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Record W2072555984 · doi:10.1115/1.4027977

Laser Texturing of Plasma Electrolytically Oxidized Aluminum 6061 Surfaces for Improved Hydrophobicity

2014· article· en· W2072555984 on OpenAlexaff
Bekir Sami Yilbaş, A. Matthews, C. Karataş, A. Leyland, Mazen Khaled, Numan Abu-Dheir, N. Al‐Aqeeli, Xueyuan Nie

Bibliographic record

VenueJournal of Manufacturing Science and Engineering · 2014
Typearticle
Languageen
FieldMaterials Science
TopicSurface Modification and Superhydrophobicity
Canadian institutionsUniversity of Windsor
FundersU.S. Department of Energy
KeywordsMaterials scienceContact angleLaserLaser ablationPlasmaScanning electron microscopeAluminiumTexture (cosmology)AlloyDiffractionSurface energyEnergy-dispersive X-ray spectroscopySpectroscopyLayer (electronics)Analytical Chemistry (journal)Composite materialOpticsChemistry

Abstract

fetched live from OpenAlex

Laser surface texturing of plasma electrolytically oxidized aluminum 6061 alloy has been carried out through a controlled surface ablation under a high pressure nitrogen gas assistance. Morphological and metallurgical changes in the laser-treated region were examined using optical, scanning electron, and atomic force microscopy, energy dispersive X-ray spectroscopy, and X-ray diffraction phase analysis. The hydrophobicity of the textured surface was assessed through water droplet contact angle measurements. It was found that a dense layer with a nanotexture/microtexture is developed at the surface after the laser treatment process. The assessment of the surface characteristics reveals that a superhydrophobic surface results from the laser treatment process; in which case, high water droplet contact angles are measured over the treated surface, which can be explained by known models of texture-induced superhydrophobicity.

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 score0.004

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 integrity0.0000.000
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 head0.008
GPT teacher head0.211
Teacher spread0.204 · 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

Citations27
Published2014
Admission routes1
Has abstractyes

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