Modeling of the PolyMUMPs-Based MEMS Sensor for Application in Trace Gas Detection
Bibliographic record
Abstract
Gas detection sensor is crucial in many practical applications. However, numerous of the existing gas sensors still suffering from high power consumption, damping, and poor accuracy. These factors have a significant impact on the gas detection sensor's sensitivity and reliability. A Micro-Electro-Mechanical System (MEMS) is presented in this paper, along with its model with high efficiency. The sensor is based on standard Polysilicon Multi-Users-MEMS-Process (PolyMUMPs). The detection of gaseous species is dependent on a changes in the sensor's resonance frequency. The resonance frequency, quality factor, and mass sensitivity are observed to reduce as the beam length increases and to rise as the beam width increases. While overall mass rises as the length/width of the beam both increases. The analytical findings of the resonance frequency, quality factor, and mass sensitivity are found to be 9.3747 kHz, 4.5183, and 5.1676 mHz/pg, respectively.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.002 | 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".