Highly Sensitive and Contactless Ammonia Detection Based on Nanocomposites of Phosphate-Functionalized Reduced Graphene Oxide/Polyaniline Immobilized on Microstrip Resonators
Bibliographic record
Abstract
Ammonia is a key compound in a variety of industrial sectors, including automotive, chemical, and food. Its hazardous effects on the environment and human health require the implementation of proper safety guidelines and monitoring techniques. An attractive approach is to add sensing functionality to low-cost wireless communication devices to allow for the monitoring/mapping of the chemical environment across a large area. This study outlines a highly sensitive contactless ammonia gas sensor with the potential for continuous and wireless mapping of ammonia emissions by integrating an antenna on the device. The devices were fabricated by casting a novel advanced sensing nanocomposite, polyaniline (PANI), and phosphate-functionalized reduced graphene oxide (P-rGO) on split-ring resonators (SRRs). P-rGO incorporation in PANI produced a positive-sensing synergistic effect to multiply the sensing response severalfold to ammonia and dimethylamine gases. Furthermore, we identified that the modification of the semiconductive behavior of the nanosheets, achieved via phosphate functionalization, is the key factor to the positive-sensing synergy observed in the nanocomposites because of the formation of localized heterojunctions. The prepared SRRs exhibited remarkably a low detection limit, ∼1 ppm, to ammonia gas, as well as good stability and selectivity, which paves the path for a novel generation of wireless, chipless, potentially fully printable, and passive sensor platforms.
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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 | 0.001 | 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".