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Record W3095860987 · doi:10.1063/12.0001015

Synthesis and mechanical characterization of polyurethane reinforced with halloysite nanotubes

2020· article· en· W3095860987 on OpenAlexaff
Rafaela Aguiar, Anton Lebar, Andrew Oddy, Ronald E. Miller, Oren E. Petel

Bibliographic record

VenueAIP conference proceedings · 2020
Typearticle
Languageen
FieldMaterials Science
TopicClay minerals and soil interactions
Canadian institutionsCarleton University
Fundersnot available
KeywordsHalloysiteMaterials scienceNanocompositePolyurethaneComposite materialUltimate tensile strengthPolymerPolymer nanocompositePolypropyleneElastomer

Abstract

fetched live from OpenAlex

Polymer composites containing nano-additive reinforcements have attracted much attention in recent years, enabling tunable properties that benefit certain material applications. In the present work, polyurethane-based nanocomposites were prepared with natural halloysite nanotubes, a clay mineral with the empirical formula Al2Si2O5(OH)4. Mechanical properties of the polymer nanocomposite system are investigated under quasi-static loading and high-strain-rate conditions. Under static loading, the nanocomposite presents an elastomeric behaviour, while under dynamic loading, a glassy-like response. Given that halloysite nanotubes can be uniformly dispersed at the polyurethane matrix, the interference of the nanotubes on polyurethane's fracture behaviour is investigated. Halloysite was incorporated into polypropylene glycol, tolylene 2,4-diisocyanate based polyurethane, with 4,4'-methylenebis(2-chloroaniline) as a curative. The reinforcement of the polymer is seen through comparisons of the ultimate tensile strength, strain to failure and spall strength between the pristine and nanocomposite polymer. Samples recovered from spall testing are examined via SEM to explore the fracture mechanism and halloysite dispersion.

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)0.000
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 score0.009
Threshold uncertainty score0.385

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.024
GPT teacher head0.233
Teacher spread0.209 · 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

Citations3
Published2020
Admission routes1
Has abstractyes

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