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Record W4229044978 · doi:10.1088/1402-4896/ac6cae

Mechanical properties of carbon nanotube reinforced polyurethane matrix using computational method: a molecular dynamics study

2022· article· en· W4229044978 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenuePhysica Scripta · 2022
Typearticle
Languageen
FieldMaterials Science
TopicCarbon Nanotubes in Composites
Canadian institutionsCarleton University
Fundersnot available
KeywordsMaterials scienceCarbon nanotubeMolecular dynamicsZigzagComposite materialGrapheneNanocompositeModulusThermoplastic polyurethanePolyurethanePolymerMatrix (chemical analysis)NanoparticleNanostructureNanotubeNanotechnologyComputational chemistryElastomer

Abstract

fetched live from OpenAlex

Abstract The reinforcing nanostructures can be made up of nanoparticles, nanosheets or nanofibres such as carbon nanotubes (CNTs) and graphene nanosheets. To investigate the reinforce mechanism, the changes in mechanical behavior of CNT reinforced Polyurethane (PU) matrix with various chirality was studied using molecular dynamics (MD) method in current work. We used the DREIDING and Tersoff force-fields for simulation of the PU and CNT samples, respectively. To report the mechanical properties of pristine PU matrix and reinforced PU/CNT structure, some physical parameters such as interaction energy between polymer chains and nanotube atoms, ultimate strength, and Young’s modulus are calculated. MD outputs indicated inserting CNT with zigzag edge into pristine matrix enlarged the Young’s modulus by 17.10% and the ultimate strength by 25.69%. These results indicated the promising effect of CNT-based nanostructures on the mechanical properties of PU matrix.

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.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.482
Threshold uncertainty score0.942

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 science0.0010.001
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 head0.024
GPT teacher head0.290
Teacher spread0.266 · 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