Microfluidic magnetic self-assembly at liquid–liquid interfaces
Bibliographic record
Abstract
We present a microfluidic method that controllably self-assembles microparticles into clusters at an aqueous two-phase liquid-liquid interface. The liquid-liquid interface is formed between converging flows of aqueous dextran and polyethylene glycol, in a microfluidic cross-slot device. We control the size of the self-assembled particle clusters as they pass through the liquid-liquid interface, by systematically varying the applied magnetic field gradient, and the interfacial tension of the liquid-liquid interface. We find that upon penetration through the interface, the number of particles within a cluster increases with increasing interfacial tension, and decreasing magnetic field gradient. We also develop a scaling model of the number of particles within a cluster, and observe an inverse scaling of the number of particles within a cluster with the dimensionless magnetic Bond number. Upon cluster penetration across the liquid-liquid interface, we find magnetic Bond number regimes where the fluid coating drains away from the surface of the cluster, and where the clusters are encapsulated inside a thin film coating layer. This self-assembly technique may find application in controlling the size of microscale self-assemblies, and coating such assemblies; for example, in clustering and coating of cells for immunoisolated cell transplants.
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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) | 0.006 | 0.015 |
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; both teacher heads agree on what is shown here.
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".