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Record W2298098565 · doi:10.1017/hpl.2016.7

Spectroscopic analysis of high electric field enhanced ionization in laser filaments in air for corona guiding

2016· article· en· W2298098565 on OpenAlexaff
Yingxia Wei, Yaoxiang Liu, Tie-Jun Wang, Na Chen, Jingjing Ju, Liu Y, Haiyi Sun, Cheng Wang, Jiansheng Liu, Haihe Lu, See Leang Chin, Ruxin Li

Bibliographic record

VenueHigh Power Laser Science and Engineering · 2016
Typearticle
Languageen
FieldPhysics and Astronomy
TopicLaser-Matter Interactions and Applications
Canadian institutionsUniversité LavalInstitut National d'Optique
FundersState Key Laboratory of High Field Laser PhysicsChinese Academy of SciencesNational Natural Science Foundation of China
KeywordsFilamentationElectric fieldCorona dischargeMaterials scienceIonizationLaserLaser-induced fluorescenceProtein filamentFemtosecondPlasmaAtomic physicsAnalytical Chemistry (journal)OpticsVoltageChemistryIonPhysics

Abstract

fetched live from OpenAlex

We report on a systematic experimental study on the fluorescence spectra produced from a femtosecond laser filament in air under a high electric field. The electric field alone was strong enough to create corona discharge (CD). Fluorescence spectra from neutral and ionic air molecules were measured and compared with pure high-voltage CD and pure laser filamentation (FIL). Among them, high electric field assisted laser FIL produced nitrogen fluorescence more efficiently than either pure CD or pure FIL processes. The nonlinear enhancement of fluorescence from the interaction of the laser filament and corona discharging electric field resulted in a more efficient ionization along the laser filament zone, which was confirmed by the spectroscopic measurement of both ionization-induced fluorescence and plasma-scattered 800 nm laser pulses. This is believed to be the key precursor process for filament-guided discharge.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.006
GPT teacher head0.236
Teacher spread0.230 · 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 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

Citations16
Published2016
Admission routes1
Has abstractyes

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