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Enregistrement 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 sur OpenAlexaff
Ahmad Al Shboul, Ricardo Izquierdo

Notice bibliographique

RevueECS Meeting Abstracts · 2021
Typearticle
Langueen
DomaineEngineering
ThématiqueAdvanced Chemical Sensor Technologies
Établissements canadiensÉcole de Technologie Supérieure
Organismes subventionnairesnon disponible
Mots-clésHydrogen sulfideDetection limitOdorFlammable liquidAnnealing (glass)NanotechnologyParts-per notationEnvironmental scienceSulfurComputer scienceMaterials scienceProcess engineeringChemistryEngineeringOrganic chemistryMetallurgy

Résumé

récupéré en direct d'OpenAlex

Hydrogen sulfide (H2S) 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, H2S 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 H2S sensors in everyday life as odor sensing systems (or as called electronic nose) in healthcare and food industry applications. H2S 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 H2S 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 (In2O3) 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 H2S concentrations lower than 100 ppb, ultrasensitive H2S 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 H2S gas using a combination of a semiconductor metal oxide (In2O3) and a metal salt (copper acetate, CuAc). Initially, we developed an anti-humid and a sensitive sensor for H2S detection at room temperature with a concentration as low as 100 ppb based on an easily prepared nanocomposite (standard) of indium oxide (In2O3), graphite flakes (Gt), and polystyrene (PS). 1,2 The major drawback of the standard sensor is its response to ammonia (NH3) gas besides H2S. 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 H2S detection.3 Whereas standard sensors (without CuAc) showed a normalized response of 2 after 25 minutes of exposure to 100 ppb H2S 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 H2S gas at room temperature (Figure 1A).3 Furthermore, the modified sensors showed significant enhancement on sensors’ selectivity toward H2S 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 In2O3 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 In2O3 to form In2S3, 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

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Expérimental (laboratoire) · Signal consensuel: Expérimental (laboratoire)
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,012
Score d'incertitude au seuil0,040

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0010,001
Science ouverte0,0010,000
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0120,006

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,010
Tête enseignante GPT0,218
Écart entre enseignants0,208 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeExpérimental (laboratoire)
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2021
Routes d'admission1
Résumé présentoui

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