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Record W2045017741 · doi:10.1142/s179329201100241x

ON THE FORMATION OF TITANIUM/TITANIUM OXIDE NANOFIBROUS STRUCTURES AND NANOSPHERES USING FEMTOSECOND LASER IN AIR

2011· article· en· W2045017741 on OpenAlexaff
M. Alubaidy, Krishnan Venkatakrishnan, Bo Tan

Bibliographic record

VenueNANO · 2011
Typearticle
Languageen
FieldEngineering
TopicLaser-Ablation Synthesis of Nanoparticles
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsMaterials scienceFemtosecondTitaniumScanning electron microscopeLaserSubstrate (aquarium)PhotocatalysisRaman spectroscopyTitanium oxideMicrostructureOxideNanostructureNanotechnologyComposite materialChemical engineeringOpticsMetallurgy

Abstract

fetched live from OpenAlex

In this paper, we report a unique growth of nanofibrous structures and nanospheres of titanium using femtosecond laser in air and without the need for any type of catalyst. The femtosecond laser was used to generate nanoparts on a titanium substrate. The irradiated substrate is assumed to be subjected to plane stress type of temperature variation and a new method combining finite difference and Runge–Kutta 4 transient thermal model has been developed to calculate the temperature distribution on the top surface of the substrate during laser ablation. A Matlab code has been developed and validated with the known results from the literature. Scanning electron microscopy (SEM), energy-dispersive X-ray (EDX), X-ray diffractograms (XRD) and micro-Raman analysis were conducted to characterize the microstructure and revealed metallic and oxide phases in the nanostructure analyses. Results showed that nanofibers and nanospheres were grown in the order of few hundreds nanometers or more. The effect of the laser power on the energy/pulse and hence the temperature was studied. It was found that high temperature results in the formation of nanofibers while lower temperature results in formation of nanospheres. This first time observation could have potential application in biomedical, optoelectronics and photocatalysis.

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 score0.015
Threshold uncertainty score0.359

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 head0.025
GPT teacher head0.206
Teacher spread0.181 · 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

Citations6
Published2011
Admission routes1
Has abstractyes

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