Elaboration of Robust Integrated Thermal Flow Sensors for Time and Spatial Resolved Aerodynamic Measurements
Bibliographic record
Abstract
This paper reports on design, fabrication, and characterization of two novels integrated thermal flow sensors, offering low cost, robust structure, high sensibility, fast response and compatible with integration in various microfluidic devices. The first device concerns a thermal mass flow sensor integrated within a fluidic micro-channel. It is composed of a four wire based metallic platinum thin films, connected in a Wheatstone bridge. The particularity of this flow sensor comes from the thermal isolation structure optimized in order to achieve highest measurement precision (30°C temperature variation from ambient, and for power supply of 20mW ), high dynamic range with maximum flow rate close to 10L/min, fast response time of 200µs in constant current operating mode. In addition to that, this innovative concept is realized completely by front-side surface micro machining technology. The second device provides flow velocity measurements even far from the walls. It consist on a MEMS hot wire sensor based on a low stress annealed metallic multilayers thin films (<100 MPa) as a sensing element for low drift and high temperature measurements (TCR close to 2100ppm/°C until 400°C). The sensing element was supported by a pair of prongs made on PECVD Nano-Cristalline Diamond (NCD) for robustness increase and mass thermal decrease.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".