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Record W2901330868 · doi:10.1177/0262489318795967

Effect of processing conditions on the cellular morphology of polyethylene hollow fiber foams for membrane applications

2018· article· en· W2901330868 on OpenAlexaff
Zahir Razzaz, Abolfazl Mohebbi, Denis Rodrigue

Bibliographic record

VenueCellular Polymers · 2018
Typearticle
Languageen
FieldMaterials Science
TopicPolymer Foaming and Composites
Canadian institutionsUniversité Laval
FundersExxon Mobil Corporation
KeywordsMaterials scienceMembraneExtrusionPolyethylenePorosityComposite materialFiberBlowing agentMorphology (biology)Chemical engineeringCharacterization (materials science)Hollow fiber membranePermeability (electromagnetism)NanotechnologyChemistry

Abstract

fetched live from OpenAlex

A continuous method without any solvent is proposed to produce porous hollow fibers for membrane (HFM) applications. In this case, linear low-density polyethylene was combined with azodicarbonamide to produce samples via extrusion. In particular, the processing (chemical blowing agent content and temperature profile) and post-processing (stretching velocity) conditions were optimized to obtain a cellular structure having a high cell density and uniform cell size distribution. From the samples obtained, a complete set of characterization was performed (morphological, mechanical, physical, and gas transport). The results show that HFM having a higher cell density can increase gas permeability, especially for hydrogen. Overall, it is shown that low-cost polyolefins having a suitable cellular structure can be used for gas separation membranes.

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)0.001
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
Metaresearch0.0000.001
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 head0.009
GPT teacher head0.253
Teacher spread0.244 · 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

Citations9
Published2018
Admission routes1
Has abstractyes

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