Multi-Walled Carbon Nanotubes Decorated with Silver Nanoparticles for Acetone Gas Sensing at Room Temperature
Bibliographic record
Abstract
Multi-walled carbon nanotubes (MWCNTs) without and with adsorbed silver nanoparticles (Ag-NPs), are used to detect acetone vapour. MWCNTs are grown on SiO2/Si substrates and silver (Ag) nanoparticles (NPs) are deposited onto some of these MWCNTs using electron beam evaporation method. The sensitivity of CNT based sensors (with and without NPs) increases with the concentration of acetone vapour (50 ppm to 800 ppm) while a substantial rise in sensitivity is obtained from MWCNTs with Ag NPs. Band diagrams of the MWCNTs, with and without NPs, are analyzed to understand the gas molecules adsorption phenomena. This study is the first to establish that such sensors can operate at 27 °C rather than the 180 °C–450 °C used elsewhere, thus offering significant advantages over existing methods. To investigate the sensors’ dependability, they’re exposed to three cycles of 50 ppm acetone gas. These tests show that the devices’ responses remain unchanged, indicating their reliability. The effects of humidity upon MWCNT acetone sensors within 100 ppm of acetone vapour are also studied and improved performance towards stability and response/recovery is observed for the sensors with Ag-NPs. Furthermore, higher selectivity is observed for the Ag-coated sensors for acetone against various target gases (acetone, ethanol, NO2, ammonia, and acetone with water).
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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".