A Flexible Indium Oxide Sensor With Anti-Humidity Property for Room Temperature Detection of Hydrogen Sulfide
Bibliographic record
Abstract
In this work, flexible hydrogen sulfide (H2S) sensors were prepared from nanocomposite mixtures of indium oxide (In2O3), graphite flakes (Gt) and polystyrene (PS). Where In2O3was chosen as the sensing material. Gt flakes were added to adjust the sensor resistance in the range of 300-400 k Ω. Also, PS were added as a modifier to maintain the sensors integrity, adhesiveness and flexibility properties. A nanocomposite mixture of In2O3:10%Gt:17%PS emerged as a promising nanocomposite candidate to develop high performance sensors to detect H2S gas. The flexible sensors were fabricated on top of flexible carbon electrodes that were screen-printed on polyethylene terephthalate (PET) substrate. Herein, we propose a sensitive H2gas sensor to detect 100 ppb at room temperature while being resistant to humidity changes. These improvements have been attributed to additional benefits for the Gt flakes and the PS modifier. Both additives contributed to enhance the surface-to-volume ratio for the sensing thin film leading to a superior sensing performance for the In2O3based sensor. Furthermore, they improved the hydrophobic property for the sensors to develop their resistance to humidity changes. The sensing mechanism for sensors depends mainly on the sulfuration and/or partial sulfuration of In2O3by H2S gas to form In2S3, which is conductive and responsible to decrease the sensor resistance.
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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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".