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Impact of mechanical reprocessing on degradation and performance of PA 11 and PA 11–LDPE blends

2025· article· en· W4412456018 on OpenAlexaff
Johanna K. Morales, Rose Mary Michell, Denis Rodrigue

Bibliographic record

VenuePolymer Degradation and Stability · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicRecycling and Waste Management Techniques
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsLow-density polyethyleneMaterials scienceDegradation (telecommunications)Composite materialPolymer blendPolyethyleneChemical engineeringPolymer sciencePolymerCopolymerEngineering

Abstract

fetched live from OpenAlex

This study examines the mechanical recycling of a virgin PA 11 and a post-consumer PA 11–low density polyethylene (LDPE) (90/10) blend over ten reprocessing cycles. Fourier transform infrared spectroscopy (FTIR) and proton nuclear magnetic resonance (¹H NMR) analyses revealed changes in the intensity and position of specific absorption bands and proton signals, indicating progressive chain scission and molecular rearrangements. A carbonyl band was identified in the virgin PA 11 after reprocessing, a confirmation of thermo-oxidative degradation. Mechanical testing showed gradual reductions in elastic modulus, stress at break, and impact strength, with significant deterioration from the third cycle onward. Rheological analysis revealed consistent decreases in storage modulus ( G′ ), loss modulus ( G″ ), complex viscosity ( η* ), and changes in damping factor (tan δ ), reflecting lower molecular weight and altered viscoelastic behavior. This was further confirmed via Cole–Cole and van Gurp–Palmen plots. In the post-consumer blend, scanning electron microscopy (SEM) showed progressive coalescence of LDPE droplets, contributing to reduced interfacial area and decreased impact resistance. In general, the results showed that virgin PA 11 retains acceptable performance up to three cycles, while the post-consumer blend exhibits faster and more pronounced degradation driven by both chemical and morphological changes particularly due to droplet coalescence and loss of interfacial area.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.154
Threshold uncertainty score0.331

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)0.0000.000

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 head0.017
GPT teacher head0.276
Teacher spread0.259 · 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 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

Citations5
Published2025
Admission routes1
Has abstractyes

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