Physics and dynamics of particle migration in zigzag inertial microchannels
Bibliographic record
Abstract
Effective manipulation and separation of particles in inertial microfluidics is essential for a broad range of biomedical, clinical, and industrial applications. Among serpentine geometries, zigzag microchannels offer unique advantages, including high separation efficiency and purity, compact design, scalability, portability, and improved resolution for focusing small particles. However, the underlying physics governing particle migration in these structures remains poorly understood. This study aims to elucidate the mechanisms of particle migration in zigzag inertial microchannels by examining how Reynolds number and channel geometry, specifically height and width, affect the interplay between inertial lift and lateral drag forces. A combined numerical and experimental approach was employed to analyze particle migration under different flow and geometric conditions. Results show that reducing channel height enhances shear-gradient lift forces, enabling completion of the first migration stage, while the balance between rotation-induced lift and lateral drag determines the final focusing pattern. Increasing channel width, in contrast, causes only minor changes in hydraulic diameter in high aspect ratio channels (H ≪ W) and leaves the shear rate nearly constant across most of the cross section, resulting in weaker effects on particle focusing. At Re = 50, 3 μm particles achieve side focusing only at smaller heights, whereas 15 μm particles transition from double-stream to single-stream focusing as the force balance shifts. These findings establish a mechanistic framework for predicting particle migration and optimizing geometry for precise alignment. Such insights are directly relevant to applications such as flow cytometry and rare-cell analysis, where accurate focusing improves detection performance and efficiency.
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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 | 0.000 |
| 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 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".