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

News and information

First human trial shows ‘wonder’ material can be developed safely: A revolutionary nanomaterial with huge potential to tackle multiple global challenges could be developed further without acute risk to human health, research suggests February 16th, 2024

Detecting breast cancer through a spit test February 16th, 2024

New chip opens door to AI computing at light speed February 16th, 2024

HKUST researchers develop new integration technique for efficient coupling of III-V and silicon February 16th, 2024

Under pressure - space exploration in our time: Advancing space exploration through diverse collaborations and ethical policies February 16th, 2024

Nanofabrication

New chip opens door to AI computing at light speed February 16th, 2024

Imaging

First direct imaging of small noble gas clusters at room temperature: Novel opportunities in quantum technology and condensed matter physics opened by noble gas atoms confined between graphene layers January 12th, 2024

The USTC realizes In situ electron paramagnetic resonance spectroscopy using single nanodiamond sensors November 3rd, 2023

Observation of left and right at nanoscale with optical force October 6th, 2023

Previously unknown pathway to batteries with high energy, low cost and long life: Newly discovered reaction mechanism overcomes rapid performance decline in lithium-sulfur batteries September 8th, 2023

Possible Futures

First human trial shows ‘wonder’ material can be developed safely: A revolutionary nanomaterial with huge potential to tackle multiple global challenges could be developed further without acute risk to human health, research suggests February 16th, 2024

Detecting breast cancer through a spit test February 16th, 2024

A battery’s hopping ions remember where they’ve been: Seen in atomic detail, the seemingly smooth flow of ions through a battery’s electrolyte is surprisingly complicated February 16th, 2024

NRL discovers two-dimensional waveguides February 16th, 2024

Nanomedicine

Detecting breast cancer through a spit test February 16th, 2024

Superbug killer: New synthetic molecule highly effective against drug-resistant bacteria February 16th, 2024

Researchers develop technique to synthesize water-soluble alloy nanoclusters January 12th, 2024

Focused ion beam technology: A single tool for a wide range of applications January 12th, 2024

Discoveries

HKUST researchers develop new integration technique for efficient coupling of III-V and silicon February 16th, 2024

Electrons screen against conductivity-killer in organic semiconductors: The discovery is the first step towards creating effective organic semiconductors, which use significantly less water and energy, and produce far less waste than their inorganic counterparts February 16th, 2024

Superbug killer: New synthetic molecule highly effective against drug-resistant bacteria February 16th, 2024

Discovery of new Li ion conductor unlocks new direction for sustainable batteries: University of Liverpool researchers have discovered a new solid material that rapidly conducts lithium ions February 16th, 2024

Materials/Metamaterials/Magnetoresistance

Focused ion beam technology: A single tool for a wide range of applications January 12th, 2024

Catalytic combo converts CO2 to solid carbon nanofibers: Tandem electrocatalytic-thermocatalytic conversion could help offset emissions of potent greenhouse gas by locking carbon away in a useful material January 12th, 2024

2D material reshapes 3D electronics for AI hardware December 8th, 2023

Finding the most heat-resistant substances ever made: UVA Engineering secures DOD MURI award to advance high-temperature materials December 8th, 2023

Announcements

Detecting breast cancer through a spit test February 16th, 2024

New chip opens door to AI computing at light speed February 16th, 2024

HKUST researchers develop new integration technique for efficient coupling of III-V and silicon February 16th, 2024

Electrons screen against conductivity-killer in organic semiconductors: The discovery is the first step towards creating effective organic semiconductors, which use significantly less water and energy, and produce far less waste than their inorganic counterparts February 16th, 2024

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

First human trial shows ‘wonder’ material can be developed safely: A revolutionary nanomaterial with huge potential to tackle multiple global challenges could be developed further without acute risk to human health, research suggests February 16th, 2024

Detecting breast cancer through a spit test February 16th, 2024

New chip opens door to AI computing at light speed February 16th, 2024

HKUST researchers develop new integration technique for efficient coupling of III-V and silicon February 16th, 2024

Tools

First direct imaging of small noble gas clusters at room temperature: Novel opportunities in quantum technology and condensed matter physics opened by noble gas atoms confined between graphene layers January 12th, 2024

New laser setup probes metamaterial structures with ultrafast pulses: The technique could speed up the development of acoustic lenses, impact-resistant films, and other futuristic materials November 17th, 2023

Ferroelectrically modulate the Fermi level of graphene oxide to enhance SERS response November 3rd, 2023

The USTC realizes In situ electron paramagnetic resonance spectroscopy using single nanodiamond sensors November 3rd, 2023

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