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Record W4366605627 · doi:10.1088/2053-1591/accf62

Acoustic wave-driven oxide dependant dynamic behavior of liquid metal droplet for inkjet applications

2023· article· en· W4366605627 on OpenAlex
Jinpyo Jeon, Jeong‐Bong Lee, Sang Kug Chung, Daeyoung Kim

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueMaterials Research Express · 2023
Typearticle
Languageen
FieldEngineering
TopicMicrofluidic and Bio-sensing Technologies
Canadian institutionsUniversity of British Columbia
FundersMyongji University
KeywordsLiquid metalAmplitudeMaterials scienceAcoustic waveMetalSurface acoustic waveRayleigh scatteringAcousticsOxideLiquid mediumMechanicsComposite materialOpticsChemistryPhysicsChromatographyMetallurgy

Abstract

fetched live from OpenAlex

Abstract In this paper, we report bouncing and separating dynamic behaviors of a liquid metal droplet with/without the oxide layer in response to the applied acoustic wave. The oxidized liquid metal droplet is readily bounced off from the surface when it is excited by acoustic wave, while the HCl treated liquid metal droplet is fragmented into several small droplets. The bouncing height of the oxidized liquid metal is proportional to the applied acoustic wave amplitude. The number of the fragmented liquid metal droplets for the HCl-treated liquid metal according to time and acoustic wave amplitude was investigated. We also demonstrated the acoustic wave-based inkjet application to generate liquid metal droplets based on the pinch-off and the Rayleigh instability by changing amplitude of the acoustic wave. The probability for the generation of various droplet sizes with different acoustic wave amplitude was also studied.

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.

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 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 score0.009
Threshold uncertainty score0.558

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)0.0000.000

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 head0.054
GPT teacher head0.328
Teacher spread0.274 · 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