Biomedical Engineering Reference
In-Depth Information
1
2
3
4
5
6
7
10
150
180
9
7
8
100
130
CNT displacement (nm)
50
80
6
loading
5
3
4
8
1
0
30
unloading
9
2
10
-50
-20
0
2000
4000
6000
Sample displacement (nm)
Fig. 7.13. AFM-based axial compression testing of MWCNTs. Top: schematic sketching of the
motion of AFM probe and attached nanotube. Bottom: typical result. The grey (upper) and black
(lower) traces show loading and unloading response respectively. A remarkable number of features
are reproduced in both curves, showing the reversibility of the transitions in CNTs. Reproduced with
permission from [505].
There are many ways to perform such manipulations, but usually such manufacture is
performed in a semi-manual way, so that the user issues a series of manipulation com-
mands to the AFM, followed by imaging of the results, another batch of commands, etc.
One reason for such a laborious manner of working is the fundamental unpredictability of
assembly at the nanoscale. Nanoscale objects such as atoms, nanoparticles, etc. do not
behave like macro-scale objects, so it is often difficult to predict how they will react to our
manipulation [518]. One way to improve the throughput of nanomanipulation is to
improve the interface, such that a real-time feedback to the user of the results of their
manipulations is possible. There have been various attempts to interface AFMs with
alternative sensing systems such as haptic interfaces (allowing the user to feel the sample)
[519, 520] or virtual reality (allowing the user to see the sample and/or probe in true 3-D)
[520], or combinations of both approaches [521].
For example, an AFM-based nanomanipulator with a haptic interface can be used to
move carbon nanotubes onto micron-scale electrodes in order to measure their electrical
properties [522]. Similar experiments can be carried out with gold nanoparticles as well
