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Record W3090201407 · doi:10.6000/1929-5995.2020.09.04

Effect of Ageing on the Mechanical Performance of Thermoset Polymers: A Statistical Approach

2020· article· en· W3090201407 on OpenAlexvenueno aff
Francisco M. dos Santos, Lívia Ávila de Oliveira, Alysson Helton Santos Bueno, Leandro José da Silva, Gilberto García del Pino, Túlio Hallak Panzera

Bibliographic record

VenueJournal of Research Updates in Polymer Science · 2020
Typearticle
Languageen
FieldEngineering
TopicEpoxy Resin Curing Processes
Canadian institutionsnot available
FundersConselho Nacional de Desenvolvimento Científico e TecnológicoCoordenação de Aperfeiçoamento de Pessoal de Nível Superior
KeywordsMaterials scienceThermosetting polymerComposite materialUltimate tensile strengthEpoxyPolymerCuring (chemistry)PolyesterYoung's modulusCompressive strengthAbsorption of waterMoisture

Abstract

fetched live from OpenAlex

The present work investigates the effect of three different ageing processes (natural, 100% relative humidity and salt spray) on the mechanical performance of two thermoset polymers, epoxy and polyester, commonly used as matrix phase in composite materials. A full factorial design is conducted to evaluate the effect of significant factors and interactions on moisture absorption, tensile and compressive strength and modulus of elasticity of the thermosets. Both polymers reveal a decrease in moisture absorption in the saline environment compared to the completely saturated condition (100% RH). Polyester polymers in harsh environments exhibit higher compressive properties compared to those subjected to natural conditioning. In general, polyester polymers, which are most affected by the positive effect of additional cross-linking, have less moisture absorption and superior mechanical properties compared to epoxy, which is more affected by the negative effect of plasticization.

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)0.012
metaresearch head score (Gemma)0.017
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.066

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.017
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.003
Bibliometrics0.0020.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.038
GPT teacher head0.340
Teacher spread0.302 · 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 designObservational
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

Citations2
Published2020
Admission routes1
Has abstractyes

Explore more

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