Development of a Novel MEMS Gas Flowmeter with a Temperature Difference Suspension Structure
Bibliographic record
Abstract
Micro-electro-mechanical system (MEMS) gas flowmeters are innovative devices that use microfabrication technology to measure gas flow with high precision and sensitivity. With MEMS technology, flow measurement can now be performed more accurately and compactly than ever, using low-power, compact, and highly accurate sensors. MEMS gas flowmeters utilize various principles to measure gas flow, including thermal, Coriolis, and pressure differential methods. A micro-flowmeter was developed by combining a MEMS sensor with a weak signal acquisition technique. High heat isolation and sensitivity can be achieved using a MEMS sensor with a thermal resistor-suspended VO2 structure. Since SU-8 gum is used for the flow channel, the technology is simple and affordable, making it suitable for batch production. To acquire high-resolution, low-noise data, the device uses a super low bias current operational amplifier, aided by guard ring protection, and a 24-bit high-resolution ADC. The sensor and data acquisition combination shows that the flowmeter has favorable linearity and sensitivity between 0 and 50 mL/min at a specific offset voltage. Biochemical detection and medicine require a high-sensitivity, high-stability, and low-cost flowmeter.
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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 | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".