Acoustothermal tweezer for droplet sorting in a disposable microfluidic chip
Bibliographic record
Abstract
Precise control over droplet position within a microchannel is fundamental to droplet microfluidic applications. This article proposes acoustothermal tweezer for the control of droplet position, which is based on thermocapillary droplet migration actuated by acoustothermal heating. The proposed system comprises an acoustothermal heater, which is composed of a slanted finger interdigital transducer patterned on a piezoelectric substrate and a thin PDMS membrane, and a PDMS microchannel. In the proposed system, droplets moving in a droplet microfluidic chip experience spatiotemporally varying thermal stimuli produced by acoustothermal heating and thus migrate laterally. In comparison to previous methods for droplet sorting, the acoustothermal tweezer offers significant advantages: first, the droplet position can be manipulated in two opposite directions, which enables bidirectional droplet sorting to one of three outlets downstream; second, precise control over the droplet position as well as improved droplet lateral displacement on the order of hundreds of micrometers can be achieved in a deterministic manner, thereby enabling multichannel droplet sorting; third, the PDMS microfluidic chip is disposable and thus can be easily replaced since it is attached to the substrate by reversible bonding, which allows the acoustothermal heater to be reused. Given these advantages, the proposed droplet sorting system is a promising droplet microfluidic lab-on-a-chip platform for tunable, on-demand droplet position control.
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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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".