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Record W2036555124 · doi:10.1063/1.4913259

Leaf-like nanotips synthesized on femtosecond laser-irradiated dielectric material

2015· article· en· W2036555124 on OpenAlexafffund
Amirkianoosh Kiani, Nikunj B Patel, Bo Tan, Krishnan Venkatakrishnan

Bibliographic record

VenueJournal of Applied Physics · 2015
Typearticle
Languageen
FieldEngineering
TopicLaser Material Processing Techniques
Canadian institutionsToronto Metropolitan UniversityUniversity of New Brunswick
FundersCanadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada
KeywordsFemtosecondMaterials scienceLaserPlasmaDielectricFabricationNanotechnologyNanometreNanostructureIrradiationOptoelectronicsNanoscopic scaleLaser ablationOpticsComposite material

Abstract

fetched live from OpenAlex

Nanotips are the key nanostructures for the improvement of field emission, flat panel displays, force microscopy, and biosensor applications. We propose a single-step, rapid synthesis method to generate nanotips using femtosecond laser irradiation at megahertz frequency with a background flow of nitrogen gas at ambient conditions. Two different types of leaf-like nanotips can be grown on the target surface: randomly oriented multiple tips growing from a single large droplet and single tips growing from small droplets. In this report, we explain the mechanism accountable for the formation of such nanotips using known concepts of laser breakdown of dielectric materials, plasma plume generation, plasma interactions with incoming laser pulses and surrounding gas, as well as known thermal properties of target material. Nitrogen gas plays an interesting role for the resultant structural changes on the target surface and thus it is given special attention in our discussion. Our unique fabrication technique has enabled us to produce tips with nanoscale apexes with a stem and length ranging from few hundred nanometers to few micrometers.

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 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 score0.105
Threshold uncertainty score0.812

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.016
GPT teacher head0.214
Teacher spread0.198 · 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 teacher head, 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

Citations9
Published2015
Admission routes2
Has abstractyes

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