Micro/Nanospheres Generation by Fluid-Fluid Interaction Technology: A Literature Review
Bibliographic record
Abstract
This review focuses on the fundamental fluid mechanics which governs the generation of micro/nanospheres. The micro/nanosphere generation process has gathered significant attention in the past two decades, since micro/nanospheres are widely used in drug delivery, food science, cosmetics, and other application areas. Many methods have been developed based on different operating principles, such as microfluidic methods, electrospray methods, chemical methods, and so forth. This paper focuses on microfluidic methods. Although the structure of the microfluidic devices may be different, the operating principles behind them are often very similar. Following an initial discussion of the fluid mechanics related to the generation of microspheres, various design approaches are discussed, including T-junction, flow focusing, membrane emulsification, modified T-junction, and double emulsification methods. The advantages and problems associated with each method are also discussed. Next, the most commonly used computational fluid dynamics (CFD) methods are reviewed at three different levels: microscopic, mesoscopic, and macroscopic. Finally, the issues identified in the current literature are discussed, and some suggestions are offered regarding the future direction of technology development related to micro/nanosphere generation. Few relevant patents to the topic have been reviewed and cited.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".