Integrated Single Camera µPTV And Florescence Imaging For Cell Tracking And Flow Investigation In Centrifugal Microfluidic Devices
Bibliographic record
Abstract
A micro-hydrocyclone is investigated as a high throughput particle/cell sorting microfluidic device. A complex flow structure has been reported by limited numerical works that increases the importance of undertaking a comprehensive experimental investigation. In addition to the flow, utilizing damageable bio cells in a micro-hydrocyclone, requires a deep understanding of the interaction between the cells entering the device, the flow structure and its instabilities forming in different operational phases. In such cases the implementation of multiple measurement techniques can be challenging due to the small scale of devices, here < 5 mm diameter. Therefore, for this complex flow an experimental approach is introduced to capture the flow structure and the motion of cells. In this method, tracer particles and fluorescent stained cells are captured simultaneous by a single camera in the same flow field. A custom image processing scheme is used for partitioning cells and particles from the raw data. To determine velocity vectors, µPTV is employed on the segmented data sets to study the effect of flow on trajectory and velocity of each individual cell in the system. By a combined investigation in a vast range of Reynolds number (50
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".