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Optimum Choice of Randomly Oriented Carbon Nanotube Networks for UV-Assisted Gas Sensing Applications
Abstract
We investigated the noise and photoresponse characteristics of various optical transparencies of nanotube networks to identify an optimal randomly oriented network of carbon nanotube (CNT)-based devices for UV-assisted gas sensing applications. Our investigation reveals that all of the studied devices demonstrate negative photoconductivity upon exposure to UV light. Our studies confirm the effect of UV irradiation on the electrical properties of CNT networks and the increased photoresponse with decreasing UV light wavelength. We also extend our analysis to explore the lowfrequency noise properties of different nanotube network transparencies. Our findings indicate that devices with higher nanotube network transparencies exhibit lower noise levels. We conduct additional measurements of noise and resistance in an ethanol and acetone gas environment, demonstrating the high sensitivity of higher-transparent (lower-density) nanotube networks. Overall, our results indicate that lower-density nanotube networks hold significant promise as a viable choice for UV-assisted gas sensing applications.
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Authors (12)
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Adil Rehman
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Aleksandra Krajewska
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Bartlomiej Stonio
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Pavlo Sai
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Aleksandra Przewłoka
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Maciej Filipiak
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Krystian Pavłov
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Grzegorz Cywinski
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Dmitry V. Lyubchenko
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Sergey Rumyantsev
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Full text
- Publication version
- Accepted or Published Version
- DOI:
- Digital Object Identifier (open in new tab) 10.1021/acssensors.3c01185
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- Category:
- Articles
- Type:
- artykuły w czasopismach
- Published in:
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ACS Sensors
no. 8,
pages 3547 - 3554,
ISSN: 2379-3694 - Language:
- English
- Publication year:
- 2023
- Bibliographic description:
- Drozdowska K., Rehman A., Smulko J., Krajewska A., Stonio B., Sai P., Przewłoka A., Filipiak M., Pavłov K., Cywinski G., Lyubchenko D. V., Rumyantsev S.: Optimum Choice of Randomly Oriented Carbon Nanotube Networks for UV-Assisted Gas Sensing Applications// ACS Sensors -Vol. 8,iss. 9 (2023), s.3547-3554
- DOI:
- Digital Object Identifier (open in new tab) 10.1021/acssensors.3c01185
- Sources of funding:
- Verified by:
- Gdańsk University of Technology
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