Droplet-based microfluidics at the femtolitre scale
Why this work is in the frame
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Bibliographic record
Abstract
We have built a toolbox of modules for droplet-based microfluidic operations on femtolitre volume droplets. We have demonstrated monodisperse production, sorting, coalescence, splitting, mixing, off-chip incubation and re-injection at high frequencies (up to 3 kHz). We describe the constraints and limitations under which satisfactory performances are obtained, and discuss the physics that controls each operation. For some operations, such as internal mixing, we obtained outstanding performances: for instance, in 75 fL droplets the mixing time was 45 μs, 35-fold faster than previously reported for a droplet microreactor. In practice, in all cases, a level of control comparable to nanolitre or picolitre droplet manipulation was obtained despite the 3 to 6 order of magnitude reduction in droplet volume. Remarkably, all the operations were performed using devices made using standard soft-lithography techniques and PDMS rapid prototyping. We show that femtolitre droplets can be used as microreactors for molecular biology with volumes one billion times smaller than conventional microtitre plate wells: in particular, the Polymerase Chain Reaction (PCR) was shown to work efficiently in 20 fL droplets.
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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