Onset of particle trapping and release via acoustic bubbles
Bibliographic record
Abstract
Trapping and sorting of micro-sized objects is one important application of lab on a chip devices, with the use of acoustic bubbles emerging as an effective, non-contact method. Acoustically actuated bubbles are known to exert a secondary radiation force (FSR) on micro-particles and stabilize them on the bubble surface, when this radiation force exceeds the external hydrodynamic forces that act to keep the particles in motion. While the theoretical expression of FSR has been derived by Nyborg decades ago, no direct experimental validation of this force has been performed, and the relationship between FSR and the bubble's ability to trap particles in a given lab on a chip device remains largely empirical. In order to quantify the connection between the bubble oscillation and the resultant FSR, we experimentally measure the amplitude of bubble oscillations that give rise to FSR and observe the trapping and release of a single microsphere in the presence of the mean flow at the corresponding acoustic parameters using an acoustofluidic device. By combining well-developed theories that connect bubble oscillations to the acoustic actuation, we derive the expression for the critical input voltage that leads to particle release into the flow, in good agreement with the experiments.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".