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Record W2100538571 · doi:10.1002/mren.201300113

Measuring Shear‐<scp>I</scp>nduced Adhesion of Gecko‐<scp>I</scp>nspired Fibrillar Arrays Using Scanning Probe Techniques

2013· article· en· W2100538571 on OpenAlexafffund
Yasong Li, Cheng Zhang, James Zhou, Carlo Menon, Byron D. Gates

Bibliographic record

VenueMacromolecular Reaction Engineering · 2013
Typearticle
Languageen
FieldEngineering
TopicAdhesion, Friction, and Surface Interactions
Canadian institutionsSimon Fraser University
FundersBritish Columbia Knowledge Development FundNatural Sciences and Engineering Research Council of Canada
KeywordsAdhesiveAdhesionGeckoContact angleMaterials scienceClimbNanotechnologyComposite materialMicrometerWettingContact areaShear (geology)Shear forceMechanical engineeringEngineeringGeology

Abstract

fetched live from OpenAlex

Abstract The natural ability of geckos and spiders to climb almost all surfaces using the compliant, nano‐structured components on their feet provides motivation for making bio‐inspired adhesives. The goal of the studies in this paper is to create an analytical technique for improving the ability to characterize dry adhesives modeled after these biological systems. The technique described herein uses a scanning probe microscope to manipulate a flat test surface in contact with biomimetic fibrillar arrays while monitoring the adhesion forces. Adhesion forces were measured after both normal contact and shear‐induced contact between the nano‐structured fibrils and the test surface. Results confirm that the adhesion forces are higher for bio‐inspired adhesives after a shear‐induced contact. Variations in these forces can be measured across the sample with micrometer‐scale lateral resolution. This method of analysis can be extended to evaluate bio‐inspired dry adhesives with realistic mechanisms of attachment utilized in robotic and similar applications of these materials.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.159
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.017
GPT teacher head0.215
Teacher spread0.197 · 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 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

Citations4
Published2013
Admission routes2
Has abstractyes

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