Nanowire Sensors Using an Electrical Resonance Approach for Vapor Detection
Bibliographic record
Abstract
Recent advances in our understanding of 1D nanomaterials are paving the way for developing novel platforms for sensors and devices based on multi-physics, multi-modal approaches. Here, we report a new way of detecting volatile organic compounds (VOC) using electrical resonance of a single platinum nanowire. The adsorption of molecular dipoles on a nanowire causes a measurable change in the dissipation and frequency of the electrical resonance. The dissipation at the resonance shows enhanced variations depending on the dipole moments of the adsorbates. Experimental results show the limit of detection (LOD) for sensing acetone, methanol, and ethanol by a nanowire sensor in the range of a few ppm. The LOD, however, can be improved by optimizing the electrical parameters of the nanowire. Furthermore, monitoring the dissipation variations at resonance as a function of temperature provides information on thermally induced polarization or depolarization of adsorbed chemical species. The temperature response of the nanowire at resonance could potentially be used to discriminate different vapor molecules based on differential calorimetry.
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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 | 0.001 |
| 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 | 0.001 |
| 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".