Machine learning fine-tunes flash graphene
Computer models used to advance environmentally friendly process

Date:
January 31, 2022
Source:
Rice University
Summary:
Scientists are using machine learning techniques to streamline
the process of synthesizing graphene from waste through flash
Joule heating.



FULL STORY ==========================================================================
Rice University scientists are using machine-learning techniques to
streamline the process of synthesizing graphene from waste through flash
Joule heating.


==========================================================================
The process discovered two years ago by the Rice lab of chemist James
Tour has expanded beyond making graphene from various carbon sources
to extracting other materials like metals from urban waste, with the
promise of more environmentally friendly recycling to come.

The technique is the same for all of the above: blasting a jolt of high
energy through the source material to eliminate all but the desired
product. But the details for flashing each feedstock are different.

The researchers describe inAdvanced Materialshow machine-learning models
that adapt to variables and show them how to optimize procedures are
helping them push forward.

"Machine-learning algorithms will be critical to making the flash process
rapid and scalable without negatively affecting the graphene product's properties," Tour said.

"In the coming years, the flash parameters can vary depending on the
feedstock, whether it's petroleum-based, coal, plastic, household waste
or anything else," he said. "Depending on the type of graphene we want
-- small flake, large flake, high turbostratic, level of purity --
the machine can discern by itself what parameters to change." Because
flashing makes graphene in hundreds of milliseconds, it's difficult to
tease out the details of the chemical process. So Tour and company took
a clue from materials scientists who have worked machine learning into
their everyday process of discovery.



==========================================================================
"It turned out that machine learning and flash Joule heating had
really good synergy," said Rice graduate student and lead author Jacob
Beckham. "Flash Joule heating is a really powerful technique, but it's difficult to control some of the variables involved, like the rate of
current discharge during a reaction. And that's where machine learning
can really shine. It's a great tool for finding relationships between
multiple variables, even when it's impossible to do a complete search
of the parameter space.

"That synergy made it possible to synthesize graphene from scrap material
based entirely on the models' understanding of the Joule heating process,"
he said.

"All we had to do was carry out the reaction -- which can eventually
be automated." The lab used its custom optimization model to improve
graphene crystallization from four starting materials -- carbon black,
plastic pyrolysis ash, pyrolyzed rubber tires and coke -- over 173
trials, using Raman spectroscopy to characterize the starting materials
and graphene products.

The researchers then fed more than 20,000 spectroscopy results to the
model and asked it to predict which starting materials would provide
the best yield of graphene. The model also took the effects of charge
density, sample mass and material type into account in their calculations.

Co-authors are Rice graduate students Kevin Wyss, Emily McHugh,
Paul Advincula and Weiyin Chen; Rice alumnus John Li; and postdoctoral researcher Yunchao Xie and Jian Lin, an associate professor of mechanical
and aerospace engineering, of the University of Missouri. Tour is the
T.T. and W.F. Chao Chair in Chemistry as well as a professor of computer science and of materials science and nanoengineering.

The Air Force Office of Scientific Research (FA9550-19- 1-0296), U.S. Army Corps of Engineers (W912HZ-21-2-0050) and the Department of Energy (DE- FE0031794) supported the research.

========================================================================== Story Source: Materials provided by Rice_University. Original written
by Mike Williams. Note: Content may be edited for style and length.


========================================================================== Journal Reference:
1. Jacob L. Beckham, Kevin M. Wyss, Yunchao Xie, Emily A. McHugh, John
Tianci Li, Paul A. Advincula, Weiyin Chen, Jian Lin, James M. Tour.

Machine Learning Guided Synthesis of Flash Graphene. Advanced
Materials, 2022; 2106506 DOI: 10.1002/adma.202106506 ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2022/01/220131132811.htm

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