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Development and Statistical Analysis of Graphene-Based Gas Sensors.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Development and Statistical Analysis of Graphene-Based Gas Sensors./
Author:	Capman, Nyssa.
Description:	1 online resource (239 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 85-11, Section: B.
Contained By:	Dissertations Abstracts International85-11B.
Subject:	Electrical engineering. -
Online resource:	click for full text (PQDT)
ISBN:	9798382721699

Development and Statistical Analysis of Graphene-Based Gas Sensors.
Capman, Nyssa.

Development and Statistical Analysis of Graphene-Based Gas Sensors. - 1 online resource (239 pages)

Source: Dissertations Abstracts International, Volume: 85-11, Section: B.

Thesis (Ph.D.)--University of Minnesota, 2024.

Includes bibliographical references

The use of graphene in gas sensors has been increasing in recent years, as graphene has many attractive properties including high carrier mobility, excellent conductivity, and high surface-area-to-volume ratio. Both individual graphene sensors and "electronic nose" (e-nose) graphene sensor arrays have been applied to detecting many gaseous chemicals involved in indoor and outdoor air pollution, food quality, and disease detection in breath. Volatile organic compounds (VOCs) are one important category of chemicals in all of these applications, as they can be health hazards, indicators of food spoilage, or biomarkers for the presence of diseases. While graphene sensors have been shown to be effective at detecting and discriminating between VOCs in certain contexts, limitations still exist which prevent graphene sensors from achieving their full potential. This dissertation will describe solutions to several of these problems, including improving selectivity through functionalization without compromising rapid operation and detecting target analytes in the presence of a background interferant.E-nose systems are a developing technology which combines gas sensors into arrays and uses their collective data to determine the identity of the gases that they are exposed to. Each individual sensor on the array is ideally selective to a different gaseous compound, and together the sensors form response patterns unique to each gas mixture. Machine learning is used to discriminate between these patterns and predict the gas mixture being sensed. A graphene-based e-nose comprised of 108 sensors functionalized with 36 different chemical receptors was applied to sensing a set of 5 VOCs at 4 concentrations each. The 5 analytes (ethanol, hexanal, methyl ethyl ketone, toluene, and octane) were chosen based on their importance as indicators of diseases such as lung cancer in human breath, since disease diagnosis in exhaled breath is one possible application of these graphene sensor arrays. Despite data acquisition of all 108 sensors occurring rapidly in only 10s of seconds, the VOC discrimination ability of the sensor arrays was found to be near-perfect (98%) when using a Bootstrap Aggregated Random Forest classifier. Even with the addition of 1-octene, a compound highly similar to octane and therefore likely to cause high numbers of misclassifications, the sensors still achieved high classification accuracy (89%).However, while the sensor arrays achieved great success in discrimination of single VOCs, their abilities are challenged in the face of complex gas mixtures. Interferants such as oxygen are present in large concentrations in many potential applications of graphene sensors, such as disease diagnosis in human breath or environmental monitoring of toxic gases. In these cases, the oxygen obscures the important response signals of the VOCs. To improve the mixed gas discrimination abilities of the sensor arrays, the behavior of individual, unfunctionalized graphene varactors was examined in the presence of VOCs mixed with oxygen. Response signal patterns unique to each VOC + O2 mixture were revealed. As these patterns developed over the entire gas exposure period, a Long Short-Term Memory (LSTM) network was chosen to classify the gas mixtures as this algorithm utilizes the entire time series. Even in the presence of varying levels of oxygen, three VOCs (ethanol, methanol, and methyl ethyl ketone) at 5 concentrations each could be classified with 100% accuracy, and the VOC concentration could be resolved within approximately 100-200 ppm. This discrimination success was also possible despite the sensors exhibiting varied drift patterns typical of graphene sensors.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798382721699Subjects--Topical Terms:

596380
Electrical engineering.
Subjects--Index Terms:

Gas sensorIndex Terms--Genre/Form:

554714
Electronic books.

Development and Statistical Analysis of Graphene-Based Gas Sensors.
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