Influence of pH on Room-Temperature Synthesis of Zinc Oxide Nanoparticles for Flexible Gas Sensor Applications
Bibliographic record
Abstract
This research contributes to work on synthesizing zinc oxide nanoparticles (ZnO NPs) at room temperature (RT) and their utilization in flexible gas sensors. RT ZnO NP synthesis with a basicity solution (pH ≈ 13) demonstrates an efficient method for synthesizing well-crystalline ZnO NPs (RT.pH13) comparable to those synthesized by the hydrothermal method (hyd.C). The RT.pH13 achieved a high thermal stability with minimal organic reside impurities (~4.2 wt%), 30–80 nm particle size distribution, and a specific surface area (14 m2 g−1). The synthesized pre- and post-calcinated RT.pH13 NPs were then incorporated into flexible sensors for gas sensing applications at ambient conditions (RT and relative humidity of 30–50%). The pre-calcinated ZnO-based sensor (RT.pH13) demonstrated superior sensitivity to styrene and acetic acid and lower sensitivity to dimethyl-6-octenal. The calcinated ZnO-based sensor (RT.pH13.C) exhibited lower sensitivity to styrene and acetic acid, but heightened sensitivity to benzene, acetone, and ethanol. This suggests a correlation between sensitivity and structural transformations following calcination. The investigation of the sensing mechanisms highlighted the role of surface properties in the sensors’ affinity for specific gas molecules and temperature and humidity variations. The study further explored the sensors’ mechanical flexibility, which is crucial for flexible Internet of Things (IoT) applications.
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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 | 0.000 |
| 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 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".