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
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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 (In 2 O 3 NPs)‐based nanocomposite for hydrogen sulfide (H 2 S) 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 H 2 S 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 H 2 S gas is based on the contribution of the physical and chemical sensing mechanisms, which rely on the H 2 S molecules’ reactions on the sensor's surface with the adsorbed oxygen molecules and the sensing materials (copper acetate (CuAc) and In 2 O 3 NPs), respectively.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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 it