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Record W2049856360 · doi:10.1088/0964-1726/22/8/085010

Dry adhesives with sensing features

2013· article· en· W2049856360 on OpenAlexafffund
Jeffrey Krahn, Carlo Menon

Bibliographic record

VenueSmart Materials and Structures · 2013
Typearticle
Languageen
FieldEngineering
TopicAdhesion, Friction, and Surface Interactions
Canadian institutionsSimon Fraser University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsAdhesiveDry cleaningMaterials scienceComposite materialEngineeringWaste management

Abstract

fetched live from OpenAlex

Geckos are capable of detecting detachment of their feet. Inspired by this basic observation, a novel functional dry adhesive is proposed, which can be used to measure the instantaneous forces and torques acting on an adhesive pad. Such a novel sensing dry adhesive could potentially be used by climbing robots to quickly realize and respond appropriately to catastrophic detachment conditions. The proposed torque and force sensing dry adhesive was fabricated by mixing Carbon Black (CB) and Polydimethylsiloxane (PDMS) to form a functionalized adhesive with mushroom caps. The addition of CB to PDMS resulted in conductive PDMS which, when under compression, tension or torque, resulted in a change in the resistance across the adhesive patch terminals. The proposed design of the functionalized dry adhesive enables distinguishing an applied torque from a compressive force in a single adhesive pad. A model based on beam theory was used to predict the change in resistance across the terminals as either a torque or compressive force was applied to the adhesive patch. Under a compressive force, the sensing dry adhesive was capable of measuring compression stresses from 0.11?Pa to 20.9?kPa. The torque measured by the adhesive patch ranged from 2.6 to 10?mN m, at which point the dry adhesives became detached. The adhesive strength was 1.75?kPa under an applied preload of 1.65?kPa for an adhesive patch with an adhesive contact area of 7.07?cm2.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.532
Threshold uncertainty score0.522

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 head0.004
GPT teacher head0.187
Teacher spread0.183 · 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

Citations7
Published2013
Admission routes2
Has abstractyes

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