New characterization techniques developed at the Catalysis Center for Energy Innovation may help improve electrochemical storage technologies, such as fuels cells used in UD’s hydrogen fuel cell buses. Photo by Jon Cox and courtesy of Josh Lansford.
However, widespread adoption of renewable energy resources from solar, wind,
biomass and more have lagged, in part because they are difficult to store and
transport.
As the search for materials to efficiently address these storage and
transport needs continues, University of Delaware researchers from the
Catalysis Center for Energy Innovation (CCEI) report new techniques for
characterizing complex materials with the potential to overcome these
challenges.
The researchers recently reported their technique in Nature
Communications.
Seeing the parts, as well as the whole
Currently technologies exist for characterizing highly ordered surfaces
with specific repeating patterns, such as crystals. Describing surfaces with no
repeating pattern is a harder problem.
UD doctoral candidate and 2019-2020 Blue Waters Graduate Fellow Josh
Lansford and Dion Vlachos, who directs both CCEI and the Delaware Energy
Institute and is the Allan and Myra Ferguson Professor of Chemical and
Biomolecular Engineering, have developed a method to observe the local surface
structure of atomic-scale particles in detail while simultaneously keeping the
entire system in view.
The approach, which leverages machine learning, data science techniques
and models grounded in physics, enables the researchers to visualize the actual
three-dimensional structure of a material they are interested in up close, but
also in context. This means they can study specific particles on the material’s
surface, but also watch how the particle’s structure evolves — over time — in
the presence of other molecules and under different conditions, such as
temperature and pressure.
Put to use, the research team’s technique will help engineers and scientists identify materials that can improve storage technologies, such as fuel cells and batteries, which power our lives. Such improvements are necessary to help these important technologies reach their full potential and become more widespread.
: UD doctoral candidate Josh Lansford is the lead author of a paper in Nature Communications describing a new technique for characterizing complex materials.
“In order to optimize electrochemical storage technologies, such as fuel
cells and batteries, we must understand how they work and what they look like,â€
said Lansford, the paper’s lead author, who is advised at UD by Vlachos, the
project’s principal investigator.
“We need to understand the structure of the materials we are generating,
in detail, so that we can recreate them efficiently at a large scale or modify
them to alter their stability.â€
Computational modeling
Lansford concedes that it is too costly and time-consuming to model
complex structures directly. Instead, they take data, generated from a single
spot on the surface of a material, and scale it to be representative for a
variety of catalysts on many surfaces of many different materials.
Imagine a cube made up of many atoms. The atoms located on the corners
of the cube will have different properties than, say, the atoms located on one
side of the cube. This is because on the corners, fewer atoms will be connected
to each other and atoms may be spaced closer together. While on the side of the
cube, more atoms will be connected even though they may be spaced farther apart
from each other.
The same is true for catalyst materials. Even if we can’t see them with
the naked eye, the particles that make up a catalyst are adsorbed onto many
different sites on the material — and these sites have different edges, bumps
and other variations that affect how materials located there will behave.
Because of these differences, scientists can’t just use a single number to try
to quantify what’s happening across a material’s entire surface, so they have
to estimate what these surfaces look like.
According to Lansford, this is where computational modeling can help.
The research team used experimental measurements of different
wavelengths of infrared light and machine learning to predict and describe the
chemical and physical properties of different surfaces of materials. The models
were trained entirely on mathematically generated data, allowing them to
visualize many different options under many different conditions.
They developed special open-source software to apply the technique on
different metals, materials and adsorbates. The methodology is flexible enough
to be used with other spectroscopic techniques beyond infrared light, so that
other scientists and engineers can modify the software to advance their own
work.
“This work introduces an entirely new way of thinking on how to bridge
the gap between real-world materials and well-defined model systems, with contributions
to surface science and machine learning that stand on their own," said
Lansford.