Hydrogen Sulfide Gas Detection in ppb Levels at Room Temperature with a Printed, Flexible, Disposable In<sub>2</sub>O<sub>3</sub> NPs‐Based Sensor for IoT Food Packaging Applications
Bibliographic record
Abstract
Abstract Flexible printed sensors are essential components for modern Internet of Things applications. They may twist and bend to fit any shape or surface. New potential applications emerge as these sensors’ sophistication and sensing efficiency improve. In this study, a printed sensor is prepared from indium oxide nanoparticles (In2O3 NPs)‐based nanocomposite for hydrogen sulfide (H2S) gas detection at ambient conditions. The as‐fabricated sensor has excellent capabilities, including sensitivity and selectivity to low gas concentrations than 100 ppb (<100 ppb), anti‐humid property up to relative humidity (RH) ≈ 100%, high chemical stability in severe environments, good mechanical flexibility up to 50 bending cycles at 30° bending angle, and good thermomechanical stability between ‐40 °C ‐ 40 °C. Moreover, the sensor detects the low concentrations of H2S gas produced during the spoilage of organosulfur‐rich food (beef and fish) while remaining insensitive to humidity changes up to RH ≈ 100%, resulting in the fist‐of‐its‐type chemiresistive sensor for food packaging application. The sensors’ response to H2S gas is based on the contribution of the physical and chemical sensing mechanisms, which rely on the H2S molecules’ reactions on the sensor's surface with the adsorbed oxygen molecules and the sensing materials (copper acetate (CuAc) and In2O3 NPs), respectively.
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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) | 0.001 | 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".