Modeling defect tolerance sensitivity to periodic post parameters in microfluidic channels
Bibliographic record
Abstract
Previously we modeled the theoretical benefits of using microfluidic channels that utilize a “Cathedral Chamber” design, in which the ceiling is supported by an array of periodic posts, compared to an array of parallel microfluidic channels. We developed a semi-automated technique that combines a rule-base defect placement system with a Monte Carlo method for modeling the fluid dynamics and blockage formation based on the likelihood of blockages forming in areas of high particle traffic and low flow rate. Earlier results indicate that Cathedral Chambers, that are supported by an array of 10 by 11 periodic posts with the same size as the spacing have six times higher lifetime expectancy compared to an array of 10 parallel channels, likely due to the provision of multiple paths during localized blockage formation in the Cathedral Chamber. In this paper, we have expanded our investigations by considering the defect tolerance sensitivity to scale by altering parameters such as the number and size of the posts and overall size of the chamber. For one set of simulations, we used the same number of posts in the chamber and the same starting position for the first 10 blockages as in our previous work. However, we shrank the size of the posts to 66% of their former size so that the new channels (flow pathways) are twice the size of the modified posts. In addition, we have also performed initial simulations based on wider microfluidic channels supported by an array of 20 by 11 periodic posts in order to explore their microfluidic behavior and lifetime.
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".