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Record W2765272385 · doi:10.1021/acs.langmuir.7b01350

Superhydrophobicity from the Inside

2017· article· en· W2765272385 on OpenAlexaff
Tomer Simovich, Cameron Ritchie, George Belev, David M. L. Cooper, Robert N. Lamb

Bibliographic record

VenueLangmuir · 2017
Typearticle
Languageen
FieldMaterials Science
TopicSurface Modification and Superhydrophobicity
Canadian institutionsUniversity of SaskatchewanCanadian Light Source (Canada)
FundersAustralian Research Council
KeywordsSynchrotronNanotechnologySubstrate (aquarium)Contact angleAqueous solutionChemical physicsLayer (electronics)DiffusionChemistryMonolayerMaterials scienceChemical engineeringOpticsComposite materialPhysical chemistryThermodynamics

Abstract

fetched live from OpenAlex

The nature of trapped air on submersed ultra-water-repellent interfaces has been investigated. These gaseous layers (plastrons) can last from hours to, in some examples such as the Salvinia molesta fern, months. The interface of submerged superhydrophobic surfaces with carefully controlled micropatterned surface roughness has been probed using synchrotron-based high-resolution X-ray phase tomography. This technique looks in situ, through the aqueous/gas interface in three dimensions. Long-term plastron stability appears to correlate with the appearance of scattered microdroplets <20 μm in diameter that are sandwiched within the 30 μm thick gaseous interfacial layer. These microdroplets are centered on defects or damaged sections within the substrate surface approximately 20-50 μm apart. Such irregularities represent heterogeneous micro/nano-hierarchical structures with varying surface structures and chemistry. The stability of microdroplets is governed by a combination of electrostatic repulsion, contact angle limitations, and a saturated vapor pressure, the latter of which reduces the rate of diffusion of gas out of the air layer, thus increasing underwater longevity. Homogenous surfaces exhibiting purely nano- or micro-regularity do not support such microdroplets, and, as a consequence, plastrons can disappear in <20 h compared with >160 h for surfaces with scattered microdroplets. Such behavior may be a requirement for long-term nonwetting in any system.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.620
Threshold uncertainty score0.999

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 studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.002

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 head0.040
GPT teacher head0.271
Teacher spread0.230 · 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

Citations5
Published2017
Admission routes1
Has abstractyes

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