Experimental and Numerical Investigation of Mass Transfer in Passive Scaled-up Micromixers
Bibliographic record
Abstract
Micromixers are vital components in micro-total analysis systems (μ-TAS) and Lab-on-Chip (LOC) devices, with applications in drug delivery, medical diagnostics, and chemical analyses, amongst others. Traditional macroscale mixing techniques may not be applied at the microscale, where viscous forces become important compared to inertial forces. As such, it remains a challenge to effectively and thoroughly mix liquid species in small characteristic dimensions. \n\tThe present work aims to analyze flow phenomena and mass transfer in three novel scaled-up micromixers, which make use of variations in channel geometry to induce mixing. Designs based on multi-lamination inlets, obstruction filled channels, Dean vortex inducing curved channels, and helical flow inducing grooves are investigated. Flow visualization is used as a qualitative tool, providing valuable information regarding flow patterns and mixing. Induced fluorescence is applied to assess whole field concentration distribution, and provide quantitative species distribution data. Complex three dimensional flows are analyzed using numerical simulations, which show good agreement with experimental work. \n \tThe mixers are evaluated over Reynolds numbers ranging from 0.5 to 100, corresponding to Péclet numbers ranging from 1.25 × 103 to 1.25 × 105. Results show a decreasing-increasing trend in the degree of mixing with increasing Reynolds number, as the dominant mixing mechanism changes from mass diffusion to mass advection. Up to 90% mixing is reported. To allow for reasonable mixing performance comparison with published work, an equivalent length parameter is proposed. The present devices offer good mixing in shorter lengths over a wide range of Reynolds numbers compared to numerous published devices.
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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) | 0.000 | 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".