It lets researchers extract pixel-by-pixel information from nanoscale X-ray movies of electrode particles absorbing and releasing lithium ions. Billions of tiny particles packed into rechargeable lithium-ion battery electrodes are responsible for storing charge and making it available when it’s n
A team from SLAC, Stanford, MIT, and Toyota Research Institute used machine learning to re-analyze X-ray movies of lithium ions flowing in and out of battery electrode nanoparticles during battery cycling. The false colors in this image show the charge status of each particle and reveal how uneven the process within a single particle can be. Credit: Cube3DX-ray movies of electrode particles absorbing and releasing lithium ions.
The new method has already suggested a way to make the billions of nanoparticles in one type of lithium-ion battery electrode store and release charge more efficiently, researchers from the Department of Energy’s SLAC National Accelerator Laboratory, Stanford University, the Massachusetts Institute of Technology, and Toyota Research Institute reported in“Until now, we could make these beautiful X-ray movies of battery nanoparticles at work, but the movies were so information-rich that...
To watch what’s happening inside the battery while it operates, Chueh’s team builds tiny, transparent cell batteries in which two electrodes are surrounded by an electrolyte solution full of free-moving lithium ions. A team from SLAC, Stanford, MIT, and Toyota Research Institute used machine learning to re-analyze X-ray movies like this one pixel by pixel and discover new physical and chemical details of battery cycling. This animation is based on X-ray images the team made in 2016. It shows some of the billions of nanoparticles in a lithium-ion battery electrode charging and discharging as lithium ions flow in and out of them, and reveals how uneven the process within a single particle can be.
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