A rapid and automated relocation method of an AFM probe for high-resolution imaging

Abstract

The atomic force microscope (AFM) is one of the most powerful tools for high-resolution imaging and high-precision positioning for nanomanipulation. The selection of the scanning area of the AFM depends on the use of the optical microscope. However, the resolution of an optical microscope is generally no larger than 200 nm owing to wavelength limitations of visible light. Taking into consideration the two determinants of relocation-relative angular rotation and positional offset between the AFM probe and nano target-it is therefore extremely challenging to precisely relocate the AFM probe to the initial scan/manipulation area for the same nano target after the AFM probe has been replaced, or after the sample has been moved. In this paper, we investigate a rapid automated relocation method for the nano target of an AFM using a coordinate transformation. The relocation process is both simple and rapid; moreover, multiple nano targets can be relocated by only identifying a pair of reference points. It possesses a centimeter-scale location range and nano-scale precision. The main advantages of this method are that it overcomes the limitations associated with the resolution of optical microscopes, and that it is label-free on the target areas, which means that it does not require the use of special artificial markers on the target sample areas. Relocation experiments using nanospheres, DNA, SWCNTs, and nano patterns amply demonstrate the practicality and efficiency of the proposed method, which provides technical support for mass nanomanipulation and detection based on AFM for multiple nano targets that are widely distributed in a large area.

Spatial Manipulation and Assembly of Nanoparticles by Atomic Force Microscopy Tip-Induced Dielectrophoresis

“…As shown in the inset image of the SCM-PIT probe in Figure a, although very few nanoparticles stick at the tip, the convolution effect , of the SCM-PIT probe is obvious at nanoscale. To determine the morphology and structure of the nanoassembly results with high resolution and precision, we obtained a scan image of the result using a new TESP probe in the tapping mode with the help of a rapid and automated relocation method for the AFM probe . The AFM image of the corresponding result and the SEM image of the new TESP probe are shown in Figure b.…”

“…To determine the morphology and structure of the nanoassembly results with high resolution and precision, we obtained a scan image of the result using a new TESP probe in the tapping mode with the help of a rapid and automated relocation method for the AFM probe. 44 The AFM image of the corresponding result and the SEM image of the new TESP probe are shown in Figure 5b. A comparison of the AFM scanned profiles in Figure 5c, which correspond to the crosssections in Figure 5a,b, demonstrates that more precise characterization of the experimental result could be achieved using the new TESP probe.…”

Gigantol from Dendrobium chrysotoxum Lindl. binds and inhibits aldose reductase gene to exert its anti-cataract activity: An in vitro mechanistic study

“…From the images of AFM, we observed that AR gene was forming many fragments of varying sizes after the interaction of AR gene and gigantol. The results of AFM provided great help in further identifying the binding interaction of the small molecule with DNA (Zhang HY et al, 2013;Zhou P et al, 2016). Previous findings demonstrated that when linear DNA interacts with the drug, the latter can be inserted into the DNA double helix between the base pairs; this could be followed by the formation of a number of spiral DNA molecular chains, which can be cut into many fragments (Nahid Shahabadi, 2011;Salerno D et al, 2010;Hou XM et al, 2009).…”

Autonomous Experiments in Scanning Probe Microscopy and Spectroscopy: Choosing Where to Explore Polarization Dynamics in Ferroelectrics

“…The CNN is trained along with the GP model in the process and is a form of deep kernel learning. 41 We utilized the radial basis function (RBF) kernel, along with a simple convolutional neural network with four layers, with filter numbers of [1,2,4,8], respectively, and compare the technique against random search as well as one where only the image pixel values are utilized. Note that in this trial we only select a single pixel to sample next, as opposed to a batch.…”

“…At the same time, it is well understood that the information of interest in most cases tends to be concentrated in a small number of spatial locations that are often associated with specific microstructural elements or imposed by nonlocal boundary effects. These considerations, combined with recent notable advances in the field of autonomous vehicles and robotics, have stimulated intensive discussion of the opportunities opened by automated experiment in microscopy, with concepts ranging from tuning of specific parameters or regions of image space to fully automated self-driving microscopes − being proposed.…”