Simultaneous circulating tumor cells (CTCs) tracking and flow field characterization through integrated single camera imaging in a micro-hydrocyclone
Bibliographic record
Abstract
High-throughput devices in biomedical engineering are the center interests due to the increasing demand of applications. Micro-hydrocyclones are centrifugal microfluidic devices with growing applications in bio-separation industry e.g. separation of suspended particles and biological cells. The internal flow physics of micro-hydrocyclones remains uncharacterized, especially in the presence of suspended biological particles such as circulating tumor cells (CTCs). To address this gap, this work is focused on developing an integrated optical measurement system for simultaneous flow and particle tracking measurements inside a micro-hydrocyclone separating CTCs. Two experimental conditions were investigated: first, a single-phase flow measurement where the internal velocity field was quantified using particle image velocimetry; and second, a two-phase flow condition where CTCs were introduced into the fluid at a ξ = 10 2 cells/ml, allowing simultaneous measurement of the flow field and individual trajectories of the CTCs. The results reveal that the presence of CTCs has a negligible effect on the global flow field, as the measured velocity fields for single-phase and two-phase conditions were nearly identical across the investigated Reynolds numbers i.e., Re = [150,300,700]. This indicates that single-phase flow studies can capture the physics of micro-hydrocyclones even in the presence of sparse biological particles. However, the dynamics of the CTCs themselves were found to deviate from the bulk flow field, with CTCs exhibiting lower momentum and lagging behind the flow due to their relatively large size compared to the device geometry. This is the first experimental study of its kind to directly measure and report the internal flow field of a micro-hydrocyclone, evaluating it under both single and two-phase conditions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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 teacher head, 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".