Creating Defect Tolerance in Microfluidic Capacitive/Photonic Biosensors
Bibliographic record
Abstract
Many biomedical sensors combine micro fluidic, electronic capacitive, and/or photonic capabilities. Micro fluidic sensors involve sealed channels through which the sample fluid containing biomedical materials flows with capacitive or photonic sensors detecting parameters contained in the liquid. However micro fluidic devices are prone to faults occurring when foreign particles in the bioliquid, or fluid bubbles, get lodged in the paths blocking a channel, thereby changing the fluidic flow in the device and affecting the parameters to be sensed. Thus, these systems require defect tolerant design in the micro fluidic and knowledge of how these changes will affect the parameters being sensed. To achieve fault tolerance we investigate a Cathedral Chamber design, with pillars supporting the roof at regular intervals. This prevents single blockages from stopping fluid flow through the system in a channel, as there are many paths. We discuss the potential causes and effects of such blockages. Monte Carlo analysis and simulations based on both randomly placed blockages and blockages occurring in low flow areas show that the Cathedral Chamber design significantly increases lifetime of the system, an average of 6 times more particles are required before full blockage occurs compared to an array of parallel channels. Fluid flow modeling shows parallel channels show rapid rise of pressure with the number of blockages while the Cathedral chamber shows much slower rise, which reaches a plateau pressure until it is blocked. The impact of these defects on the sensed parameters, such as capacitive measurement of the fluid or photonic measurements, is discussed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".