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Enregistrement W3025726359 · doi:10.1149/ma2020-01282136mtgabs

Zinc Oxide Sensing Devices for Meat Spoilage Detection

2020· article· en· W3025726359 sur OpenAlexaff
Jennifer Bruce, Carissa Ouellette, Ken Bosnick

Notice bibliographique

RevueECS Meeting Abstracts · 2020
Typearticle
Langueen
DomaineEngineering
ThématiqueAdvanced Chemical Sensor Technologies
Établissements canadiensNational Research Council Canada
Organismes subventionnairesnon disponible
Mots-clésFood spoilageNanomaterialsMaterials scienceDimethylamineNanostructureNanotechnologyNanoparticleChemical engineeringSubstrate (aquarium)MethylamineChemistryOrganic chemistry

Résumé

récupéré en direct d'OpenAlex

Introduction In North America, over 20% of the initial production of meat is lost due to waste. Losses and waste in industrialized regions are highest at the end of the food supply chain due to high per capita meat consumption and large amounts of waste generated by retailers and consumers [1]. Smart materials and devices can reduce this waste by detecting meat spoilage in the early stages. When meat proteins begin to degrade, they release biogenic amines (e.g. putrescine, NH 2 (CH 2 ) 4 NH 2 ). Detection of these amines can lead to an early indication of meat spoilage [2-4]. Gas sensing semiconductors such as ZnO can be used for this detection by measuring a change in electrical resistance due to charge transfer caused by the amine molecule on the surface. In this work, a chemiresistive response to amines is tested for by utilizing model test gases, including methylamine (NH 2 CH 3 ) and dimethylamine (NH(CH 3 ) 2 ), with novel, synthesized ZnO nanomaterials optimized for this application. Sensor Fabrication and Testing ZnO nanostructures are synthesized directly onto interdigitated electrode substrates via a two-step hydrothermal process. ZnO nanomaterial deposition on the substrate surface is first seeded from solution, followed by nanostructure growth from a second solution at elevated temperatures. The morphology of the ZnO nanostructure deposits is controlled by adjusting the pH of the growth solution and by adjusting the point at which the seeded substrates are immersed into the growth solution. Different metal catalyst nanoparticles are deposited onto the ZnO via a wet chemical method to optimize the device response to amines and lower the operating temperature [5]. The ZnO nanostructures are characterized by SEM, TEM, and XPS (Figure 1). The sensor response to amines is tested using a home built apparatus. The amine test gas is diluted in air through mass flow controllers and then flowed over the device under test while the device resistance is monitored. The device temperature is set by a resistive heater and thermocouple located near the device and a closed loop controller. The entire apparatus is controlled by a central computer through a LabView script. Interference from moisture is tested for by flowing water saturated air over the device under test and monitoring for changes in resistance. Results and Conclusions A strong chemiresistive response to methylamine is found using the ZnO nanostructures, with a large increase in sensitivity occurring after decorating the nanostructures with Pd catalyst particles (Figure 1) [6]. The morphology of the ZnO nanostructure is optimized during synthesis as described above and is found to have a strong impact on the sensor response. The optimal response is found from a flower-like nanostructure morphology with a high surface area, large aspect ratios, and a percolated electrical network connectivity, as shown in Figure 1. Optimization of the morphology and the catalyst loading leads to a decrease in the operating temperature of the device and the observance of room temperature sensing. Experiments are underway to further characterize these responses and optimize the devices for high sensitivity, room temperature (or low temperature) operation. Interferences from moisture in the air would lead to issues with adoption of the technology and therefore tests are underway to check for such interferences. The ZnO materials have been successfully doped with Ga and Al, and experiments are planned to optimize the device response via this doping, as well as by UV exposure of the ZnO. References [1] FAO. 2011. Global food losses and food waste – Extent, causes and prevention. Available from www.fao.org/docrep/014/mb060e/mb060e.pdf [2] F. Galgano, F. Favati, et al., Role of Biogenic Amines as Index of Freshness in Beef Meat Packed with Different Biopolymeric Materials, Food Res. Int. 42, 1147-1152 (2009); doi.org/10.1016/j.foodres.2009.05 [3] CC. Balamatsia, EK. Paleologos, et al., Correlation Between Microbial Flora, Sensory Changes and Biogenic Amines Formation in Fresh Chicken Meat Stored Aerobically or Under Modified Atmosphere Packaging at 4 °C, Antonie Leeuwenhoek 89, 9-17 (2006); doi.org/10.1007/s10482-005-9003-4 [4] C. Ruiz-Capillas, F. Jimenez-Colmenero, Biogenic Amines in Meat and Meat Products, Crit. Rev. Food Sci. Nutr. 44, 489-499 (2005); doi.org/10.1080/10408690490489341 [5] Deng, SB. Sang, PW. Li, G. Li, FQ. Gao, YJ. Sun, WD. Zhang, J. Hu, Preparation, Characterization, and Mechanistic Understanding of Pd-Decorated ZnO Nanowires for Ethanol Sensing, J Nanomater 2013, 8 pages (2013); doi.org/10.1155/2013/297676 [6] Bosnick, LL. Tay, J. Bruce, B. Smith, H. Zhang, C. Shwartz, Meat Spoilage Sensing Devices, TechConnect Briefs 3, 12 (2018) 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 distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,001
Version: codex-gemma-dda1882f352aStatut 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 enseignants0,164
Score d'incertitude au seuil0,724

Scores Codex et Gemma par catégorie

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

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 Opus0,018
Tête enseignante GPT0,225
Écart entre enseignants0,207 · 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 tête enseignante, 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

Citations1
Publié2020
Routes d'admission1
Résumé présentoui

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