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Record W2922299211 · doi:10.1039/c9sm00270g

Magnetically stimulating capillary effect for reversible wet adhesions

2019· article· en· W2922299211 on OpenAlexaff
Meng Li, Qingwen Dai, Qing Jiao, Wei Huang, Xiaolei Wang

Bibliographic record

VenueSoft Matter · 2019
Typearticle
Languageen
FieldEngineering
TopicAdhesion, Friction, and Surface Interactions
Canadian institutionsBell Helicopter Textron (Canada)
FundersChina Scholarship CouncilNational Natural Science Foundation of China
KeywordsCapillary actionChemistryChemical engineeringMaterials scienceComposite materialEngineering

Abstract

fetched live from OpenAlex

Despite fascinating natural examples of switchable adhesives to wet surfaces, strategies for an artificially switching capillary adhesion system in situ remains a challenge. Here, we develop a smart reversible magnetic fluid (MF) meniscus adhesion system whose capillary effect can be regulated by external magnetic stimuli. It is revealed that the MF filled joint between two solid surfaces undergoes alteration of its adhesive properties in response to the external stimulus of a varying magnetic field. Compared with the original capillary force (without stimuli), the stimulated one increases or decreases depending on the distributions of applied magnetic field intensities, allowing for switchable adhesive behavior. In addition to the Laplace pressure, hydrostatic pressure induced by the intensity difference in the magnetic field between the inner and outer surfaces of the meniscus was found to contribute to wet adhesion, which accounted for the reversibility. Theoretical models of reversible adhesions have been built and solved as well, and agree well with the experiment results. Our findings not only provide a deep understanding of MF capillary adhesion, but also provide a new method to design reversible wet adhesion 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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.292
Threshold uncertainty score0.998

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)0.0030.003

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 head0.005
GPT teacher head0.217
Teacher spread0.212 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
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

Citations8
Published2019
Admission routes1
Has abstractyes

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