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Degradation of polyethylene geomembranes exposed to different mine tailings pore waters

2025· article· en· W4412743609 on OpenAlexafffund
Rodrigo Alves e Silva, R. Kerry Rowe, F.B. Abdelaal

Bibliographic record

VenueGeotextiles and Geomembranes · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicLandfill Environmental Impact Studies
Canadian institutionsQueen's University
FundersOntario Ministry of Research and InnovationNatural Sciences and Engineering Research Council of CanadaCoordenação de Aperfeiçoamento de Pessoal de Nível SuperiorCanada Foundation for InnovationTerrafix Geosynthetics
KeywordsGeomembraneTailingsDegradation (telecommunications)PolyethyleneEnvironmental scienceWaste managementGeotechnical engineeringMining engineeringGeologyMaterials scienceEngineeringComposite materialMetallurgy

Abstract

fetched live from OpenAlex

The chemical durability of three 1.5 mm geomembranes (GMBs) – two made from high-density polyethylene (HDPE) and one from a blended polyolefin resin – was examined over 3 years in synthetic tailings pore water solutions. The immersion solutions included a pH 4.0 solution simulating pore water from oxidized copper-zinc mine tailings (PW-4), pH 7.0 (PW-7) and 8.0 (PW-8) solutions simulating arsenic-bearing pore waters from saturated gold mine tailings, and a pH 9.5 solution (PW-9.5) simulating pore water affected by cyanide complexes and/or chemicals from a cyanidation plant. Both HDPE GMBs reached nominal failure in stress-crack resistance at 85 °C, followed by reductions in melt index and, in some cases, tensile strength. The blended GMB showed no degradation. PW-7 was the most aggressive solution in terms of degradation of mechanical properties of the HDPEs, while PW-9.5 was the most aggressive for antioxidant depletion for all three GMBs. The relative performance of the three GMBs in different tailings pore waters could not be predicted from the GMBs’ initial properties or the solutions' chemistry. Overall, results highlight the importance of conducting immersion tests for applications involving complex chemical environments.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.668
Threshold uncertainty score0.838

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 head0.006
GPT teacher head0.211
Teacher spread0.205 · 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

Citations1
Published2025
Admission routes2
Has abstractyes

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