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Record W3175765034 · doi:10.1038/s41598-021-92663-5

Microstructural evidence of the toughening mechanisms of polyurethane reinforced with halloysite nanotubes under high strain-rate tensile loading

2021· article· en· W3175765034 on OpenAlexafffund
Rafaela Aguiar, Ronald E. Miller, Oren E. Petel

Bibliographic record

VenueScientific Reports · 2021
Typearticle
Languageen
FieldMaterials Science
TopicClay minerals and soil interactions
Canadian institutionsCarleton University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials scienceHalloysiteComposite materialNanocompositeScanning electron microscopeUltimate tensile strengthMicrostructureCrystallinityToughnessFracture toughness

Abstract

fetched live from OpenAlex

In this study, we have investigated the relationship between the spherulitic morphology and the dynamic tensile response of polyurethane reinforced with Halloysite nanotubes (HNTs). The polyurethane prepolymer is partially silane end-capped and filled with only 0.8 wt.% of acid-treated Halloysite nanotubes. The resultant nanocomposite material presents a 35% higher spall strength compared to the neat polyurethane and 21% higher fracture toughness. We show evidence that the HNTs are not the toughening phase in the nanocomposite, but rather it is their influence on the resultant spherulitic structures which alters the polymer microstructure and leads to a tougher dynamic response. Microstructural characterization is performed via Scanning Electron Microscopy, Atomic Force Microscopy and Field Emission Scanning Electron Microscopy, and crystallinity examination via X-ray diffraction. The spherulitic structures present a brittle fracture character, while the interspherulitic regions are more ductile and show large deformation. The nanocomposite presents a finer and more rigid spherulitic structure, and a more energy dissipative fracture mechanism characterized by a rougher fracture surface with highly deformed interspherulitic regions.

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 score0.007
Threshold uncertainty score0.520

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.022
GPT teacher head0.258
Teacher spread0.236 · 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

Citations14
Published2021
Admission routes2
Has abstractyes

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