Enhancing defect tolerance in periodic post microfluidic channels
Bibliographic record
Abstract
Biomedical sensors using microfluidic channels are prone to blockage due to particles and bubbles in the fluid. Wider channels may be used, but wide polymer channels may suffer from structural instability (e.g., sagging channel covers). A common design uses many parallel flow channels separated by structural support walls, but these can be rapidly blocked by particulates. We have been studying an alternative “Cathedral Chamber” design where the channel “roof” (cover) is support by periodic posts which creates many possible flow paths to bypass blockages. We use Monte Carlo modelling with iterative COMSOL fluid dynamics simulations to establish the stream lines, and particle velocities. Then a rules based methodology iteratively places trapped particles based on the fluid paths created by the existing blockages, until the system become fully blocked. Previous work has shown that the periodic post design increases lifetime by allowing 6 to 7 times more blockages than can a parallel channel design. In this paper, we simulate and analyze why expanding the number of channels increases almost linearly the number of particles required for blockages. Lifetime increase is still 4.5-5.5 times even for the limiting case of a 2 channel cathedral chamber. This shows the sideways flow created by the periodic posts creates many advantages for the microfluidic chambers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".