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 (H2S) gas sensors were fabricated from solution-printed nanocomposites comprising indium oxide nanoparticles (In2O3 NPs), graphite flakes (Gt), polystyrene (PS), and copper acetate monohydrate (CuAc). The standard In2O3 NP-based sensor (SS, without CuAc) showed H2S 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 H2S gas levels, far superior to the standard In2O3 NP-based nanocomposite. Adding CuAc to the In2O3 NP-based nanocomposite as a modifying additive leads to copper sulfide (CuS) formation owing to its reaction with H2S gas. CuS significantly enhances the nanocomposite layer’s conductivity and H2S reactions on the sensors’ surface, resulting in a substantial reduction in electrical resistance for sensors. The modified In2O3 NP-based sensor presents remarkable enhancement in their selectivity toward H2S gas detection when evaluated against various hazardous vapors. Furthermore, the modified In2O3 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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".