A Fluidic Interface with High Flow Uniformity for Reusable Large Area Resonant Biosensors
Bibliographic record
Abstract
Resonant biosensors are known for their high accuracy and high level of miniaturization. However, their fabrication costs prevent them from being used as disposable sensors and their effective commercial success will depend on their ability to be reused repeatedly. Accordingly, all the parts of the sensor in contact with the fluid need to tolerate the regenerative process which uses different chemicals (H₃PO₄, H₂SO₄ based baths) without degrading the characteristics of the sensor. In this paper, we propose a fluidic interface that can meet these requirements, and control the liquid flow uniformity at the surface of the vibrating area. We study different inlet and outlet channel configurations, estimating their performance using numerical simulations based on finite element method (FEM). The interfaces were fabricated using wet chemical etching on Si, which has all the desirable characteristics for a reusable biosensor circuit. Using a glass cover, we could observe the circulation of liquid near the active surface, and by using micro-particle image velocimetry (μPIV) on large surface area we could verify experimentally the effectiveness of the different designs and compare with simulation results.
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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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 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".