Numerical simulation of microfluidic injection processes in crossing microchannels
Bibliographic record
Abstract
The design and operation control of microfluidic devices have drawn a great deal of attention over the last decade due to the emerging lab-on-a-chip applications. Cross-shaped microchannels connecting liquid reservoirs are typical configurations of the microfluidic chips. Normally the microchannels have a large length-to-width aspect ratio (typically 1500:1), therefore, the transport phenomena in these microchannels are essentially multiscale and multidimensional problems. There are no analytical solutions existing for such kind of problems and it has been found that effectively and efficiently simulating the transport phenomena in such microchannels is very difficult. A numerical model developed here uses the designed boundaries to truncate the physical domain to a small computation domain in order to concentrate computing power in the areas exhibiting multidimensional phenomena (such as intersections) and apply analytical functions in the areas of one-dimensionality (such as fully developed flow region). This model is employed to simulate the flow and mass transport processes in a planar glass chip with a cross-shaped microchannel, and the model predictions are compared to the experimental results. Agreement between the model predictions and experimental results verified that this newly developed model is capable of accurately and efficiently simulating the transport phenomena in microfluidic devices.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".