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Record W2785080287 · doi:10.1038/s41598-018-31993-3

Laser guided ionic wind

2018· article· en· W2785080287 on OpenAlexafffund
Shengzhe Du, Tiejun Wang, Zhongbin Zhu, Yaoxiang Liu, Na Chen, Jianhao Zhang, Hao Guo, Haiyi Sun, Jingjing Ju, Cheng Wang, Jiansheng Liu, See Leang Chin, Ruxin Li, Zhizhan Xu

Bibliographic record

VenueScientific Reports · 2018
Typearticle
Languageen
FieldEngineering
TopicLaser-induced spectroscopy and plasma
Canadian institutionsUniversité LavalInstitut National d'Optique
FundersChinese Academy of SciencesUniversité Laval
KeywordsIon windElectric fieldLaserFemtosecondIonic bondingPlasmaPlasma channelAtomic physicsLidarVoltagePhysicsCoupling (piping)MechanicsComputational physicsIonMaterials scienceEnvironmental scienceOpticsComposite material

Abstract

fetched live from OpenAlex

We report on a method to experimentally generate ionic wind by coupling an external large electric field with an intense femtosecond laser induced air plasma channel. The measured ionic wind velocity could be as strong as >4 m/s. It could be optimized by increasing the strength of the applied electric field and the volume of the laser induced plasma channel. The experimental observation was qualitatively confirmed by a numerical simulation of spatial distribution of the electric field. The ionic wind can be generated outside a high-voltage geometry, even at remote distances.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.391
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0010.001

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 head0.014
GPT teacher head0.241
Teacher spread0.227 · 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; both teacher heads agree on what is shown here.

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
Published2018
Admission routes2
Has abstractyes

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