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Simple Fabrication of Superhydrophobic Surfaces Using Atmospheric-Pressure Plasma

2018· article· en· W2906327515 on OpenAlexafffund
E. Vazirinasab, Reza Jafari, Gelareh Momen, Tony Carreira

Bibliographic record

VenueMaterials science forum · 2018
Typearticle
Languageen
FieldMaterials Science
TopicSurface Modification and Superhydrophobicity
Canadian institutionsUniversité du Québec à Chicoutimi
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsWettingMaterials scienceSilicone rubberAtmospheric-pressure plasmaFourier transform infrared spectroscopyScanning electron microscopePlasmaAtmospheric pressurePlasma cleaningPlasma processingSurface modificationChemical engineeringDielectric barrier dischargeContact angleNatural rubberNanotechnologyAnalytical Chemistry (journal)Composite materialChromatographyOptoelectronicsChemistryDielectric

Abstract

fetched live from OpenAlex

Nature-inspired superhydrophobic surfaces have received immense industrial and academic interest due to their non-wettability and self-cleaning properties. To fabricate superhydrophobic silicone rubber surfaces, a simple, environmentally friendly atmospheric-pressure plasma treatment was applied. The effect of diverse plasma processing parameters on the final wettability behavior of the substrates, including plasma power, plasma frequency, number of passes, plasma jet speed, plasma cycle time and distance between the nuzzle outlet and substrate, were analyzed by means of design of experiments (DoE). Surface chemical characterization illustrated the influence of plasma treatment on the chemical composition of the produced silicone rubber. Furthermore, the presence of microstructures as well as the chemical composition of the surface was confirmed using scanning electron microscopy (SEM) and Fourier transform infrared (FTIR) spectroscopy analysis.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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 integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.022
GPT teacher head0.273
Teacher spread0.251 · 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 source (direct Gemma or distilled Codex), 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
Published2018
Admission routes2
Has abstractyes

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