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Record W4290050154 · doi:10.1002/pen.26049

Crosslinked polyethylene: A review on the crosslinking techniques, manufacturing methods, applications, and recycling

2022· review· en· W4290050154 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenuePolymer Engineering and Science · 2022
Typereview
Languageen
FieldEnvironmental Science
TopicRecycling and Waste Management Techniques
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsMaterials scienceReusePolyethyleneDuctility (Earth science)PolymerComposite materialProcess engineeringWaste managementCreepEngineering

Abstract

fetched live from OpenAlex

Abstract One of the most used resins in the plastics industry is polyethylene (PE). Although PE has good impact resistance and ductility, its low maximum use temperature and mechanical strength limit some commercial development, especially for load‐bearing applications. To get better overall performances, crosslinking is performed to improve the chemical, mechanical, and thermal properties of PE. Although PE can be crosslinked by using various chemical and physical methods, this makes the resulting polymers more difficult to recycle since a three‐dimensional (3D) network is created. In this review, we first describe the different crosslinking techniques for PE to manufacture crosslinked PE (XLPE) parts. Then, as more than half of the XLPE‐based products are disposed directly after use, we present several options to reuse and recycle these products to overcome this environmental issue and find a sustainable solution. A focus is made on mechanical recycling and de‐crosslinking techniques for XLPE to generate recycled‐XLPE (r‐XLPE). Finally, a conclusion is presented on the current situation and research gaps that must be filled by future works.

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.

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)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.997
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
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 head0.035
GPT teacher head0.337
Teacher spread0.303 · 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