Quantitative characterization of micromixing simulation
Bibliographic record
Abstract
Micromixers with floor-grooved microfluidic channels have been successfully demonstrated in experiment. In this work, we numerically simulated the mixing within the devices and used the obtained concentration versus channel length profiles to quantitatively characterize the process. It was found that the concentration at any given cross-section location of the microfluidic channel periodically oscillates along the channel length, in coordination with the groove-caused helical flow during the mixing, and eventually converges to the neutral concentration value of two the mixing fluids. With these data, the specific channel length required for each helical flow to complete, the mixing efficiency of the devices, and the total channel length required to complete a mixing were easily defined and quantified, and were used to directly and comprehensively characterize the micromixing. This concentration versus channel length profile-based characterization method was also demonstrated in quantitatively analyzing the micromixing within a classic T mixer. It has clear advantages over the traditional concentration image-based characterization method that is only able to provide qualitative or semiquantitative information about a micromixing, and is expected to find an increasing use in studying mixing and optimizing device structure through numerical simulations.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".