Deriving a blood-mimicking fluid for particle image velocimetry in sylgard-184 vascular models
Bibliographic record
Abstract
A new blood-mimicking fluid (BMF) has been developed for particle image velocimetry (PIV), which enables flow studies in vascular models (phantoms). A major difficulty in PIV that affects measurement accuracy is the refraction and distortion of light passing through the interface between the model and the fluid, due to the difference in refractive index (n) between the two materials. The problem can be eliminated by using a fluid with a refractive index matching that of the model. Such fluids are not commonly available, especially for vascular research where the fluid should also have a viscosity similar to human blood. In this work, a blood-mimicking fluid, composed of water (47.38% by weight), glycerol (36.94% by weight) and sodium iodide salt (15.68% by weight), was developed for compatibility with our silicone (Sylgard 184; n = 1.414) phantoms. The fluid exhibits a dynamic viscosity of 4.31+/-0.03 cP which lies within the range of human blood viscosity (4.4+/-0.6 cP). Both refractive index and viscosity were attained at 22.2+/-0.2 degrees C, which is a feasible room temperature, thus eliminating the need for a temperature-control system. The fluid will be used to study hemodynamics in vascular flow models fabricated from Sylgard 184.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".