Scholarlink

<p>The changes in Carbon Nanotubes (CNTs)’s electrical response when subjected to different gases make them have an outstanding sensitivity for use in sensing applications in comparison with other gas detection devices. In this study, the analytical model of the sensing mechanism for a Field Effect Transistor (FET) based structure as a platform for a gas detection sensor is used, in which the CNT conductance change resulting from the chemical interaction between NH<sub>3</sub> gas and CNT. The two model sensing parameters which are the temperature parameter and gas concentration parameter were optimized using Genetic algorithms (GA). I-V characteristics of the gas sensor are considered for the comparative study between the analytical model and the experimental data under exposure to different gas concentrations and temperatures. Application of this model in the prediction of gas sensor performance will allow us to obtain a satisfactory accurate comprehension of the sensor response to gas exposure, specifically to detect NH<sub>3</sub>​ as a safeguards signature, where high sensitivity is paramount for early detection of fuel fabrication activities.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Optimization of sensing parameters for carbon nanotube based gas sensors using genetic algorithms

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月3日

查看全文
  • Noha Shaaban,
  • Amal El Gamel,
  • W. I. Zidan

摘要

The changes in Carbon Nanotubes (CNTs)’s electrical response when subjected to different gases make them have an outstanding sensitivity for use in sensing applications in comparison with other gas detection devices. In this study, the analytical model of the sensing mechanism for a Field Effect Transistor (FET) based structure as a platform for a gas detection sensor is used, in which the CNT conductance change resulting from the chemical interaction between NH3 gas and CNT. The two model sensing parameters which are the temperature parameter and gas concentration parameter were optimized using Genetic algorithms (GA). I-V characteristics of the gas sensor are considered for the comparative study between the analytical model and the experimental data under exposure to different gas concentrations and temperatures. Application of this model in the prediction of gas sensor performance will allow us to obtain a satisfactory accurate comprehension of the sensor response to gas exposure, specifically to detect NH3​ as a safeguards signature, where high sensitivity is paramount for early detection of fuel fabrication activities.