Modeling the interactions between compliant microcapsules and pillars in microchannels
Bibliographic record
Abstract
Using a computational model, we investigate the motion of microcapsules inside a microchannel that encompasses a narrow constriction. The microcapsules are composed of a compliant, elastic shell and an encapsulated fluid; these fluid-filled shells model synthetic polymeric microcapsules or biological cells (e.g., leukocytes). Driven by an imposed flow, the capsules are propelled along the microchannel and through the constricted region, which is formed by two pillars that lie in registry, extending from the top and bottom walls of the channels. The tops of these pillars (facing into the microchannel) are modified to exhibit either a neutral or an attractive interaction with the microcapsules. The pillars (and constriction) model topological features that can be introduced into microfluidic devices or the physical and chemical heterogeneities that are inherently present in biological vessels. To simulate the behavior of this complex system, we employ a hybrid method that integrates the lattice Boltzmann model (LBM) for fluid dynamics and the lattice spring model (LSM) for the micromechanics of elastic solids. Through this LBM/LSM technique, we probe how the capsule's stiffness and interaction with the pillars affect its passage through the chambers. The results yield guidelines for regulating the movement of microcarriers in microfluidic systems and provide insight into the flow properties of biological cells in capillaries.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| 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".