MétaCan
Menu
Back to cohort
Record W2718998089 · doi:10.1002/polb.24384

DC electrorheological response of polyethylene/organically modified layered silicate nanocomposites

2017· article· en· W2718998089 on OpenAlexafffund
Soheil Sadeghi, Mohammad Arjmand, Tieqi Li, Uttandaraman Sundararaj

Bibliographic record

VenueJournal of Polymer Science Part B Polymer Physics · 2017
Typearticle
Languageen
FieldEngineering
TopicVibration Control and Rheological Fluids
Canadian institutionsNova Chemicals (Canada)University of Calgary
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsHigh-density polyethyleneNanocompositePolyethyleneCationic polymerizationSilicateMaterials scienceRelaxation (psychology)Polymer chemistryRheologyChemical engineeringPolymerComposite material

Abstract

fetched live from OpenAlex

The present work reports the electrorheological (ER) response of high-density polyethylene (HDPE)/organically modified silicate layers nanocomposites based on four commercially available HDPE matrices. Two single-site catalyzed bimodal resins, one single-site catalyzed unimodal resin and one Ziegler–Natta catalyzed unimodal resin are studied. It is revealed that the distinct separation of the two modes of the bimodal HDPE resins significantly enhances the ER response. It is proposed that the slower structural relaxation modes introduced by higher molecular weight species in the bimodal HDPE matrices enhance the ER response of the nanocomposites. This is ascribed to the longer induction time for leaking current density, which is an indicator of mobility and release of immobilized cationic surfactants at the silicate layers surface induced by field exposure. It is found that the screening effect of prematurely released cationic surfactants leads to a weaker ER response in nanocomposites whose matrices have faster relaxation modes. © 2017 Wiley Periodicals, Inc. J. Polym. Sci., Part B: Polym. Phys. 2017, 55, 1298–1309

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)0.001
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 score0.021
Threshold uncertainty score0.766

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0020.000
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 head0.019
GPT teacher head0.256
Teacher spread0.237 · 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

Citations4
Published2017
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Polymer Science Part B Polymer PhysicsSame topicVibration Control and Rheological FluidsFrench-language works237,207