Droplet-based microfluidics at the femtolitre scale
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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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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".