Wall Shear Stress Sensors Based on Carbon Nanotube Pillars
Bibliographic record
Abstract
Increasingly smaller fluid flow devices depend on increasingly smaller sensors to operate. In this work, we develop a miniature wall shear stress sensor consisting of a pair of carbon nanotube pillars that produce a measurable capacitance change when one is deflected by the flow. The sensor, including material producing the capacitance change, has a 50 μm × 60 μm footprint and <200 μm height. It provides 0.05–1 fF/Pa sensitivity with up to ±8 Pa range. Most sensors produced were found to have a wider operating range when the thinner pillar was positioned downstream because greater deflection could be achieved without contact between the pillars. Interestingly, a small number of sensors responded differently to flow due to twisting of the sensing element rather than bending or due to a dominant Bernoulli effect. These diverse sensing mechanisms could be exploited to tune sensitivity or operating range in future designs.
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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) | 0.001 | 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".