Tracking of Capillary Interface in Microfluidic Channels
Bibliographic record
Abstract
Capillarity is commonly used for fluid transport in microfluidic devices. The capillary flow can be divided into three different flow regimes: entry regime, Poiseuille regime, and surface tension regime as shown in Fig. 1[1]. Generally, it is anticipated that at the entrance of any narrow confinement, the flow goes through entrance flow regime. For capillary flow, this entrance regime has generally been neglected in the literature. Beyond this entrance regime, the flow attains the fully developed velocity profile across the channel, which is termed as a Poiseuille flow. Moreover, in the capillary flow, the interface is always under traction — due to the capillary forces and hence, a third flow regime needs to be considered behind the interface which is referred as the surface tension regime. These regimes are yet to be experimentally explored and analyzed. An “in-house” developed μ-PIV system is used to quantify the flow field at the liquid/air interface (surface tension regime) in a rectangular glass microchannel of dimension 1.5 mm (width) × 500 μm (depth). The magnitude of velocity and the flow front evolution along the microchannel is calculated utilizing commercially available image processing software. Figure 2 shows the μ-PIV experimental setup used here. The main components of the experimental setup include an imaging device, magnification optics, and a continuous laser source (473 nm) in back illumination mode. The fluorescent particle of 1.9 m in diameter with DI water is used as a working fluid. The concentration of the microparticles is very less which is approximately 1%, therefore the effect of microbead concentration on the wetting properties is considered to be negligible for the present study. Moreover, it is assumed that the surface properties of the particles also do not affect the fluid flow. The capillary flow interface is captured and the corresponding processed images are presented to depict the velocity field at the liquid/air interface. The enlarged view of the microchannel cross section is shown in Fig. 2. The section A-A is the location at which the images are captured for the analysis.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".