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Femtosecond laser irradiation of metallic surfaces: effects of laser parameters on superhydrophobicity

2013· article· en· W2000584963 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueNanotechnology · 2013
Typearticle
Languageen
FieldMaterials Science
TopicSurface Modification and Superhydrophobicity
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMaterials scienceWettingFluenceIrradiationFemtosecondLaserContact angleNano-Surface roughnessSurface finishHysteresisLaser ablationComposite materialSurface energyNanotechnologyOpticsCondensed matter physics

Abstract

fetched live from OpenAlex

This work studies in detail the effect of femtosecond laser irradiation process parameters (fluence and scanning speed) on the hydrophobicity of the resulting micro/nano-patterned morphologies on stainless steel. Depending on the laser parameters, four distinctly different nano-patterns were produced, namely nano-rippled, parabolic-pillared, elongated sinusoidal-pillared and triple roughness nano-structures. All of the produced structures were classified according to a newly defined parameter, the laser intensity factor (LIF); by increasing the LIF, the ablation rate and periodicity of the asperities increase. In order to decrease the surface energy, all of the surfaces were coated with a fluoroalkylsilane agent. Analysis of the wettability revealed enhanced superhydrophobicity for most of these structures, particularly those possessing the triple roughness pattern that also exhibited low contact angle hysteresis. The high permanent superhydrophobicity of this pattern is due to the special micro/nano-structure of the surface that facilitates the Cassie-Baxter state.

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.

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 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 score0.002
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0010.001

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 head0.012
GPT teacher head0.218
Teacher spread0.206 · 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