Tuning Composition of Multicomponent Surface Nanodroplets in a Continuous Flow‐In System
Bibliographic record
Abstract
Abstract Droplets are excellent platforms for compartmentalization of many processes such as chemical reactions, liquid–liquid extraction, and biological or chemical analyses. Accurately controlling and optimizing the composition of these droplets is of high importance to maximize their functionality. In this work, the formation of multicomponent droplets with controllable composition by employing a continuous flow‐in setup is demonstrated. Multiple streams of different oil solutions are introduced and mixed in a passive flow mixer and the outcoming mixture is subsequently fed into a flow chamber to form surface nanodroplets by solvent exchange. This method is time‐effective, enabling programmable continuous processes for droplet formation and surface cleaning. The surface nanodroplets are formed within 2.5 min in one cycle, and the droplet formation is reliable with similar size distribution over multiple cycles. The composition of the resulting surface nanodroplet can be tuned at will simply by controlling the flow rate ratios of each stream of the oil solution. Using fluorescence imaging, it is shown that the composition of the binary surface nanodroplets agrees well with theoretical values predicted using the phase diagram.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".