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Record W4211138265 · doi:10.1177/00952443211060407

Study Mullins effect of polyurethane reinforcement with halloysite nanotube by molecular dynamics simulation

2022· article· en· W4211138265 on OpenAlexafffund
Mohammadreza Heidari Pebdani

Bibliographic record

VenueJournal of Elastomers & Plastics · 2022
Typearticle
Languageen
FieldMaterials Science
TopicClay minerals and soil interactions
Canadian institutionsCarleton University
FundersCanadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada
KeywordsHalloysiteMaterials scienceComposite materialPolyurethaneStrain rateMolecular dynamicsNanotubeComposite numberVolume fractionComputational chemistryChemistryCarbon nanotube

Abstract

fetched live from OpenAlex

Molecular dynamics simulation was applied to study the irreversible strain through loading and unloading cyclic tests of polyurethane (PU) reinforced with halloysite nanotube (HNT). The influences of the stretching cycle rate, different temperatures, the volume of halloysite nanotube and the density rate of the hard and soft domain of PU were studied on the permanent set. The results illustrate that the residual strain was increasing when the stretching loading is increasing, for example, with increasing strain load to 250% the residual strain increased to 55%. In contrast, the increasing volume fraction of HNT and hard part content of PU lead to lower residual strain. The recovery of the permanent set is achievable by increasing temperature from 1 K to 200 K residual strain is decreased to 52%. An Ogden constitutive and the theory of pseudo-elasticity were adopted to simulate this composite in the ABAQUS software. This model has proposed a reasonable prediction of plastic deformation.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.155
Threshold uncertainty score0.517

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.005
GPT teacher head0.257
Teacher spread0.252 · 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 designSimulation or modeling
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

Citations6
Published2022
Admission routes2
Has abstractyes

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