materials for the fabrication of protective gloves against chemical and mechanical risks. However, in many workplaces, such as metal manufacturing or automotive mechanical services, its mechanical hazards usually appear together with metalworking fluids (MWFs). The presence of these contaminants, particularly at high temperatures, could modify its properties due to the scission, the plasticization and the crosslinking of the polymer network and thus lead to severe modification of the mechanical and physicochemical properties of material. This work aims to determine the effect of temperature and a metalworking fluid on the mechanical behavior of butyl rubber composite, dealing with crosslinking density, cohesion forces and the elastic constant of BRC, based on Mooney–Rivlin’s theory. The effect of temperature with and without MWFs on the thermo-dynamical properties and morphology of butyl membranes was also investigated. The prediction of service lifetime was then evaluated from the extrapolation of the Arrhenius plot at different temperatures.

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)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.011
GPT teacher head0.217
Teacher spread0.206 · 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

Citations15
Published2019
Admission routes2
Has abstractyes

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