Metallic nanoparticles functionalizing carbon nanotube networks for gas sensing applications
Bibliographic record
Abstract
We report the fabrication of carbon nanotube (CNT) based gas sensors functionalized with different metallic nanoparticles (NPs) (Au, Pd, Ag) with exceptionally high responses towards four test gases (NH3, CO2, CO and ethanol). The CNT networks were fabricated through a low cost spray deposition process while the NPs were deposited by a thermal evaporation process. CNT based gas sensors functionalized with Au with a nominal thickness of 1.0 nm showed superior response towards NH3, CO and ethanol. The sensors' normalized responses reached 92%, 22% and 32% with concentrations of 100 ppm, 50 ppm and 100 ppm for NH3, CO and ethanol respectively. CNT based gas sensors functionalized with Pd with a nominal thickness of 1.5 nm showed the best performance with CO2. The normalized response reached 3%, 6%, 12% and 17% with concentrations of 500 ppm, 1000 ppm, 2500 ppm and 5000 ppm of CO2 respectively. We also investigated the morphological and optical changes that occur to the NPs upon thermal treatment. Functionalization of CNT films deposited on glass with Au and Ag showed surface plasmon resonance effects that are dependent on the nominal thickness of the functionalization layer.
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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) | 0.000 | 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 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".