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Record W2527357081 · doi:10.1680/jgein.16.00025

Diffusion of multiwall carbon nanotubes through a high-density polyethylene geomembrane

2016· article· en· W2527357081 on OpenAlexafffund
Pooneh T. Saheli, R. Kerry Rowe, Elijah J. Petersen, Denis M. O’Carroll

Bibliographic record

VenueGeosynthetics International · 2016
Typearticle
Languageen
FieldEngineering
TopicNanotechnology research and applications
Canadian institutionsWestern UniversityQueen's University
FundersNatural Sciences and Engineering Research Council of CanadaKillam Trusts
KeywordsGeomembraneGeosyntheticsCarbon nanotubeMaterials scienceDiffusionPolyethyleneComposite materialHigh-density polyethyleneCarbon fibersGeotechnical engineeringEngineeringComposite numberThermodynamics

Abstract

fetched live from OpenAlex

The new applications for carbon nanotubes (CNTs) in various fields and consequently their greater production volume have increased their potential release to the environment. Landfills are one of the major locations where carbon nanotubes are expected to be disposed, and it is important to ensure that they can limit the release of CNTs. Diffusion of multiwall carbon nanotubes (MWCNTs) dispersed in an aqueous media through a high-density polyethylene (HDPE) geomembrane (as a part of the landfill barrier system) was examined. Based on the laboratory tests, the permeation coefficient was estimated to be less than 5.1 × 10 −15 m 2 /s. The potential performance of an HDPE geomembrane and geosynthetic clay liner (GCL) as parts of a composite liner in containing MWCNTs was modelled for six different scenarios. The results suggest that the low value of permeation coefficient of an HDPE geomembrane makes it an effective diffusive barrier for MWCNTs, and by keeping the geomembrane defects to minimum during the construction (e.g., the number of holes and length of wrinkles) a composite liner commonly used in municipal solid waste landfills will effectively contain MWCNTs.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.018
Threshold uncertainty score0.335

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 head0.008
GPT teacher head0.225
Teacher spread0.218 · 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

Citations25
Published2016
Admission routes2
Has abstractyes

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