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Record W4407168753 · doi:10.1126/sciadv.adp0686

Electric plasma guided with ultrasonic fields

2025· article· en· W4407168753 on OpenAlexaff
Josu Irisarri, Iñigo Ezcurdia, Naroa Iriarte, Marika Sirkka, Dmitry Nikolaev, J. Mäkinen, Alexander Martinez-Marchese, Denys Iablonskyi, Ari Salmi, Asier Marzo

Bibliographic record

VenueScience Advances · 2025
Typearticle
Languageen
FieldEngineering
TopicElectrohydrodynamics and Fluid Dynamics
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsPlasmaUltrasonic sensorVoltageHigh voltageSPARK (programming language)Electric fieldAcousticsComputer scienceMaterials scienceElectrical engineeringOptoelectronicsPhysicsEngineering

Abstract

fetched live from OpenAlex

Electric plasma forms sparks in midair that transfer electrical current. This current can power high-voltage electronics, kill bacteria, produce tactile sensations, or be used for welding. However, the formation of the spark is chaotic and hard to control. Laser pulses can guide discharges but require high power and are disruptive and cumbersome to control. Here, we show that ultrasonic fields can guide plasma sparks, even around obstacles. The ultrasonic beams can be directed dynamically and within milliseconds, enabling precise, nondangerous, and fast control of high-voltage sparks. This phenomenon can be used for applications in high-voltage switching and plasma treatments.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.403
Threshold uncertainty score0.328

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
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 head0.003
GPT teacher head0.221
Teacher spread0.218 · 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 designSimulation or modeling
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

Citations3
Published2025
Admission routes1
Has abstractyes

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