Nanocomposite of Nitrogen‐Doped Graphene/Polyaniline for Enhanced Ammonia Gas Detection
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Graphene nanosheets are widely used for designing functional nanocomposite sensors that are highly sensitive. In this study, nitrogen‐doped reduced graphene oxide (N‐rGO) polyaniline (PANI) nanocomposites composed of localized heterojunctions are prepared for the detection of ammonia, dimethylamine, and trimethylamine gases with superior sensing performances. rGO nanosheets with electrical properties modified via N‐doping are strategically incorporated in p‐type PANI via in situ synthesis, with the nanosheets acting as templates for PANI growth. N‐rGO nanosheets featuring large specific area, high electrical conductivity, and n‐type semiconductive behavior combined with the attractive electrical p‐type characteristics of PANI are found to be highly beneficial for improving detection sensitivity toward ammonia, dimethylamine, and trimethylamine gases at 25 °C. Overall, the detection sensitivity of the advanced N‐rGO nanocomposites is more than two times higher than that of PANI alone. Moreover, the N‐rGO/PANI nanocomposites reach an estimated limit of detection for ammonia gas down to the sub‐ppm range. Improvement in sensing performance is also observed for rGO/PANI and GO/PANI nanocomposites; however, the level of the improvement is less than that of N‐rGO/PANI nanocomposites. This study demonstrates the excellent potential of designing advanced graphene nanocomposite gas sensors with superior performances by manipulating the electronic properties of the graphene nanosheets.
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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) | 0.001 | 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 it