Monodisperse, Submicrometer Droplets via Condensation of Microfluidic‐Generated Gas Bubbles
Bibliographic record
Abstract
Microfluidics (MFs) can produce monodisperse droplets with precise size control. However, the synthesis of monodisperse droplets much smaller than the minimum feature size of the microfluidic device (MFD) remains challenging, thus limiting the production of submicrometer droplets. To overcome the minimum micrometer-scale droplet sizes that can be generated using typical MFDs, the droplet material is heated above its boiling point (bp), and then MFs is used to produce monodisperse micrometer-scale bubbles (MBs) that are easily formed in the size regime where standard MFDs have excellent size control. After MBs are formed, they are cooled, condensing into dramatically smaller droplets that are beyond the size limit achievable using the original MFD, with a size decrease corresponding to the density difference between the gas and liquid phases of the droplet material. Herein, it is shown experimentally that monodisperse, submicrometer droplets of predictable sizes can be condensed from a monodisperse population of MBs as generated by MFs. Using perfluoropentane (PFP) as a representative solvent due to its low bp (29.2 °C), it is demonstrated that monodisperse PFP MBs can be produced at MFD temperatures >3.6 °C above the bp of PFP over a wide range of sizes (i.e., diameters from 2 to 200 μm). Independent of initial size, the generated MBs shrink rapidly in size from about 3 to 0 °C above the bp of PFP, corresponding to a phase change from gas to liquid, after which they shrink more slowly to form fully condensed droplets with diameters 5.0 ± 0.1 times smaller than the initial size of the MBs, even in the submicrometer size regime. This new method is versatile and flexible, and may be applied to any type of low-bp solvent for the manufacture of different submicrometer droplets for which precisely controlled dimensions are required.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".