Elasto-inertial microparticle focusing in straight microchannels: A numerical parametric investigation
Bibliographic record
Abstract
Elasto-inertial microfluidic particle separation has attracted attention in biotechnological applications due to its passive nature and enhanced versatility compared to inertial systems. Developing a robust elasto-inertial sorting device can be facilitated with numerical simulation. In this study, a numerical parametric investigation was undertaken to study elasto-inertial focusing of microparticles in a straight microchannel. Our goal was to develop an approach that could be both accurate and easily implementable on the commercial solvers. We simulated the flow field using the Carreau model. The resulting elastic lift force was implemented based on an approximation of the Oldroyd-B model. Results were verified and validated against experimental measurements by us and others. A parametric study was conducted to investigate elasto-inertial particle focusing considering the important non-dimensional numbers such as the Reynolds number (Re), the Deborah number (De), dimensionless channel length (L), and blockage ratio (β). Based on this investigation, the commonly used design threshold, that is, De·L·β2=1, for particle focusing was modified and a new threshold was proposed De·Re0.2·L·β2=5. This reduced particle dispersion throughout the width of the channel from ∼20% to ∼3%. Based on this analysis and the new thresholding scheme, an empirical non-dimensional correlation was developed to predict elasto-inertial particle dispersion in straight square cross-sectional microchannels. Using this new correlation, variation in predicted dispersion was reduced from ∼15% to less than ∼5%. Our model can be used to optimize the design of elasto-inertial microfluidic particle sorters to improve experimental outcomes.
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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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".