Microfluidic production of multiple emulsions and functional microcapsules
Bibliographic record
Abstract
Recent advances in microfluidics have enabled the controlled production of multiple-emulsion drops with onion-like topology. The multiple-emulsion drops possess an intrinsic core-shell geometry, which makes them useful as templates to create microcapsules with a solid membrane. High flexibility in the selection of materials and hierarchical order, achieved by microfluidic technologies, has provided versatility in the membrane properties and microcapsule functions. The microcapsules are now designed not just for storage and release of encapsulants but for sensing microenvironments, developing structural colours, and many other uses. This article reviews the current state of the art in the microfluidic-based production of multiple-emulsion drops and functional microcapsules. The three main sections of this paper discuss distinct microfluidic techniques developed for the generation of multiple emulsions, four representative methods used for solid membrane formation, and various applications of functional microcapsules. Finally, we outline the current limitations and future perspectives of microfluidics and microcapsules.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".