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Record W3082881523 · doi:10.1039/d0sm00478b

Ice premelting layer of ice–rubber friction studied using resonance shear measurement

2020· article· en· W3082881523 on OpenAlexaff
Florian Lecadre, Motohiro Kasuya, Sylvain Hemette, A. Harano, Yuji Kanno, Kazue Kurihara

Bibliographic record

VenueSoft Matter · 2020
Typearticle
Languageen
FieldMedicine
TopicWinter Sports Injuries and Performance
Canadian institutionsHatch (Canada)
FundersJapan Society for the Promotion of Science
KeywordsNatural rubberPremeltingMaterials scienceComposite materialPolystyreneElasticity (physics)Melting pointPolymer

Abstract

fetched live from OpenAlex

We performed a resonance shear measurement (RSM) based on a low-temperature surface force apparatus to evaluate the frictional properties of the interface between butadiene rubber and ice at various temperatures below 0 °C. Friction between the rubber and ice was high and constant at temperatures below -5 °C, but sharply decreased when the temperature rose above -5 °C. We performed the same measurement by replacing the rubber with polystyrene and silica films which were rigid and exhibited practically minimal elastic deformation in comparison to the rubber. The friction decreased gradually with the increase in temperature from -20 °C to 0 °C at both the polystyrene-ice and the silica-ice interfaces. These results indicated that the elasticity of rubber was responsible for the differences in the rubber-ice interface and the other two samples. To understand the detailed mechanism of friction between the rubber and the ice, we analyzed the obtained RSM data using a physical model. The result indicated that the friction between ice and rubber was determined by the elastic deformation of the rubber film at temperatures below -5 °C, and by the viscosity of the ice premelting layer above -5 °C.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.239
Threshold uncertainty score0.607

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)0.0010.000

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 head0.075
GPT teacher head0.292
Teacher spread0.217 · 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 designObservational
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

Citations8
Published2020
Admission routes1
Has abstractyes

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