Parametric Investigation of Carbon Nanotube-Based Nanomechanical Mass Sensors using Structural Mechanics and an Artificial Neural Network Approach

Document Type : Original Research Paper

Authors

1 Department of Computer Engineering, Hamedan University of Technology, Hamedan, Iran.

2 Department of Mechanical Engineering, Hamedan University of Technology, Hamedan, Iran.

10.22084/jrstan.2023.26558.1216

Abstract

 The use of single-walled carbon nanotubes (CNTs) as mechanical sensors to
detect tiny objects has dramatically expanded in the last decade. In this article, the parameters affecting the efficiency of sensors, including the diameter of
single-walled carbon nanotubes (SWCNTs), the length of SWCNTs, SWCNT
chirality, applied strain, and added mass, were investigated. At first, the
effects of the desired parameters were investigated using structural mechanics.
Then, an artificial neural network (ANN) was trained to predict the sensor
behavior in other design points. After the training phase, the ANN-based
model provided an accurate macro-model of a sensor. The results showed
that the nanotube-based sensor could detect a mass of even 10 zeptograms
(1zg=10−21g) and that the applied axial strain significantly increased the
efficiency of the sensor. According to the results, the ANN-based model can
model the dynamic behavior of this type of sensor with significant accuracy.
Moreover, the ANN-based model is 104 orders of magnitude faster than the
existing models in structural mechanics.

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