Near field electrospinning of nanowires for multi-modal hydrogen sensing
Bibliographic record
Abstract
Hydrogen gas is a common byproduct in industrial and chemical processes. It is also frequently used in transportation applications such as fuel cell vehicles. It has no smell and no taste, but it may pose immediate safety risks because it is combustible in air. Multi-modal hydrogen sensors are developed by depositing nanofibers on quartz tuning forks (QTF). Near field electrospinning (NFES) was used to produce flexible, semi-conductive nanofibers that can be integrated into electronic systems as environmental gas sensors. The electrospinning parameters, especially tip-to-collector distance, were optimized to increase sensor performance. Treated multi-walled carbon nanotubes, camphorsulfonic acid doped polyaniline and platinum nanoparticles were used as the sensing materials with polyethylene oxide being used as an electrospinning guide. Intense pulsed light and sputter coating were used to maximize adhesion of the fibers onto the devices. The QTF sensor combines mechanical and electrochemical sensing methodologies. Changes in the resonance frequency were used to determine gas adsorption. Changes in the electrical resistance were used to determine the gas properties. As a result, the sensors were selective to hydrogen versus other gases and vapors including methane, hexane, toluene, ammonia, ethanol and carbon dioxide. Furthermore, the sensors can detect ppm levels of hydrogen even in the presence of high humidity.
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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".