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 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".