Mixing Evaluation of a Passive Scaled-Up Serpentine Micromixer With Slanted Grooves
Bibliographic record
Abstract
A novel, passive, scaled-up micromixer based on fluid rotation is proposed and evaluated experimentally and numerically over Reynolds numbers ranging from 0.5 to 100. Flow visualization is employed to qualitatively assess flow patterns, while induced fluorescence is used to quantify species distribution at five locations along the channel length. Two individual fluids are supplied to the test section via a Y-inlet. The fluid enters a meandering channel with four semicircular portions, each of which is lined with nine slanted grooves at the bottom surface. The main mixing channel is 3 mm wide and 0.75 mm deep, with a total length of 155.8 mm. Numerical simulations confirm rotation at all investigated Reynolds numbers, and the strength of rotation increases with increasing Reynolds number. Grooves are employed to promote helical flow, while the serpentine channel structure results in the formation of Dean vortices at Re ≥ 50 (Dean number ≥ 18.25), where momentum has a more significant effect. A decreasing-increasing trend in the degree of mixing was noted, with an inflection point at Re = 5, marking the transition from diffusion dominance to advection dominance. The increase in interfacial surface area is credited with the improved mixing in the advection-dominant regime, while high residence time allowed for significant mass diffusion in the diffusion-dominant regime. Good mixing was achieved at both high and low Reynolds numbers, with a maximum mixing index of 0.90 at Re = 100.
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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) | 0.000 | 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) | 0.001 | 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".