Investigation of geometrical effects in the carbon allotropes manipulation based on AFM: multiscale approach
Source abstract
Abstract
Carbon allotropes are used as nanocarriers for drug and cell delivery. To obtain an accurate result in the nanoscale, it is important to use a precise model. In this paper, a multiscale approach is presented to investigate the manipulation process of carbon allotropes based on atomic force microscopy (AFM). For this purpose, the AFM setup is separated into two parts with different sizes as macro field (MF) and nano field (NF). Using Kirchhoff’s plate model, the cantilever (the main part of MF) is modeled. The molecular dynamics method is applied to model the NF part, and then the MF and NF are coupled with the multiscale algorithm. With this model, by considering the effect of size and shape, the manipulation of carbon allotropes is carried out. The manipulations of armchair CNTs and fullerenes are performed to study the diameter changing effects. The result shows that the manipulation and friction force increases by increasing the diameter. The result of the indentation depth for the armchair CNTs indicates that decreasing the diameter causes the indentation depth to reduce. Moreover, the manipulations of four kinds of carbon allotropes with the same number of atoms have been studied to investigate the geometrical effects. The shapes of these nanoparticles change from sphere to cylinder. The results illustrate that the manipulation and the friction force decrease as the nanoparticle shape varies from sphere to cylinder. The Von-Mises results demonstrate that by changing the nanoparticle shape from the spherical to the cylindrical form, the stress increases, although the manipulation force reduces.
Source: openaire · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Force Microscopy Techniques and Applications · Advanced Surface Polishing Techniques · Advanced machining processes and optimization
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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