Nanotechnology Now

Our NanoNews Digest Sponsors
Heifer International



Home > Press > Machine learning peeks into nano-aquariums

Illinois researchers have linked electron microscope imaging and machine learning, making it much easier to study nanoparticles in action. The schematic shows how a neural network, middle, works as a bridge between liquid-phase electron microscope imaging, left, and streamlined data output, right. For more information visit, pubs.acs.org/doi/10.1021/acscentsci.0c00430.

Graphic courtesy ACS and the Qian Chen group
Illinois researchers have linked electron microscope imaging and machine learning, making it much easier to study nanoparticles in action. The schematic shows how a neural network, middle, works as a bridge between liquid-phase electron microscope imaging, left, and streamlined data output, right. For more information visit, pubs.acs.org/doi/10.1021/acscentsci.0c00430. Graphic courtesy ACS and the Qian Chen group

Abstract:
In the nanoworld, tiny particles such as proteins appear to dance as they transform and assemble to perform various tasks while suspended in a liquid. Recently developed methods have made it possible to watch and record these otherwise-elusive tiny motions, and researchers now take a step forward by developing a machine learning workflow to streamline the process.

Machine learning peeks into nano-aquariums

Champaign, IL | Posted on August 31st, 2020

The new study, led by Qian Chen, a professor of materials science and engineering at the University of Illinois, Urbana-Champaign, builds upon her past work with liquid-phase electron microscopy and is published in the journal ACS Central Science.

Being able to see – and record – the motions of nanoparticles is essential for understanding a variety of engineering challenges. Liquid-phase electron microscopy, which allows researchers to watch nanoparticles interact inside tiny aquariumlike sample containers, is useful for research in medicine, energy and environmental sustainability and in fabrication of metamaterials, to name a few. However, it is difficult to interpret the dataset, the researchers said. The video files produced are large, filled with temporal and spatial information, and are noisy due to background signals – in other words, they require a lot of tedious image processing and analysis.

“Developing a method even to see these particles was a huge challenge,” Chen said. “Figuring out how to efficiently get the useful data pieces from a sea of outliers and noise has become the new challenge.”

To confront this problem, the team developed a machine learning workflow that is based upon an artificial neural network that mimics, in part, the learning potency of the human brain. The program builds off of an existing neural network, known as U-Net, that does not require handcrafted features or predetermined input and has yielded significant breakthroughs in identifying irregular cellular features using other types of microscopy, the study reports.

“Our new program processed information for three types of nanoscale dynamics including motion, chemical reaction and self-assembly of nanoparticles,” said lead author and graduate student Lehan Yao. “These represent the scenarios and challenges we have encountered in the analysis of liquid-phase electron microscopy videos.”

The researchers collected measurements from approximately 300,000 pairs of interacting nanoparticles, the study reports.

Click here to see liquid-phase electron microscopy with combined machine learning in action.

As found in past studies by Chen’s group, contrast continues to be a problem while imaging certain types of nanoparticles. In their experimental work, the team used particles made out of gold, which is easy to see with an electron microscope. However, particles with lower elemental or molecular weights like proteins, plastic polymers and other organic nanoparticles show very low contrast when viewed under an electron beam, Chen said.

“Biological applications, like the search for vaccines and drugs, underscore the urgency in our push to have our technique available for imaging biomolecules,“ she said. “There are critical nanoscale interactions between viruses and our immune systems, between the drugs and the immune system, and between the drug and the virus itself that must be understood. The fact that our new processing method allows us to extract information from samples as demonstrated here gets us ready for the next step of application and model systems.”

The team has made the source code for the machine learning program used in this study publicly available through the supplemental information section of the new paper. “We feel that making the code available to other researchers can benefit the whole nanomaterials research community,”Chen said.

Chen also is affiliated with chemistry, the Beckman Institute for Advanced Science and Technology and the Materials Research Laboratory at the U. of I.

The National Science Foundation and Air Force Office of Scientific Research supported this study.

####

For more information, please click here

Contacts:
Qian Chen
217-300-1137

Copyright © University of Illinois at Urbana-Champaign

If you have a comment, please Contact us.

Issuers of news releases, not 7th Wave, Inc. or Nanotechnology Now, are solely responsible for the accuracy of the content.

Bookmark:
Delicious Digg Newsvine Google Yahoo Reddit Magnoliacom Furl Facebook

Related Links

The paper “Machine learning to reveal nanoparticle dynamics from liquid-phase TEM videos” is available online and from the U. of I. News Bureau. DOI: 10.1021/acscentsci.0c00430:

Related News Press

Imaging

An artificial intelligence probe help see tumor malignancy July 1st, 2022

News and information

Two opposing approaches could give lithium-sulfur batteries a leg up over lithium-ion July 1st, 2022

Robot nose that can “smell” disease on your breath: Scientists develop diagnostic device for identifying compounds unique to particular diseases July 1st, 2022

Efficiently processing high-quality periodic nanostructures with ultrafast laser July 1st, 2022

Photonic synapses with low power consumption and high sensitivity are expected to integrate sensing-memory-preprocessing capabilities July 1st, 2022

New protocol for assessing the safety of nanomaterials July 1st, 2022

Nanofabrication

Efficiently processing high-quality periodic nanostructures with ultrafast laser July 1st, 2022

First integrated laser on lithium niobate chip: Research paves the way for high-powered telecommunication systems April 8th, 2022

Possible Futures

Technologies boost potential for carbon dioxide conversion to useful products: Researchers explore use metal-organic frameworks based catalysts for hydrogenation of carbon dioxide July 1st, 2022

Sieving carbons: Ideal anodes for high-energy sodium-ion batteries July 1st, 2022

An artificial intelligence probe help see tumor malignancy July 1st, 2022

Photon-controlled diode: an optoelectronic device with a new signal processing behavior July 1st, 2022

Nanomedicine

An artificial intelligence probe help see tumor malignancy July 1st, 2022

Robot nose that can “smell” disease on your breath: Scientists develop diagnostic device for identifying compounds unique to particular diseases July 1st, 2022

From outside to inside: A rapid and precise total assessment method for cells: Researchers at Nara Institute of Science and Technology show that using four frequencies of applied voltage can improve the measurement of cell size and shape during impedance cytometry, enabling to en June 24th, 2022

New technology helps reveal inner workings of human genome June 24th, 2022

Discoveries

Technologies boost potential for carbon dioxide conversion to useful products: Researchers explore use metal-organic frameworks based catalysts for hydrogenation of carbon dioxide July 1st, 2022

Sieving carbons: Ideal anodes for high-energy sodium-ion batteries July 1st, 2022

Efficiently processing high-quality periodic nanostructures with ultrafast laser July 1st, 2022

Photonic synapses with low power consumption and high sensitivity are expected to integrate sensing-memory-preprocessing capabilities July 1st, 2022

Materials/Metamaterials

New protocol for assessing the safety of nanomaterials July 1st, 2022

Nanotubes: a promising solution for advanced rubber cables with 60% less conductive filler June 1st, 2022

New route to build materials out of tiny particles May 27th, 2022

A one-stop shop for quantum sensing materials May 27th, 2022

Announcements

Two opposing approaches could give lithium-sulfur batteries a leg up over lithium-ion July 1st, 2022

Robot nose that can “smell” disease on your breath: Scientists develop diagnostic device for identifying compounds unique to particular diseases July 1st, 2022

Efficiently processing high-quality periodic nanostructures with ultrafast laser July 1st, 2022

Photonic synapses with low power consumption and high sensitivity are expected to integrate sensing-memory-preprocessing capabilities July 1st, 2022

Interviews/Book Reviews/Essays/Reports/Podcasts/Journals/White papers/Posters

Technologies boost potential for carbon dioxide conversion to useful products: Researchers explore use metal-organic frameworks based catalysts for hydrogenation of carbon dioxide July 1st, 2022

Sieving carbons: Ideal anodes for high-energy sodium-ion batteries July 1st, 2022

An artificial intelligence probe help see tumor malignancy July 1st, 2022

Photon-controlled diode: an optoelectronic device with a new signal processing behavior July 1st, 2022

Tools

New technology helps reveal inner workings of human genome June 24th, 2022

Snapshot measurement of single nanostructure’s circular dichroism March 25th, 2022

Eyebrow-raising: Researchers reveal why nanowires stick to each other February 11th, 2022

JEOL Introduces New Scanning Electron Microscope with “Simple SEM” Automation and Live Elemental and 3D Analysis January 14th, 2022

NanoNews-Digest
The latest news from around the world, FREE




  Premium Products
NanoNews-Custom
Only the news you want to read!
 Learn More
NanoStrategies
Full-service, expert consulting
 Learn More











ASP
Nanotechnology Now Featured Books




NNN

The Hunger Project