Abstract
Graphene nanoribbon (GNR), a superior material with two-dimensional structure and monolayer honeycomb of carbon, is noteworthy and important in all fields’ mainly electronic, chemistry, biology, physics and nanotechnology. Recently, observing about sensors demonstrates that for better accuracy, faster response time and enlarged sensitivity, it needs to be improved. Nowadays, carbon-based equipments as an exclusive substance are remarkable in the sensing technology. High conductivity as unique properties caused that graphene can be used in biological applications. Gas sensor based on graphene can be supposed to have great sensitivity for gas molecules detection. In this study, graphene-based carbon dioxide sensor analytically is modeled. In addition, new methods of gas sensor model based on the gradient of GNR conductance are provided. Also, a field effect transistor-based structure as a modeling platform is suggested. Ultimately, optimum model is evaluated by comparison study between analytical model and experimental performance.
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Acknowledgments
Authors would like to acknowledge “This work is financed by Malaysia–Japan International Institute of Technology (MJIIT) (Universiti Teknologi Malaysia Vote: 4J010, Project Titled: Intelligent Fault Detection and Diagnosing for Process Plant.” Also thanks to the Research Management Center (RMC) of University Teknologi Malaysia (UTM) for providing excellent research environment in which to complete this work.
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Akbari, E., Yousof, R., Ahmadi, M.T. et al. The effect of concentration on gas sensor model based on graphene nanoribbon. Neural Comput & Applic 24, 143–146 (2014). https://doi.org/10.1007/s00521-013-1463-2
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DOI: https://doi.org/10.1007/s00521-013-1463-2