Liquid–Liquid Encapsulation of Ferrofluid Using Magnetic Field
Bibliographic record
Abstract
Abstract Encapsulated magnetic microdroplets are of paramount importance in drug targeting and therapeutic applications. However, conventional techniques for generating encapsulated magnetic microdroplets suffer from several challenges, including lack of monodispersity, inflexibility in core–shell combinations, and complex device architecture to achieve encapsulation. Herein, a facile magnet‐assisted framework to controllably wrap ferrofluid (FF) droplets inside polydimethylsiloxane (PDMS) floating on a water bath is developed. A permanent magnet placed at the bottom of a static glass cuvette pulls the ferrofluid droplet across the PDMS–water interface, which results in the wrapping of the FF droplet by a thin PDMS layer. The deformation of the FF–PDMS interface and the encapsulation of FF inside PDMS thereof is attributed to the interplay of magnetic force and force due to PDMS–water interfacial tension. Based on the experimental observations, three regimes are identified, namely, stable encapsulation, unstable encapsulation, and no encapsulation, which depends on the magnetic Bond number (Bo m ) and the thickness of the PDMS layer (δ). The versatility of the technique is demonstrated further by showing stable wrapping of multiple ferrofluid droplets inside the same encapsulated cargo and successful underwater manipulation of the encapsulated droplets, which finds relevance in the encapsulation and magnet‐assisted actuation of novel encapsulated materials.
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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".