An integrated droplet-digital microfluidic system for on-demand droplet creation, mixing, incubation, and sorting
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
Droplet microfluidics is a technique that has the ability to compartmentalize reactions in sub nano- (or pico-) liter volumes that can potentially enable millions of distinct biological assays to be performed on individual cells. In a typical droplet microfluidic system, droplets are manipulated by pressure-based flows. This has limited the fluidic operations that can be performed in these devices. Digital microfluidics is an alternative microfluidic paradigm with precise control and manipulation over individual droplets. Here, we implement an integrated droplet-digital microfluidic (which we call 'ID2M') system in which common fluidic operations (i.e. droplet generation, cell encapsulation, droplet merging and mixing, droplet trapping and incubation, and droplet sorting) can be performed. With the addition of electrodes, we have been able to create droplets on-demand, tune their volumes on-demand, and merge and mix several droplets to produce a dilution series. Moreover, this device can trap and incubate droplets for 24 h that can consequently be sorted and analyzed in multiple n-ary channels (as opposed to typical binary channels). The ID2M platform has been validated as a robust on-demand screening system by sorting fluorescein droplets of different concentration with an efficiency of ∼96%. The utility of the new system is further demonstrated by culturing and sorting tolerant yeast mutants and wild-type yeast cells in ionic liquid based on their growth profiles. This new platform for both droplet and digital microfluidics has the potential to be used for screening different conditions on-chip and for applications like directed evolution.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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