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Record W4287325021 · doi:10.48550/arxiv.2102.11324

Theoretical limits in detachment strength for axisymmetric bi-material\n adhesives

2021· preprint· en· W4287325021 on OpenAlexfundno aff
Farid H. Benvidi, Mattia Bacca

Bibliographic record

VenuearXiv (Cornell University) · 2021
Typepreprint
Languageen
FieldEngineering
TopicAdhesion, Friction, and Surface Interactions
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaBudapesti Műszaki és Gazdaságtudományi Egyetem
KeywordsAdhesiveMaterials scienceComposite materialFracture mechanicsElastic modulusStrain energy release rateLinear elasticityStress intensity factorSurface roughnessSurface finishStructural engineeringFinite element method

Abstract

fetched live from OpenAlex

Dry adhesives rely on short-ranged intermolecular bonds, hence requiring a\nlow elastic modulus to conform to the surface roughness of the adhered\nmaterial. Under external loads, however, soft adhesives accumulate strain\nenergy, which release drives the propagation of interfacial flaws prompting\ndetachment. The ideal adhesive is then soft but rigid. The solution to this\ncontroversial requirement is a bi-material adhesive having a soft tip, for\nsurface conformation, and a rigid backing, for reduced strain energy release,\nhence, better adhesive strength. This design strategy is widely observed in\nnature across multiple species. However, the detachment mechanisms of these\nadhesives are poorly understood and quantitative analysis of their adhesive\nstrength is still missing. Based on linear elastic fracture mechanics, we\nanalyze the strength of axisymmetric bi-material adhesives. We observed two\nmain detachment mechanisms, namely (i) center crack propagation and (ii) edge\ncrack propagation. If the soft tip is sufficiently thin, mechanism (i)\ndominates and provides stable crack propagation, thereby toughening the\ninterface. We ultimately provide the maximum theoretical strength of these\nadhesives obtaining closed form estimates for the detachment stress independent\nof the crack size, rendering the interface flaw tolerant.\n

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.077
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.040
GPT teacher head0.194
Teacher spread0.154 · 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.

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

Citations0
Published2021
Admission routes1
Has abstractyes

Explore more

Same venuearXiv (Cornell University)Same topicAdhesion, Friction, and Surface InteractionsFrench-language works237,207