Simulation and experimental determination of the online separation of blood components with the help of microfluidic cascading spirals
Bibliographic record
Abstract
Microfluidic spirals were used to successfully separate rare solid components from unpretreated human whole blood samples. The measured separation ratio of the spirals is the factor by which the concentration of the rare component is increased due to the Dean effect present in a flow profile in a curved duct. Different rates of dilution of the blood samples with a phosphate-buffered solution were investigated. The diameters of the spherical particles to separate ranged from 2 μm to 18 μm. It was found that diluting the blood to 20% is optimal leading to a separation ratio up to 1.97. Using two spirals continuously placed in a row led to an increase in separation efficacy in samples consisting of phosphate-buffered solution only from 1.86 to 3.79. Numerical investigations were carried out to display the flow profiles of Newtonian water samples and the shear-thinning blood samples in the cross-section of the experimentally handled channels. A macroscopic difference in velocity between the two rheologically different fluids could not be found. The macroscopic Dean flow is equally present and useful to help particles migrate to certain equilibrium positions in blood as well as lower viscous Newtonian fluids. The investigations highlight the potential for using highly concentrated, very heterogeneous, and non-Newtonian fluidic systems in known microsystems for screening applications.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".