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Record W4308043665 · doi:10.1002/app.53312

Mechanical fatigue of recycled and virgin high‐/low‐density polyethylene

2022· article· en· W4308043665 on OpenAlexafffund
Jian Zhang, Valerian Hirschberg, Denis Rodrigue

Bibliographic record

VenueJournal of Applied Polymer Science · 2022
Typearticle
Languageen
FieldMaterials Science
TopicPolymer crystallization and properties
Canadian institutionsUniversité Laval
FundersScience and Engineering Research CouncilNatural Sciences and Engineering Research Council of Canada
KeywordsHigh-density polyethyleneLow-density polyethylenePolymerMaterials sciencePolyethyleneUltimate tensile strengthComposite materialCarbon footprintGreenhouse gas

Abstract

fetched live from OpenAlex

Abstract The high consumption rates of polymers generate large amounts of wastes imposing long‐term adverse effects on the environment combined with a significant carbon footprint. So the appeal for a circular economy is becoming loud enough to take actions. Despite increasing interests for polymer recycling, some reserves about their mechanical performances, especially long‐term properties such as fatigue resistance, are barriers to introduce more recycled polymers back into production lines. In this study, a comparison between the fatigue resistance of virgin and recycled high‐/low‐density polyethylene (H/LDPE) is made to provide more quantitative information to address these concerns. Although some recycled polymers (HDPE) show similar tensile properties compared to virgin ones, significant differences can be observed in their fatigue lifetime. So tensile testing alone is not sufficient to provide a complete information about the overall properties of recycled polymers. Our results show that recycling polymers does not necessarily result in reduced fatigue resistance.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.017
Threshold uncertainty score0.609

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.014
GPT teacher head0.233
Teacher spread0.219 · 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.

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

Citations13
Published2022
Admission routes2
Has abstractyes

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