Dimensionless Analysis of Micro Pirani Gauges for Broad Pressure Sensing Range
Bibliographic record
Abstract
This article proposes a dimensionless thermal analysis of micro-fabricated membranes as Pirani gauges for pressure measurements. Use of dimensionless numbers simplifies the mode l and facilitates understanding. Our model's predictions are consistent with experimental results obtained from heated suspended SiO2/SiN membranes with a sub-micrometer separation distance with the substrate (500 nm). Other systems reported in the literature also confirmed the modelling. This framework is a powerful prediction tool as it allows a study of the effects of key parameters on the sensing pressure range, including geometry, material properties and radiative heat fluxes. It also addresses the effect of the operating mode, either constant temperature or constant power. Furthermore, we propose a methodology for rapid design of Pirani gauge arrays to reach a broad range of pressure measurements from atmospheric pressure to high vacuum (10-3Pa) without microcontroller use. We prove that this new formalism offers a way to optimize the geometry to reach the target application, which essentially depends on the power consumption, material choice and the pressure measurement range.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".