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Record W2011899280 · doi:10.1155/2009/590763

Critical Time to Nucleation: Graphite and Silicon Nanoparticle Generation by Laser Ablation

2009· article· en· W2011899280 on OpenAlexafffund
Sid Senadheera, Bo Tan, Krishnan Venkatakrishnan

Bibliographic record

VenueJournal of Nanotechnology · 2009
Typearticle
Languageen
FieldEngineering
TopicLaser-induced spectroscopy and plasma
Canadian institutionsToronto Metropolitan University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsNucleationMaterials scienceFemtosecondLaser ablationSiliconLaserGraphiteAblationClassical nucleation theoryOpticsOptoelectronicsThermodynamicsComposite materialPhysics

Abstract

fetched live from OpenAlex

Femtosecond laser ablation hydrodynamics has been analyzed numerically. The nucleation process of two materials (silicon and graphite) in an expanding plume produced by a femtosecond laser ablation is analyzed within the framework of the Zeldovich-Raizer theory (RZ theory). RZ theory is the most appropriate theory applicable to an expanding plume created by a femtosecond laser ablation and has been used to estimate the critical times in the evolution of nanoparticle formation. However, there is no experimental result to directly substantiate the theoretical model on a fundamental level. This paper gives an experimental approach to prove theoretical predictions of critical times to nucleation in laser-ablated graphite and silicon plumes. The critical time to nucleation deduced using the RZ theory was found to be in close agreement with pulse frequency at which fibrous nanoparticle aggregates start to form for both materials. We experimentally showed that the nanoparticle aggregates were produced consistently when the pulse frequency corresponding to the femtosecond laser time interval was shorter than the critical time to begin nucleation. Therefore the nucleation time acts as a threshold time length to begin nucleation.

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 score0.009
Threshold uncertainty score0.305

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 head0.006
GPT teacher head0.219
Teacher spread0.213 · 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

Citations13
Published2009
Admission routes2
Has abstractyes

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