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Record W2792245858 · doi:10.1177/1081286518756725

An ellipsoidal cap model for adhesion of a soft particle on a rigid substrate

2018· article· en· W2792245858 on OpenAlexaff
C. Q. Ru

Bibliographic record

VenueMathematics and Mechanics of Solids · 2018
Typearticle
Languageen
FieldEngineering
TopicAdhesion, Friction, and Surface Interactions
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsEllipsoidContact mechanicsClassical mechanicsMechanicsParticle (ecology)Surface energyRADIUSWork (physics)Surface (topology)AdhesionElastic energyExpression (computer science)PhysicsMathematical analysisGeometryMaterials scienceMathematicsFinite element methodThermodynamicsComposite materialGeology

Abstract

fetched live from OpenAlex

Surface energy outside the contact zone, which is not accounted for in the classical Johnson–Kendall–Roberts (JKR) model, can play an essential role in adhesion mechanics of soft particles. An open problem in the adhesion mechanics of soft elastic particles is how to achieve an explicit expression for the surface energy outside the contact zone in terms of the two JKR-type variables ( a, δ), where a is the radius of the contact zone and δ is the relative approach of two bodies. The present work aims to develop an ellipsoidal cap model for the surface energy outside the contact zone of a soft elastic particle on a rigid substrate in terms of the two JKR-type variables ( a, δ). An explicit expression for the surface energy outside the contact zone is derived, and simple asymptotic equations are obtained to determine the two unknowns ( a, δ). The validity and accuracy of the derived expression and asymptotic equations are verified by good agreement with the Young–Dupre equation in the absence of an external applied force, and are also justified by good agreement of the predicted pull-off force with known results available in recent literature.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.701
Threshold uncertainty score0.361

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 head0.027
GPT teacher head0.265
Teacher spread0.238 · 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 designSimulation or modeling
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

Citations1
Published2018
Admission routes1
Has abstractyes

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