Upscaling production of droplets and magnetic particles with additive manufacturing
Bibliographic record
Abstract
Purpose Monodisperse microfluidic emulsions – droplets in another immiscible liquid – are beneficial to various technological applications in analytical chemistry, material and chemical engineering, biology and medicine. Upscaling the mass production of micron-sized monodisperse emulsions, however, has been a challenge because of the complexity and technical difficulty of fabricating or upscaling three-dimensional (3 D) microfluidic structures on a chip. Therefore, the authors develop a fluid dynamical design that uses a standard and straightforward 3 D printer for the mass production of monodisperse droplets. Design/methodology/approach The authors combine additive manufacturing, fluid dynamical design and suitable surface treatment to create an easy-to-fabricate device for the upscaling production of monodisperse emulsions. Considering hydrodynamic networks and associated flow resistance, the authors adapt microfluidic flow-focusing junctions to produce (water-in-oil) emulsions in parallel in one integrated fluidic device, under suitable flow rates and channel sizes. Findings The device consists of 32 droplet-makers in parallel and is capable of mass-producing 14 L/day of monodisperse emulsions. This convenient method can produce 50,000 millimetric droplets per hour. Finally, the authors extend the current 3 D printed fluidics with the generated emulsions to synthesize magnetic microspheres. Originality/value Combining additive manufacturing and hydrodynamical concepts and designs, the authors experimentally demonstrate a facile method of upscaling the production of useful monodisperse emulsions. The design and approach will be beneficial for mass productions of smart and functional microfluidic materials useful in a myriad of applications.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".