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Record W3097383683 · doi:10.1063/12.0000781

Effect of interface adhesion on the spall strength of particle-reinforced polymer matrix composites

2020· article· en· W3097383683 on OpenAlexaff
Anton Lebar, Andrew Oddy, Rafaela Aguiar, Oren E. Petel

Bibliographic record

VenueAIP conference proceedings · 2020
Typearticle
Languageen
FieldEngineering
TopicTribology and Wear Analysis
Canadian institutionsCarleton University
Fundersnot available
KeywordsMaterials scienceComposite materialSpallSilanesAdhesionUltimate tensile strengthPolymerParticle (ecology)ElastomerSilane

Abstract

fetched live from OpenAlex

Prior research investigating the spall strength in metal alloys has shown that the presence of secondary phase intermetallics can be a source of spall nucleation. In polymer composites with a reinforcing phase, the particle surfaces can be functionalized to improve the surface adhesion between the matrix and filler at the interface, which has been shown to improve the quasi-static strengths of the materials. In the present study, we apply silanes to modify the surface chemistry of micron-sized alumina particles to tailor interface adhesion between the alumina filler and an elastomer matrix Sylgard 184. Two silanes were selected to both increase and decrease interface adhesion respectively to further observe the significance of interface adhesion on dynamic tensile strength. The composites were characterized through spall experiments using a single stage light gas gun at Carleton University. It was found that composites with impeded adhesion showed a drastic reduction in spall strength associated with a loss of adhesion at the interface.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.012
GPT teacher head0.237
Teacher spread0.225 · 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 source (direct Gemma or distilled Codex), 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

Citations5
Published2020
Admission routes1
Has abstractyes

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