07-10, 14:20–14:50 (US/Pacific), Room 315
This track highlights the fantastic scientific applications that the
SciPy community creates with the tools we collectively make. Talk
proposals to this track should be stories of how using the Scientific
Python ecosystem the speakers were able to overcome challenges, create
new collaborations, reduce the time to scientific insight, and share
their results in ways not previously possible. Proposals should focus
on novel applications and problems, and be of broad interest to the
conference, but should not shy away from explaining the scientific
nuances that make the story in the proposal exciting.
Introduction and Background
Batteries are ubiquitous in modern life, from enabling long-lasting
mobile devices to stabilizing the electric grid. In many ways it is
amazing they exist at all, since stored chemical energy will always
tend towards equilibrium (i.e. a dead battery). Improving the
charge-storage capabilities of modern batteries requires continued
research to understand the chemistry driving the charge storage
mechanisms.
Batteries form a hierarchical system: the overall performance of a
given cell depends on the behavior at smaller scales. For example,
a typical Li-ion battery cathode contains countless ~10
micrometer-sized secondary particles whose behavior is driven by
the individual ~100 nm primary particles of which they are
composed. These primary particles, in turn, depend on the behavior
of their individual atom constituents. As a result, the progress of
battery technology depends on improving our understanding of the
chemical processes that are dominant at each of these
length-scales, as well as how they relate to one another[1].
Methods
X-rays are a versatile probe of materials chemistry that is
well-suited to battery research. For example, X-rays can be used to
form a three-dimensional representation of the inside of a battery,
similar to a medical CT scan. Furthermore, X-ray spectroscopy
measures the amount of light transmitted based on its wavelength,
revealing element-specific states of charge. A battery can then be
charged while repeatedly performing spectroscopy and/or imaging
measurements to reveal the structural and chemical properties as a
function of the state of charge of the battery, allowing us to see
the chemistry taking place within.
For one battery cell, this produces a data-set with four to five
unique axes, some of which may even be complex-valued. To be
useful, these data must to be cleaned, aligned, analyzed, and
visualized using tools such as scipy and scikit-image. The
individual pixel-wise spectra must them be converted to a
scientifically meaningful number, requiring more specific
tools. This must be done in a way that is reliable, and
reproducible to ensure the scientific conclusions are properly
supported. The xanespy package combine these
python packages in ways suited for analyzing these types of
data[2].
Results
The python ecosystem is well-suited for these analysis tasks,
producing maps showing the oxidation states of individual particles
within a battery cathode over time[3]. These maps revealed that
individual secondary particles did not follow the predictions of
thermodynamics. Instead, each particle underwent an initial latent
period followed by rapid oxidation to its fully charged
state. Physics simulations showed that the best explanation for
this behavior is a change in the kinetics of how lithium leaves the
cathode and enters the electrolyte at the surface of the battery
particles.
Additional coherent imaging (ptychography) experiments probed down
to the level of individual particles ~100nm in diameter[4]. After
extracting spectral signals using a Bayesian optimization, we
observed gradients in the state of charge for these particles, from
the surface up to several tens of nanometers into the particle,
with the outer atomic layers showing discharged battery material
even when the battery was fully charged. Taken together, these
behaviors translate into inefficient energy storage in the battery.
Conclusions
The success of these experiments shows the versatility of the
scientific python ecosystem for handling these otherwise unwieldy
data sets. Alternative tools are either designed with a specific
data structure in mind and are therefore not adaptable to novel
experiments, or else generic and so are not tailored to these kinds
of analyses.
Synchrotron radiation sources are continuing to improve, providing
faster and more precise measurements, resulting in ever-growing
data-sets. New tools are now being developed that will allow us to
both execute these experiments effectively and access the resulting
data in an efficient manner.
References
[1] https://doi.org/10.1021/acs.chemmater.6b05114
[2] https://github.com/canismarko/xanespy
Mark is a beamline scientist in the spectroscopy group at the Advanced Photon Source, collaborating closely with visiting researchers to execute cutting edge scientific experiments across a variety of disciplines. His research background emphasizes in-situ measurements, where chemical states are measured inside an operating battery in order to better understand the otherwise inaccessible dynamic processes. Combining high-resolution imaging, spectroscopy, and diffraction provides insights into the local interactions that drive the energy storage in cutting edge battery technologies. This research provides a foundation for future technological development that will deliver faster, more efficient, and safer energy storage solutions.
Mark completed his PhD in the chemistry department at the University of Illinois Chicago with Jordi Cabana, where he studied particle-level dynamics for layered cathodes. During this time, Mark spent a year as a visiting graduate student at the Advanced Photon Source through the U.S. Department of Energy’s Science Graduate Research Program. The result of this collaboration was a new cell for three-dimensional imaging of operating Li-ion batteries. He built upon his graduate research as a postdoctoral appointee in Interfacial Processes group working closely with Tim Fister to include additional 3D imaging in working cells, and high-temperature preparation of cutting edge battery materials. Throughout his work, Mark has written many scientific software packages to aid in data analysis and visualization.