A Novel Gas Sensor Based on Tunneling-Field-Ionization on Whisker-Covered Gold Nanowires
Bibliographic record
Abstract
Typical gas ionization sensors (GISs) work by fingerprinting the ionization breakdown voltages of the gases to be identified. In this work, we developed a GIS that operates by field-ionizing the unknown gas at exceptionally low voltages. The resultant field-ion current-voltage$(I-V)$characteristic was then used to identify the gas. Freestanding gold nanowires (AuNW), terminated with nanoscale whisker-like features, were employed as field-amplifiers to reduce the field ionization threshold voltages. Synthesis of the AuNWs was carried out by the template-assisted technique accompanied by two alterations: 1) polystyrene (PS) microspheres were incorporated to reduce the compactness of the pores, thus prevent the nanostructures from collapse, and 2) the template was impregnated by$\hbox{HAuCl}_{4}$to form gold nanowhiskers during the electrochemical nucleation of AuNWs. The sensor was tested in three elemental gases: Ar,$\hbox{N}_{2}$and He, in a pressure range of$0.01≪P≪100$torr. Each gas demonstrated a distinctive$I-V$curve, particularly in the field-limited regime. The threshold ionization voltages ranged from 1 to 10 V, almost three orders of magnitude lower than the voltages used in field-ion-microscopy. The low-voltage field ionization was attributed to the field-amplifying nanoscale whiskers on the AuNW tips, as well as the presence of residual amorphous alumina with semiconducting characteristics, due to incomplete removal of the porous anodized alumina (PAA) template.
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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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".