Forum for Science, Industry and Business

Sponsored by:     3M 
Search our Site:

 

Capturing 3D microstructures in real time

03.04.2020

Modern scientific research on materials relies heavily on exploring their behavior at the atomic and molecular scales. For that reason, scientists are constantly on the hunt for new and improved methods for data gathering and analysis of materials at those scales.

Researchers at the Center for Nanoscale Materials (CNM), a U.S. Department of Energy (DOE) Office of Science User Facility located at the DOE's Argonne National Laboratory, have invented a machine-learning based algorithm for quantitatively characterizing, in three dimensions, materials with features as small as nanometers. Researchers can apply this pivotal discovery to the analysis of most structural materials of interest to industry.


Machine-learning enabled characterization of 3D microstructure showing grains of different sizes and their boundaries.

Credit: Argonne National Laboratory

"What makes our algorithm unique is that if you start with a material for which you know essentially nothing about the microstructure, it will, within seconds, tell the user the exact microstructure in all three dimensions," said Subramanian Sankaranarayanan, group leader of the CNM theory and modeling group and an associate professor in the Department of Mechanical and Industrial Engineering at the University of Illinois at Chicago.

"For example, with data analyzed by our 3D tool," said Henry Chan, CNM postdoctoral researcher and lead author of the study, "users can detect faults and cracks and potentially predict the lifetimes under different stresses and strains for all kinds of structural materials."

"What makes our algorithm unique is that if you start with a material for which you know essentially nothing about the microstructure, it will, within seconds, tell the user the exact microstructure in all three dimensions." -- Subramanian Sankaranarayanan, CNM group leader and associate professor at the University of Illinois at Chicago

Most structural materials are polycrystalline, meaning a sample used for purposes of analysis can contain millions of grains.

The size and distribution of those grains and the voids within a sample are critical microstructural features that affect important physical, mechanical, optical, chemical and thermal properties. Such knowledge is important, for example, to the discovery of new materials with desired properties, such as stronger and harder machine components that last longer.

In the past, scientists have visualized 3D microstructural features within a material by taking snapshots at the microscale of many 2D slices, processing the individual slices, and then pasting them together to form a 3D picture.

Such is the case, for example, with the computerized tomography scanning routine done in hospitals. That process, however, is inefficient and leads to the loss of information. Researchers have thus been searching for better methods for 3D analyses.

"At first," said Mathew Cherukara, an assistant scientist at CNM, "we thought of designing an intercept-based algorithm to search for all the boundaries among the numerous grains in the sample until mapping the entire microstructure in all three dimensions, but as you can imagine, with millions of grains, that is extraordinarily time-consuming and inefficient."

"The beauty of our machine learning algorithm is that it uses an unsupervised algorithm to handle the boundary problem and produce highly accurate results with high efficiency," said Chan. "Coupled with down-sampling techniques, it only takes seconds to process large 3D samples and obtain precise microstructural information that is robust and resilient to noise."

The team successfully tested the algorithm by comparison with data obtained from analyses of several different metals (aluminum, iron, silicon and titanium) and soft materials (polymers and micelles).

These data came from earlier published experiments as well as computer simulations run at two DOE Office of Science User Facilities, the Argonne Leadership Computing Facility and the National Energy Research Scientific Computing Center. Also used in this research were the Laboratory Computing Resource Center at Argonne and the Carbon Cluster in CNM.

"For researchers using our tool, the main advantage is not just the impressive 3D image generated but, more importantly, the detailed characterization data," said Sankaranarayanan. "They can even quantitatively and visually track the evolution of a microstructure as it changes in real time."

The machine-learning algorithm is not restricted to solids. The team has extended it to include characterization of the distribution of molecular clusters in fluids with important energy, chemical and biological applications.

This machine-learning tool should prove especially impactful for future real-time analysis of data obtained from large materials characterization facilities, such as the Advanced Photon Source, another DOE Office of Science User Facility at Argonne, and other synchrotrons around the world.

###

This study, titled "Machine learning enabled autonomous microstructural characterization in 3D samples," appeared in npj Computational Materials. In addition to Sankaranarayanan and Chan, authors include Mathew Cherukara, Troy D. Loeffler, and Badri Narayanan. This study received funding from the DOE Office of Basic Energy Sciences.

About Argonne's Center for Nanoscale Materials
The Center for Nanoscale Materials is one of the five DOE Nanoscale Science Research Centers, premier national user facilities for interdisciplinary research at the nanoscale supported by the DOE Office of Science. Together the NSRCs comprise a suite of complementary facilities that provide researchers with state-of-the-art capabilities to fabricate, process, characterize and model nanoscale materials, and constitute the largest infrastructure investment of the National Nanotechnology Initiative. The NSRCs are located at DOE's Argonne, Brookhaven, Lawrence Berkeley, Oak Ridge, Sandia and Los Alamos National Laboratories. For more information about the DOE NSRCs, please visit https://science.osti.gov/User-Facilities/User-Facilities-at-a-Glance.

Argonne National Laboratory seeks solutions to pressing national problems in science and technology. The nation's first national laboratory, Argonne conducts leading-edge basic and applied scientific research in virtually every scientific discipline. Argonne researchers work closely with researchers from hundreds of companies, universities, and federal, state and municipal agencies to help them solve their specific problems, advance America's scientific leadership and prepare the nation for a better future. With employees from more than 60 nations, Argonne is managed by UChicago Argonne, LLC for the U.S. Department of Energy's Office of Science.

The U.S. Department of Energy's Office of Science is the single largest supporter of basic research in the physical sciences in the United States and is working to address some of the most pressing challenges of our time. For more information, visit https://energy.gov/science.

Media Contact

Diana Anderson
ddanderson@anl.gov
630-252-4593

 @argonne

http://www.anl.gov 

Diana Anderson | EurekAlert!
Further information:
https://www.anl.gov/article/capturing-3d-microstructures-in-real-time
http://dx.doi.org/10.1038/s41524-019-0267-z

More articles from Materials Sciences:

nachricht Oriented hexagonal boron nitride foster new type of information carrier
25.05.2020 | Japan Advanced Institute of Science and Technology

nachricht A replaceable, more efficient filter for N95 masks
22.05.2020 | American Chemical Society

All articles from Materials Sciences >>>

The most recent press releases about innovation >>>

Die letzten 5 Focus-News des innovations-reports im Überblick:

Im Focus: I-call - When microimplants communicate with each other / Innovation driver digitization - "Smart Health“

Microelectronics as a key technology enables numerous innovations in the field of intelligent medical technology. The Fraunhofer Institute for Biomedical Engineering IBMT coordinates the BMBF cooperative project "I-call" realizing the first electronic system for ultrasound-based, safe and interference-resistant data transmission between implants in the human body.

When microelectronic systems are used for medical applications, they have to meet high requirements in terms of biocompatibility, reliability, energy...

Im Focus: When predictions of theoretical chemists become reality

Thomas Heine, Professor of Theoretical Chemistry at TU Dresden, together with his team, first predicted a topological 2D polymer in 2019. Only one year later, an international team led by Italian researchers was able to synthesize these materials and experimentally prove their topological properties. For the renowned journal Nature Materials, this was the occasion to invite Thomas Heine to a News and Views article, which was published this week. Under the title "Making 2D Topological Polymers a reality" Prof. Heine describes how his theory became a reality.

Ultrathin materials are extremely interesting as building blocks for next generation nano electronic devices, as it is much easier to make circuits and other...

Im Focus: Rolling into the deep

Scientists took a leukocyte as the blueprint and developed a microrobot that has the size, shape and moving capabilities of a white blood cell. Simulating a blood vessel in a laboratory setting, they succeeded in magnetically navigating the ball-shaped microroller through this dynamic and dense environment. The drug-delivery vehicle withstood the simulated blood flow, pushing the developments in targeted drug delivery a step further: inside the body, there is no better access route to all tissues and organs than the circulatory system. A robot that could actually travel through this finely woven web would revolutionize the minimally-invasive treatment of illnesses.

A team of scientists from the Max Planck Institute for Intelligent Systems (MPI-IS) in Stuttgart invented a tiny microrobot that resembles a white blood cell...

Im Focus: NASA's Curiosity rover finds clues to chilly ancient Mars buried in rocks

By studying the chemical elements on Mars today -- including carbon and oxygen -- scientists can work backwards to piece together the history of a planet that once had the conditions necessary to support life.

Weaving this story, element by element, from roughly 140 million miles (225 million kilometers) away is a painstaking process. But scientists aren't the type...

Im Focus: Making quantum 'waves' in ultrathin materials

Study co-led by Berkeley Lab reveals how wavelike plasmons could power up a new class of sensing and photochemical technologies at the nanoscale

Wavelike, collective oscillations of electrons known as "plasmons" are very important for determining the optical and electronic properties of metals.

All Focus news of the innovation-report >>>

Anzeige

Anzeige

VideoLinks
Industry & Economy
Event News

Dresden Nexus Conference 2020: Same Time, Virtual Format, Registration Opened

19.05.2020 | Event News

Aachen Machine Tool Colloquium AWK'21 will take place on June 10 and 11, 2021

07.04.2020 | Event News

International Coral Reef Symposium in Bremen Postponed by a Year

06.04.2020 | Event News

 
Latest News

Inexpensive retinal diagnostics via smartphone

25.05.2020 | Medical Engineering

Smart machine maintenance: New AI system also detects unknown faults

25.05.2020 | Information Technology

Artificial Intelligence for optimized mobile communication

25.05.2020 | Information Technology

VideoLinks
Science & Research
Overview of more VideoLinks >>>