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Record W3212711853 · doi:10.1016/j.isci.2021.103427

Recoverable underwater superhydrophobicity from a fully wetted state via dynamic air spreading

2021· article· en· W3212711853 on OpenAlexafffund
Yiping Zhao, Zhao Xu, Lu Gong, Shu Yang, Hongbo Zeng, Chunju He, Dengteng Ge, Lili Yang

Bibliographic record

VenueiScience · 2021
Typearticle
Languageen
FieldMaterials Science
TopicSurface Modification and Superhydrophobicity
Canadian institutionsUniversity of Alberta
FundersFundamental Research Funds for the Central UniversitiesNatural Sciences and Engineering Research Council of CanadaNational Natural Science Foundation of ChinaCanada Research ChairsCanada Foundation for Innovation
KeywordsBubbleUnderwaterDragMaterials scienceNanotechnologyMetastabilityContact angleWettingBiofoulingComposite materialMechanicsChemistryPhysicsGeology

Abstract

fetched live from OpenAlex

Maintaining the superhydrophobicity underwater offers drag resistance reduction, antifouling, anti-corrosion, noise reduction, and gas collection for boat hulls and submarine vehicles. However, superhydrophobicity typically does not last long underwater since the Cassie state is metastable. Here, we report a reversible and localized recovery of superhydrophobicity from the fully wetted state via air bubble spreading. Composed of sparse fluorinated chained nanoparticles, the submerged surface shows super-low energy barrier for bubble attachment. Especially the recovered plastron exhibits excellent longevity. Based on a simplified, truncated nanocone model, the dynamic spreading of bubbles is analyzed considering two basic parameters, i.e., surface geometric structure and surface energy (which appeared as intrinsic water contact angle). Numerical simulation results via COMSOL confirms the effect of geometric structure on bubble spreading. This investigation will not only offer new insights for the design of robust recoverable superhydrophobic surfaces but also broaden the applications of superhydrophobic coatings.

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)0.001
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.035
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.002

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 head0.015
GPT teacher head0.239
Teacher spread0.224 · 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 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

Citations15
Published2021
Admission routes2
Has abstractyes

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