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Record W2056436519 · doi:10.1115/imece2010-40547

Modeling Stress Relaxation Behaviour of a Hygrothermally Aged PC/ABS Polymer Blend

2010· article· en· W2056436519 on OpenAlexafffund
Mojtaba Haghighi‐Yazdi, Pearl Lee‐Sullivan

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMaterials Science
TopicPolymer crystallization and properties
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials sciencePolycarbonateRelaxation (psychology)Stress relaxationGlass transitionComposite materialRelative humidityMoisturePolymerHumidityModulusTime–temperature superpositionStress (linguistics)Accelerated agingSuperposition principlePolyethyleneThermodynamicsCreepMathematics

Abstract

fetched live from OpenAlex

The competing effects of physical aging and moisture absorption on the relaxation behaviour of a polycarbonate/acrylonitrile-butadiene-styrene (PC/ABS) polymer blend have been investigated. Physical aging was simulated by thermal aging the blend at temperatures up to 80 °C which is below the glass transition temperature of ABS, i.e., the lower of the two components. Another set of samples was exposed to six different relative humidity and temperature combinations. Progressively aged samples in dry and hygrothermal aging conditions were then subjected to stress relaxation tests. Momentary master curves were developed by applying time/aging-time and time/moisture superposition principles for dry and hygrothermally aged specimens, respectively. The experimental data were also fitted with the Kohlrausch-Williams-Watts (KWW) model to determine the initial modulus, relaxation time constant and shape parameter. Comparisons of the first two parameters between thermally aged and hygrothermally aged conditions suggest that physical aging processes dominate absorbed moisture effects in terms of influencing viscoleastic behaviour, especially when the aging temperature approaches 80 °C.

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)0.000
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 score0.010
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

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)0.0040.000

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 head0.017
GPT teacher head0.234
Teacher spread0.218 · 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

Citations1
Published2010
Admission routes2
Has abstractyes

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