Thin Film Gas Sensors Based on Planetary Ball-Milled Zinc Oxide Nanoinks: Effect of Milling Parameters on Sensing Performance
Bibliographic record
Abstract
Planetary ball-milled zinc oxide (ZnO) nanoparticle suspensions (nanoinks) were used to produce thin film chemiresistive gas sensors that operate at room temperature. By varying milling or grinding parameters (speed, time, and solvent) different thin film gas sensors with tunable particle sizes and porosity were fabricated and tested with dry air/oxygen against hydrogen, argon, and methane target species, in addition to relative humidity, under ambient light conditions. Grinding speeds of up to 1000 rpm produced particle sizes and RMS thin film roughness below 100 nm, as measured by atomic force and scanning electron microscopy. Raman spectroscopy, photoluminescence, and X-ray analysis confirmed the purity and structure of the resulting ZnO nanoparticles. Gas sensor response at room temperature was found to peak for nanoinks milled at 400 rpm and for 30 min in ethylene glycol and deionized water, which could be correlated to an increased film porosity and enhanced variation in electron concentration resulting from adsorption/desorption of oxygen ions on the surfaces of ZnO nanoparticles. Sensor response and dynamic behavior was found to improve as the temperature was increased, peaking between 100 and 150 °C. This work demonstrates the use of low-cost PBM nanoinks as the active materials for solution-processed thin film gas/humidity sensors for use in environmental, medical, food packaging, laboratory, and industrial applications.
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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 | 0.001 |
| 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 | 0.001 | 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".