Upscaling production of droplets and magnetic particles with additive manufacturing
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it