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Record W2071332302 · doi:10.1063/1.1691454

Modeling the time evolution of laser-induced plasmas for various pulse durations and fluences

2004· article· en· W2071332302 on OpenAlexaff
S. Laville, François Vidal, T. W. Johnston, Mohamed Chaker, Boris Le Drogoff, O. Barthélemy, J. Margot, Mohamad Sabsabi

Bibliographic record

VenuePhysics of Plasmas · 2004
Typearticle
Languageen
FieldEngineering
TopicLaser-induced spectroscopy and plasma
Canadian institutionsUniversité de MontréalInstitut National de la Recherche Scientifique
Fundersnot available
KeywordsPlasmaPhysicsLaserAtomic physicsFluenceElectron temperatureElectron densityAblationPulse (music)ElectronSaturation (graph theory)Pulse durationLaser ablationRadiative transferOptics

Abstract

fetched live from OpenAlex

In this paper, the expansion in ambient air of a plasma produced by laser ablation from an aluminum target is studied with the help of a one-dimensional fluid code that includes a consistent description of ablation and of the subsequent plasma expansion. Two limiting cases for the expansion geometry have been considered: Cartesian and hemispheric. The plasma temperature and electron density as a function of the space coordinate and time have been obtained for laser pulse durations of 100 fs, 200 ps, and 5 ns, and laser fluences up to a few tens of J/cm2. Simulation results are in good agreement with experiments. The code shows that for times typically longer than 1 μs, the plasma space-averaged temperature and electron density are nearly independent of the laser parameters and of the chosen expansion geometry. For a given pulse duration the plasma temperature first increases with the fluence and then reaches a saturation value. It is shown that most of the observed plasma behavior can be explained by radiative cooling.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.013
GPT teacher head0.221
Teacher spread0.208 · 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 designSimulation or modeling
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

Citations40
Published2004
Admission routes1
Has abstractyes

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