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Record W4296523069 · doi:10.18280/acsm.460404

Assessment of Polyethylene Geomembrane Properties after Accelerated Thermal Ageing

2022· article· en· W4296523069 on OpenAlexvenueno aff
Addessalam Bezza, S. Bouhelal

Bibliographic record

VenueAnnales de Chimie Science des Matériaux · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicLandfill Environmental Impact Studies
Canadian institutionsnot available
Fundersnot available
KeywordsGeomembraneCrystallinityMelt flow indexPolyethyleneShore durometerMaterials scienceUltimate tensile strengthComposite materialHigh-density polyethylenePolymer

Abstract

fetched live from OpenAlex

Application temperatures and exposure environments are among the causes of premature antioxidant loss in polymer geomembranes. Therefore, over the long term, polymer degradation takes place which is reflected in geomembrane properties. In this work, accelerated thermal ageing of polyethylene geomembrane was carried out in a climatic chamber at 70℃ with 21% oxygen. The evolution of tensile properties, puncture resistance, and shore D hardness with exposure time was assessed. Changes in chemical and crystalline structure and melt flow index were made. According to the findings, geomembrane surface oxidation takes place in the polyethylene matrix after 9 months of exposure. This fact is confirmed by evaluating carbonyl, vinyl, and hydroxyl indexes. Uniaxial tensile properties, puncture resistance, and shore D hardness indicated slight changes after one year of exposure. While the crystallinity index, determined by XRD measurement, has shown an increase with exposure. Further, melt flow index measurements confirmed the absence of chain scission during the early exposure periods. However, for longer periods, chain scission reactions occurred and MI values increased moderately after five months of accelerated aging. All results indicated no significant bulk degradation while maintaining a certain mechanical resistance with applications under a temperature of 70℃. This temperature value is relevant in a waste storage center, but the studied geomembrane is not recommended for this case.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.035
GPT teacher head0.270
Teacher spread0.234 · 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 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

Citations0
Published2022
Admission routes1
Has abstractyes

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