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Record W3025726359 · doi:10.1149/ma2020-01282136mtgabs

Zinc Oxide Sensing Devices for Meat Spoilage Detection

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

Bibliographic record

VenueECS Meeting Abstracts · 2020
Typearticle
Languageen
FieldEngineering
TopicAdvanced Chemical Sensor Technologies
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsFood spoilageNanomaterialsMaterials scienceDimethylamineNanostructureNanotechnologyNanoparticleChemical engineeringSubstrate (aquarium)MethylamineChemistryOrganic chemistry

Abstract

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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

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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 categoriesnone
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 score0.164
Threshold uncertainty score0.724

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.018
GPT teacher head0.225
Teacher spread0.207 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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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Citations1
Published2020
Admission routes1
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