Printed Chemiresistive In<sub>2</sub>O<sub>3</sub> Nanoparticle-Based Sensors with ppb Detection of H<sub>2</sub>S Gas for Food Packaging
Bibliographic record
Abstract
Cost-effective and disposable smart sensing technologies capable of monitoring packaged foods’ degradation are necessary for human health and the growing processed-food industry. Herein, highly sensitive and selective hydrogen sulfide (H 2 S) gas sensors were fabricated from solution-printed nanocomposites comprising indium oxide nanoparticles (In 2 O 3 NPs), graphite flakes (Gt), polystyrene (PS), and copper acetate monohydrate (CuAc). The standard In 2 O 3 NP-based sensor (SS, without CuAc) showed H 2 S detection ≈100 ppb under ambient conditions. The presence of CuAc resulted in a highly sensitive nanocomposite layer enabling the detection of lower than 100 ppb (<100 ppb) concentrations of H 2 S gas levels, far superior to the standard In 2 O 3 NP-based nanocomposite. Adding CuAc to the In 2 O 3 NP-based nanocomposite as a modifying additive leads to copper sulfide (CuS) formation owing to its reaction with H 2 S gas. CuS significantly enhances the nanocomposite layer’s conductivity and H 2 S reactions on the sensors’ surface, resulting in a substantial reduction in electrical resistance for sensors. The modified In 2 O 3 NP-based sensor presents remarkable enhancement in their selectivity toward H 2 S gas detection when evaluated against various hazardous vapors. Furthermore, the modified In 2 O 3 NP-based sensors possess good anti-humid property under highly humid conditions (≈80% relative humidity).
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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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".