A Highly Sensitive Dual-Mode Thermal Flow Sensor Based on Calorimetric Mode
Bibliographic record
Abstract
This article reports a dual-mode switching of highly sensitive thermal flow sensors based on calorimetric mode that is more suitable for microflow measurements. Upstream heating and downstream heating modes (which are both calorimetric) are adopted in this article. The sensor consists of two vertically stacked suspended membrane MEMS chips. A gas microchannel is fabricated inside the chip that is mounted perpendicular to the flow direction. Because of the Laval effect, the gas flowing through the internal channels of the chip increases the gas flow velocity and improves the heat exchange efficiency between the chip and the gas; moreover, the dual-chip stacked design increases the thermal resistance of solid heat conduction and the heat exchange area of the gas, thus improving its sensitivity. Simulation results show that the gas flow velocity and heat exchange efficiency increased seven times and three times, respectively. Experimental results show that the sensor achieves high sensitivity measurements of 0.77 mV/(mL/min) for 0–18 mL/min and 0.16 mV/(mL/min) for 18–100 mL/min over the entire range by dual-mode switching. The response time is 25 ms. To the best of the authors’ knowledge, this is the first report of a thermal flow sensor with dual-mode switching of upstream and downstream heating in calorimetric mode. In addition, the sensor can be widely used in semiconductors, hydrogen energy, scientific instruments, and other fields.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".