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Record W2577413518 · doi:10.1063/1.4973467

Laser stimulated plasma-induced luminescence for on-air material analysis

2017· article· en· W2577413518 on OpenAlexafffund
S. Veltri, M. Barberio, Chiara Liberatore, M. Scisciò, Antoine Laramée, L. Palumbo, François Légaré, P. Antici

Bibliographic record

VenueApplied Physics Letters · 2017
Typearticle
Languageen
FieldEngineering
TopicLaser-induced spectroscopy and plasma
Canadian institutionsInstitut National de la Recherche Scientifique
FundersNatural Sciences and Engineering Research Council of CanadaFonds de recherche du Québec – Nature et technologiesCanadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada
KeywordsCathodoluminescenceLuminescencePhotoluminescencePlasmaLaserMaterials scienceElectronRange (aeronautics)OptoelectronicsAtomic physicsAnalytical Chemistry (journal)OpticsChemistryPhysicsComposite material

Abstract

fetched live from OpenAlex

In this work, we present a method for performing analysis of the chemical composition and optical properties of materials using In-Air Plasma-Induced Luminescence. This is achieved by interaction of a focused high-energy laser with air, an interaction that produces a sub-millimetric plasma. The energetic electrons generated and accelerated in the plasma at energies higher than 5 keV reach the target surface of the sample to be analyzed, causing luminescence emission and plasmonic resonance. Each material is characterized by different chemical and optical properties that can be determined with the above-described technique. As such, our method allows obtaining an exact analysis of the sample, covering surfaces in the range of tens of mm2, in only a few minutes. We show that the acquired information with our method is identical to what obtained with more sophisticated methods, such as SEM-cathodoluminescence and photoluminescence.

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 categoriesMeta-epidemiology (narrow)
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 score0.080
Threshold uncertainty score1.000

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 head0.015
GPT teacher head0.236
Teacher spread0.221 · 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.

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

Citations7
Published2017
Admission routes2
Has abstractyes

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