Nanocomposite-Decorated Filter Paper as a Twistable and Water-Tolerant Sensor for Selective Detection of 5 ppb–60 v/v% Ammonia
Bibliographic record
Abstract
Ammonia (NH 3 ) sensors proposed for the simultaneous exhalation diagnosis, environmental pollution monitoring, and industrial leakage alarm require high flexibility, selectivity, stability, humidity tolerance, and wide-concentration-range detection; however, technical challenges still remain. Herein, twistable and water-tolerant paper-based sensors integrated over surgical masks have been developed for NH 3 detection at room temperature, via decorating specially designed ternary nanocomposites (ternary-NCs) on the commercial filter paper. The NCs consist of a multiwalled carbon nanotube framework with a polypyrrole nanolayer and are further loaded with Pt nanodots. Benefiting from the synergy effect of ternary components, the ternary-NCs exhibit an ultrasensitive response to 5 ppb–60 v/v% NH 3 and present high selectivity confirmed by the theory calculations. Remarkably, the filter-paper-based sensors possess outstanding stability against twisting 0–1080°, along with excellent cuttability and foldability. Critically, such paper-based sensors can be integrated over surgical masks for simulated exhaled diagnosis and display superior water tolerance even being immersed in water for 24 h. Practically, the detecting accuracy of the filter-paper-based sensor toward the simulated exhaled NH 3, environmental NH 3 pollution, and industrial NH 3 leakage is validated using ion chromatography.
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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) | 0.000 | 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".