A Novel Gas Sensor Based on Tunneling-Field-Ionization on Whisker-Covered Gold Nanowires
Bibliographic record
Abstract
<para xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> 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 <formula formulatype="inline"> <tex>$(I-V)$</tex></formula> 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 <formula formulatype="inline"><tex>$\hbox{HAuCl}_{4}$</tex></formula> to form gold nanowhiskers during the electrochemical nucleation of AuNWs. The sensor was tested in three elemental gases: Ar, <formula formulatype="inline"><tex>$\hbox{N}_{2}$</tex> </formula> and He, in a pressure range of <formula formulatype="inline"><tex>$0.01≪P≪100$</tex> </formula> torr. Each gas demonstrated a distinctive <formula formulatype="inline"> <tex>$I-V$</tex></formula> 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. </para>
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".