Realization of a stable, monodisperse water-in-oil droplet system with micro-scale and nano-scale confinement for tandem microscopy and diffusion NMR studies
Bibliographic record
Abstract
In this work we generate stable and monodisperse water-in-oil emulsions using a co-flowing geometry that produced droplet sizes between 13 μm and 250 μm. The drops survived transfer to NMR tubes and were stable for at least 26 hours, enabling the performance of pulsed-field-gradient NMR experiments in addition to microscopy. The drops sizes achieved as a function of flow rate agree well with a simple model for droplet generation: this yields a precise measure of the interfacial tension. The design of a cell mimetic environment with nano-scale confinement has also been demonstrated with diffusion measurements on macromolecules (PEG and Ficoll70) within droplets that are further structured internally using agarose gel networks. Containing the agarose gel in droplets appears to provide very reproducible and homogeneous network environments, enabling quantitative agreement of Ficoll70 dynamics with a theoretical model, with no fit parameters, and, with PEG, yielding a systematic polymer-size dependent slowing down in the network. This is in contrast with bulk agarose, where identical macromolecular diffusion measurements indicate the presence of heterogeneities with water pockets.
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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 | 0.000 | 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".