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Record W3185444790 · doi:10.1149/ma2021-01561524mtgabs

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

2021· article· en· W3185444790 on OpenAlexaff
Ahmad Al Shboul, Ricardo Izquierdo

Bibliographic record

VenueECS Meeting Abstracts · 2021
Typearticle
Languageen
FieldEngineering
TopicAdvanced Chemical Sensor Technologies
Canadian institutionsÉcole de Technologie Supérieure
Fundersnot available
KeywordsHydrogen sulfideDetection limitOdorFlammable liquidAnnealing (glass)NanotechnologyParts-per notationEnvironmental scienceSulfurComputer scienceMaterials scienceProcess engineeringChemistryEngineeringOrganic chemistryMetallurgy

Abstract

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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 (&gt;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 (&lt;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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.148
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.010
GPT teacher head0.218
Teacher spread0.208 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

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".

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Citations0
Published2021
Admission routes1
Has abstractyes

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