Bringing atomic mapping to the mainstream

Editorial

Rebecca Pool

Thursday, September 14, 2017 - 15:15
Image: Atomic composition of an iron-platinum nanoparticle split into separate parts to show individual atoms. [Colin Ophus and Florian Niekiel/Berkeley Lab]
 
Mapping the internal atomic structure of small particles has just got easier thanks to a new computer algorithm and graphical user interface developed by US-based researchers. 
 
Peter Ercius from the National Center for Electron Microscopy, Berkeley Lab’s Molecular Foundry, and colleagues, hope their latest development will ease the use of atomic electron tomography to characterise materials at the single-atom level.
 
“Unlike biological structures, in materials science every nanoparticle’s structure is unique at the atomic scale,” highlights Ercius. “But with the 3D coordinates, you can start learning about the precise atomic structure and how that structure gives a material its properties.”
 
To map a structure of a nanoparticle in 3D, researchers typically image the particle in 2D from multiple angles and then rely on sophisticated computer algorithms to convert the 2D projections into a 3D reconstruction of the particle.
 
However, the researchers’ latest algorithm is parallelized, meaning that its individual tasks can be split up and run simultaneously on separate computer processors.
 
These separate outcomes are then combined to produce the final result while greatly increases the image-processing speed.
 
As Ercius highlights, the algorithm code is open source to increase the accessibility of the technique.
 
The new GUI makes it easier for researchers to take advantage of computer algorithms designed for atomic electron tomography. [Berkeley Lab]
 
What's more, a newly developed graphical user interface makes it easier for researchers to take advantage of computer algorithms designed for atomic electron tomography.
 
“The user interface will provide a way to set up the calculations and analyze the results while showing all of the options available, so users can optimize their image reconstructions,” says Ercius. “Every nanostructure is unique and requires input from the user to get the best results.”
 
Learn more here.
 
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