• 文献标题:   The effect of concentration on gas sensor model based on graphene nanoribbon
  • 文献类型:   Article, Proceedings Paper
  • 作  者:   AKBARI E, YOUSOF R, AHMADI MT, KIANI MJ, RAHMANI M, ABADI HKF, SAEIDMANESH M
  • 作者关键词:   graphene nanoribbon, co2 sensor, conductance, field effect transistor fet
  • 出版物名称:   NEURAL COMPUTING APPLICATIONS
  • ISSN:   0941-0643 EI 1433-3058
  • 通讯作者地址:   Univ Teknol Malaysia
  • 被引频次:   9
  • DOI:   10.1007/s00521-013-1463-2
  • 出版年:   2014

▎ 摘  要

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.