A Novel Scanning Molecular Tagging Velocimetry Technique for Two Dimensional Microfluidic Applications
Bibliographic record
Abstract
Micro particle image velocimetry forms the basis of modern microscale flow measurement techniques. However, the motion of tracking particles that have been introduced into the fluid to measure flow velocity in micro fluidic flows can be affected by a number of different body forces that may not affect the carrier fluid. Under these conditions, molecular tagging velocity (MTV) has a distinct advantage in resolving fluid motion. A MTV approach, based on the photobleaching mechanism, is presented that allows any desired pattern of flow markers to be written into the region-of-interest to track the flow. This allows standard particle master shadowgraphy algorithms to be used to track groups of tagged molecules in the flow resolving two components of the bulk fluid motion. The MTV techniques developed so far for microfluidic application provide only a one dimensional flow measurement, typically along the axial direction perpendicular to the tagged region. Other tagging methods include the use of a grid and a structured mask for macro-scale and micro-scale flows respectively. One of the limitations of these approaches is that velocity information for only a predefined pattern is available. The MTV technique presented here is capable of resolving two dimensional flow velocity information with the limitations associated with the application of a mask. The salient feature of the presented approach is the use of a laser scanner which allows unrestricted, repeatable and accurate movement of the write laser within the field-of-view. The temporal displacement of the tagged region is captured onto an imaging device. The proof of concept of the technique is presented in the current work. A known flow velocity through a curved section of a 750 μm wide serpentine channel is measured using the designed system. The tagged regions are in the form of dark dots which advect with the flow. The temporal and spatial evolution of these tagged regions is tracked to reveal the flow information. The aim of this technique is to perform velocity measurements in a dielectrophoretic flow of a mixture of nanoparticles and the caged fluorescent dye, which may show different flow behavior, possibly in two opposite directions due to the charge on them.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".