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Record W3043806594 · doi:10.1063/5.0010387

Electrical characterization of positive and negative pulsed nanosecond discharges in water coupled with time-resolved light detection

2020· article· en· W3043806594 on OpenAlexafffund
Ahmad Hamdan, Jérémy Gorry, Thomas Merciris, J. Margot

Bibliographic record

VenueJournal of Applied Physics · 2020
Typearticle
Languageen
FieldMedicine
TopicPlasma Applications and Diagnostics
Canadian institutionsUniversité de Montréal
FundersFonds de recherche du Québec – Nature et technologiesNatural Sciences and Engineering Research Council of CanadaCanada Foundation for Innovation
KeywordsNanosecondPolarity symbolsWaveformPhotomultiplierStreamer dischargeVoltageMaterials scienceElectric fieldIonizationElectrostatic dischargePulsed powerElectric dischargePartial dischargeElectrical breakdownSpark gapElectrodeRise timeAnalytical Chemistry (journal)ChemistryBreakdown voltageOptoelectronicsOpticsLaserIonDielectricElectrical engineeringPhysics

Abstract

fetched live from OpenAlex

Electrical discharge in liquids is a research field that has great potential in environmental and technological applications. Depending on the experimental conditions (liquid nature, interelectrodes distance, applied voltage, pulse width, etc.), various discharge modes can be obtained. The involved physical processes have relatively fast spatiotemporal dynamics and, therefore, are not well understood. In this study, we report the electrical characterization, coupled with time-resolved light detection (using a photomultiplier, PM, tube), of positive and negative pulsed nanosecond spark discharges in de-ionized water using copper electrodes (distanced by ∼50 μm) in a pin-to-plate configuration. A detailed analysis of the current–voltage waveforms during the pre-breakdown and the breakdown phases is shown, and we found that the pre-breakdown phase depends on the high voltage magnitude only for positive polarity. On the other hand, the PM signals showed dependence on the voltage magnitude and on the pulse width, and various emission phases are observed. These phases can be related to the discharge power and/or to the discharge current. Filtered PM signals at various wavelengths are also acquired, and their temporal dynamics are discussed regarding the discharge conditions.

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 score0.023
Threshold uncertainty score0.249

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 head0.005
GPT teacher head0.198
Teacher spread0.193 · 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

Citations15
Published2020
Admission routes2
Has abstractyes

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