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Record W2594385221 · doi:10.1021/acsami.6b14789

Wrinkles in Polytetrafluoroethylene on Polystyrene: Persistence Lengths and the Effect of Nanoinclusions

2017· article· en· W2594385221 on OpenAlexaff
Jeffrey T. Paci, Craig T. Chapman, Won‐Kyu Lee, Teri W. Odom, George C. Schatz

Bibliographic record

VenueACS Applied Materials & Interfaces · 2017
Typearticle
Languageen
FieldEngineering
TopicAdvanced Materials and Mechanics
Canadian institutionsUniversity of Victoria
FundersOffice of Naval ResearchInternational Institute for Nanotechnology, Northwestern University
KeywordsWrinkleMaterials sciencePolystyrenePersistence lengthComposite materialPolymerPolytetrafluoroethyleneLength scaleWavelengthMechanics

Abstract

fetched live from OpenAlex

We characterize wrinkling on the surfaces of prestrained polystyrene sheets coated with thin polytetrafluoroethylene skins using a combination of mechanical strain measurements and 3D finite element simulations. The simulations show that wrinkle wavelength increases with skin thickness, in agreement with a well-known continuum model and recent experiments. The wrinkle amplitudes also increase with strain. Nanoinclusions, such as holes and patterned lines, influence wrinkle patterns over limited distances, and these distances are shown to scale with the wrinkle wavelengths. Good agreement between experimental and simulated influence distances is observed. The inclusions provide strain relief, and they behave as if they are attracting adjacent material when the sheets are under strain. The wrinkles have stiffnesses in much the same way as do polymers (but at different length scales), a property that is quantified for polymers using persistence lengths. We show that the concept of persistence length can be useful in characterizing the wrinkle properties that we have observed. However, the calculated persistence lengths do not vary systematically with thickness and strain, as interactions between neighboring wrinkles produce confinement that is analogous to the kinetic confinement of polymers.

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 score0.006
Threshold uncertainty score0.598

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 head0.007
GPT teacher head0.220
Teacher spread0.213 · 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

Citations16
Published2017
Admission routes1
Has abstractyes

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