Miniaturized Gas Ionization Sensor Based on Field Enhancement Properties of Silicon Nanostructures
Bibliographic record
Abstract
According to principle of the operation, gas field ionization sensors are classified as transduction-based gas sensors. These sensors identify the unknown gases based on their unique ionization properties such as breakdown voltage or tunneling current. Appling 1D nanostructure in gas ionization sensors would enhance the local electric field at the tip of the structures. The average field enhancement coefficient (βtol), considering constructive/destructive interferences of the local electric field of thousands of nanowires in the whole structure, is desired to optimize the design and structure of the gas sensors. Using chemical/electrochemical techniques silicon nanowires were grown on one of the electrodes of the gas sensor. Mechanism of the nanowires formation was modeled and simulated using COMSOL multiphysics simulation tool prior to their fabrication. A gas field ionization tunneling sensor, was designed, fabricated, and tested successfully for several gases like N2, He, and Ar. Estimated βtol of the sensor showed that the electric field strength inside the sensor is 3750 times greater than a planar parallel-plate sensor causing to reduce the breakdown voltages from several thousand volts to the range of 60–70 V for various gases.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".