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

Abstract

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