On-demand acoustic droplet splitting and steering in a disposable microfluidic chip
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.
Bibliographic record
Abstract
On-chip droplet splitting is one of the fundamental droplet-based microfluidic unit operations to control droplet volume after production and increase operational capability, flexibility, and throughput. Various droplet splitting methods have been proposed, and among them the acoustic droplet splitting method is promising because of its label-free operation without any physical or thermal damage to droplets. Previous acoustic droplet splitting methods faced several limitations: first, they employed a cross-type acoustofluidic device that precluded multichannel droplet splitting; second, they required irreversible bonding between a piezoelectric substrate and a microfluidic chip, such that the fluidic chip was not replaceable. Here, we present a parallel-type acoustofluidic device with a disposable microfluidic chip to address the limitations of previous acoustic droplet splitting devices. In the proposed device, an acoustic field is applied in the direction opposite to the flow direction to achieve multichannel droplet splitting and steering. A disposable polydimethylsiloxane microfluidic chip is employed in the developed device, thereby removing the need for permanent bonding and improving the flexibility of the droplet microfluidic device. We experimentally demonstrated on-demand acoustic droplet bi-splitting and steering with precise control over the droplet splitting ratio, and we investigated the underlying physical mechanisms of droplet splitting and steering based on Laplace pressure and ray acoustics analyses, respectively. We also demonstrated droplet tri-splitting to prove the feasibility of multichannel droplet splitting. The proposed on-demand acoustic droplet splitting device enables on-chip droplet volume control in various droplet-based microfluidic applications.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it