Magnetic water-in-water droplet microfluidics: Systematic experiments and scaling mathematical analysis
Bibliographic record
Abstract
A major barrier to the clinical utilization of microfluidically generated water-in-oil droplets is the cumbersome washing steps required to remove the non-biocompatible organic oil phase from the droplets. In this paper, we report an on-chip magnetic water-in-water droplet generation and manipulation platform using a biocompatible aqueous two-phase system of a polyethylene glycol-polypropylene glycol-polyethylene glycol triblock copolymer (PEG-PPG-PEG) and dextran (DEX), eliminating the need for subsequent washing steps. By careful selection of a ferrofluid that shows an affinity toward the DEX phase (the dispersed phase in our microfluidic device), we generate magnetic DEX droplets in a non-magnetic continuous phase of PEG-PPG-PEG. We apply an external magnetic field to manipulate the droplets and sort them into different outlets. We also perform scaling analysis to model the droplet deflection and find that the experimental data show good agreement with the model. We expect that this type of all-biocompatible magnetic droplet microfluidic system will find utility in biomedical applications, such as long-term single cell analysis. In addition, the model can be used for designing experimental parameters to achieve a desired droplet trajectory.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".