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Record W2180837850 · doi:10.1063/1.4936251

Axial- and radial-resolved electron density and excitation temperature of aluminum plasma induced by nanosecond laser: Effect of the ambient gas composition and pressure

2015· article· en· W2180837850 on OpenAlexaff
Mahmoud S. Dawood, Ahmad Hamdan, J. Margot

Bibliographic record

VenueAIP Advances · 2015
Typearticle
Languageen
FieldEngineering
TopicLaser-induced spectroscopy and plasma
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsArgonAtomic physicsElectron densityAtmospheric pressureElectron temperaturePlasmaChemistryExcitation temperatureHeliumNanosecondPlasma diagnosticsLaserAnalytical Chemistry (journal)IonEmission spectrumOpticsSpectral line

Abstract

fetched live from OpenAlex

The spatial variation of the characteristics of an aluminum plasma induced by a pulsed nanosecond XeCl laser is studied in this paper. The electron density and the excitation temperature are deduced from time- and space- resolved Stark broadening of an ion line and from a Boltzmann diagram, respectively. The influence of the gas pressure (from vacuum up to atmospheric pressure) and compositions (argon, nitrogen and helium) on these characteristics is investigated. It is observed that the highest electron density occurs near the laser spot and decreases by moving away both from the target surface and from the plume center to its edge. The electron density increases with the gas pressure, the highest values being occurred at atmospheric pressure when the ambient gas has the highest mass, i.e. in argon. The excitation temperature is determined from the Boltzmann plot of line intensities of iron impurities present in the aluminum target. The highest temperature is observed close to the laser spot location for argon at atmospheric pressure. It decreases by moving away from the target surface in the axial direction. However, no significant variation of temperature occurs along the radial direction. The differences observed between the axial and radial direction are mainly due to the different plasma kinetics in both directions.

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 score0.003
Threshold uncertainty score0.436

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 head0.004
GPT teacher head0.209
Teacher spread0.205 · 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

Citations5
Published2015
Admission routes1
Has abstractyes

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