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Record W2897728543 · doi:10.1063/1.5046181

Influence of a square pulse voltage on argon-ethyl lactate discharges and their plasma-deposited coatings using time-resolved spectroscopy and surface characterization

2018· article· en· W2897728543 on OpenAlexafffund
Morgane Laurent, Edouard Desjardins, Maximilian Meichelboeck, Antoine Belinger, Nicolas Naudé, Luc Stafford, Nicolas Ghérardi, Gaétan Laroche

Bibliographic record

VenuePhysics of Plasmas · 2018
Typearticle
Languageen
FieldMedicine
TopicPlasma Applications and Diagnostics
Canadian institutionsUniversité de MontréalUniversité LavalHôpital Saint-François d'Assise
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsArgonPlasmaAtomic physicsMicrosecondAnalytical Chemistry (journal)SpectroscopyPhysicsMetastabilityMaterials scienceOpticsChemistry

Abstract

fetched live from OpenAlex

By comparing time-resolved optical emission spectroscopy measurements and the predictions of a collisional-radiative model, the evolutions of electron temperature (Te) and number density of argon metastable atoms [n(Arm)] were determined in argon-ethyl lactate dielectric barrier discharges. The influence of a square pulse power supply on Te, n(Arm), and discharge current is evaluated and correlated with the chemistry and the topography of plasma-deposited coatings. Pulsed discharges were found to have shorter (100 ns) but stronger (1 A) current peaks and higher electron temperatures (0.7 eV) than when using a 35 kHz sinusoidal power supply (2 μs, 30 mA, 0.3 eV). The n(Arm) values seemed to be rather stable around 1011 cm−3 with a sinus power supply. In contrast, with a pulse power supply with long time off (i.e., time without discharge) between each pulse, a progressive increase in n(Arm) from 1011 cm−3 up to 1012–1013 cm−3 was observed. When the time off was reduced, this increase was measured in sync with the current peak. The chemical composition of the coatings was not significantly affected by using a pulse signal, whereas the topography was strongly influenced and led to powder formations when reducing the time off.

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 score0.047
Threshold uncertainty score0.645

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 head0.012
GPT teacher head0.253
Teacher spread0.241 · 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
Published2018
Admission routes2
Has abstractyes

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