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Record W1974756783 · doi:10.1039/c4sm02501f

Wrinkling and folding of thin films by viscous stress

2014· article· en· W1974756783 on OpenAlexfundno aff
Sourav Chatterjee, Christina Mcdonald, Jiani Niu, Sachin Velankar, Peng Wang, Rui Huang

Bibliographic record

VenueSoft Matter · 2014
Typearticle
Languageen
FieldEngineering
TopicAdvanced Materials and Mechanics
Canadian institutionsnot available
FundersMcMaster University
KeywordsWrinkleBucklingMaterials scienceWavelengthComposite materialViscoelasticityStress (linguistics)Viscous liquidNatural rubberMechanicsPolymerPhysics

Abstract

fetched live from OpenAlex

We examine the buckling of a thin elastic film floating on a viscous liquid layer which is itself supported on a prestretched rubber sheet. Releasing the prestretch in the rubber induces a viscous stress in the liquid, which in turn induces a compressive stress in the elastic film, leading to buckling. Unlike many previous studies on wrinkling of floating films, the buckling process in the present study is dominated by viscous effects whereas gravitational effects are negligible. An approximate shear lag model predicts the evolution of the stress profile in the unbuckled film that depends on three parameters: the rate at which the prestretch is released, the thickness of the liquid layer, and the length of the elastic film. A linear perturbation analysis is developed to predict the wavelength of wrinkles. Numerical simulations are conducted to predict nonlinear evolution of the wrinkle wavelength and amplitude. Experiments using elastic polymer films and viscous polymer liquids show trends that are qualitatively consistent with the predictions although quantitatively, the experimentally-observed wrinkle wavelengths are longer than predicted. Although this article is focused only on small-strain wrinkling behavior, we show that application of large nominal strains (on the order of 100%) leads to sharply localized folds. Thus this approach may be useful for developing buckled features with high aspect ratio on surfaces.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.104
Threshold uncertainty score0.249

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 head0.003
GPT teacher head0.183
Teacher spread0.180 · 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 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

Citations23
Published2014
Admission routes1
Has abstractyes

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