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Record W2113642408 · doi:10.1098/rspa.2008.0373

Geometry- and velocity-constrained cohesive zones and mixed-mode fracture/adhesion energy of interfaces with periodic cohesive interactions

2008· article· en· W2113642408 on OpenAlexaff
Bin Chen, Peidong Wu, Huajian Gao

Bibliographic record

VenueProceedings of the Royal Society A Mathematical Physical and Engineering Sciences · 2008
Typearticle
Languageen
FieldEngineering
TopicAdhesion, Friction, and Surface Interactions
Canadian institutionsMcMaster University
Fundersnot available
KeywordsCohesive zone modelMaterials scienceStiffnessFracture mechanicsTension (geology)Composite materialStrain energy release rateFracture (geology)MechanicsAdhesionBendingMode (computer interface)PhysicsCompression (physics)

Abstract

fetched live from OpenAlex

We show that the mixed-mode fracture/adhesion energy of an interface with periodically varying cohesive interactions generally depends on the size of the cohesive zone near the tip of a crack along the interface: it is equal to the average cohesive energy of the interface, if the cohesive zone size is much larger than the period of cohesive interaction but becomes the peak value of the local cohesive energy when the opposite is true. It is also interesting that the cohesive zone size can be strongly influenced by the geometry and velocity of the crack. As an example of geometry-constrained cohesive zone, we consider peeling of a thin film on substrate and show that the cohesive zone size under 90° peeling scales with the bending stiffness of the film, while that under 0° peeling scales with the tension stiffness of the film. As an example of a velocity-constrained cohesive zone, we consider crack propagation along an interfacial layer of weak molecular bonds joining two elastic media and show that the cohesive zone size can be altered by an order of magnitude over feasible regimes of crack velocity. These results suggest possible strategies to control fracture/adhesion strength of interfaces in both engineering and biological systems.

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 score0.680
Threshold uncertainty score0.450

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 studies0.0000.001
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 head0.007
GPT teacher head0.205
Teacher spread0.198 · 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

Citations13
Published2008
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of the Royal Society A Mathematical Physical and Engineering SciencesSame topicAdhesion, Friction, and Surface InteractionsFrench-language works237,207