Printed in<sub>2</sub>O<sub>3</sub>-Based Sensors with ppb H<sub>2</sub>s Sensing at Room Temperature for Healthcare and Food Industry Applications
Bibliographic record
Abstract
Hydrogen sulfide (H 2 S) gas is a well-known poisonous and flammable gas that is produced from the bacterial degradation of organic-sulfur-rich materials in the absence of oxygen. Its production is not limited to volcanos eruptions and mining activities. Besides, it is produced by the metabolism of sulfhydryl-containing amino acids and enzymatic pathways in the mouth. Moreover, it evolves from the bacterial degradation of organic-sulfur rich food. Consequently, H 2 S gas is considered as a potential biomarker for oral malodor analysis and food quality control applications. This opens the gate for the wide use of H 2 S sensors in everyday life as odor sensing systems (or as called electronic nose) in healthcare and food industry applications. H 2 S sensors have been in development for decades. However, drawbacks limit their practical use in the above-mentioned applications that require lower gas detection than 100 ppb. These challenges can be summarised into four issues. The first issue relates to sensors’ sensitivity, as commercial H 2 S gas sensors have a gas detection limit in the ppm range. Second, several preparation procedures for sensors are costly because of comprising the use of time and money-consuming steps such as annealing at high temperatures for hours. For example, researchers found by annealing indium oxide (In 2 O 3 ) at 1000 ℃ for 1-5 hours, it can form nanorods increasing surface-to-volume ratio and then enhancing sensors’ sensitivity. The third issue is that sensors can respond to humidity change leading to faulty response signals from sensors. Finally, complications are related to the operating conditions for sensors such as the use of high operating temperatures (>100 ℃) for metal oxide-based sensors. As the bacterial activities in the mouth and food can produce H 2 S concentrations lower than 100 ppb, ultrasensitive H 2 S sensors in the ppb range at ambient conditions are essential to determine the degree of bacterial activity based on the gas concentrations. Furthermore, promising sensors must be easy to prepare, cost-effective, have excellent anti-humid properties to prevent humidity interference with gas sensors’ response, high chemical stability to avoid degradation of sensing materials, and good mechanical properties (mechanical flexibility) to resist deformation. To build a smart odor sensing system, we developed a printed and flexible chemiresistive gas sensor for quantitative detection of H 2 S gas using a combination of a semiconductor metal oxide (In 2 O 3 ) and a metal salt (copper acetate, CuAc). Initially, we developed an anti-humid and a sensitive sensor for H 2 S detection at room temperature with a concentration as low as 100 ppb based on an easily prepared nanocomposite (standard) of indium oxide (In 2 O 3 ), graphite flakes (Gt), and polystyrene (PS). 1,2 The major drawback of the standard sensor is its response to ammonia (NH 3 ) gas besides H 2 S. We overcame this challenge by modifying sensing layer composition with the addition of a modifying additive of CuAc powder to the nanocomposite (Modified), which boosted sensors’ sensitivity and selectivity toward H 2 S detection. 3 Whereas standard sensors (without CuAc) showed a normalized response of 2 after 25 minutes of exposure to 100 ppb H 2 S gas at room temperature. The modified sensors (with CuAc) exhibited a significant improvement of sensing performance to the gas concentration going to lower than 100 ppb (<100 ppb) sensing at room temperature. At 100 ppb, the modified sensors showed a response of ≈ 18 (9 folds higher than standard sensors) after 60 seconds of exposure to H 2 S gas at room temperature ( Figure 1A ). 3 Furthermore, the modified sensors showed significant enhancement on sensors’ selectivity toward H 2 S gas detection than the standard sensors ( Figure 1B ). Here, The key change in the sensing mechanism for the modified sensors is ascribed to the formation of CuS that can create an ohmic contact with In 2 O 3 leading to enhancement of the conductivity (reduction in resistance) of the nanocomposite layer. In the standard sensor, the sensing mechanism depends on the sulfuration of In 2 O 3 to form In 2 S 3 , which conductive and responsible for the resistance reduction. References A. Al Shboul, A. Shih, M. Oukachmih, and R. Izquierdo, in 2019 IEEE SENSORS ,, vol. 2019-Octob, p. 1–4, IEEE (2019) https://ieeexplore.ieee.org/document/8956528/. A. Al Shboul, A. Shih, and R. Izquierdo, IEEE Sens. J. , 1–1 (2020) https://ieeexplore.ieee.org/document/9145740/. A. Al Shboul and R. Izquierdo, in 4th International Conference of Theoretical and Applied Nanoscience and Nanotechnology (TANN’20) ,, vol. 137, p. 681–686 (2020) https://avestia.com/TANN2020_Proceedings/files/paper/TANN_139.pdf. Figure 1
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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 | 0.000 | 0.001 |
| 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 | 0.001 | 0.002 |
| 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".