Scientists apply ancient technique to better understand batteries
Lithium batteries are everywhere, so why don't they work better?
Lithium batteries power much of our modern lives, from cellphones and laptops to electric vehicles. But despite their widespread use, these batteries still suffer from internal degradation problems that only worsen as they age and lead to a loss of charge and effectiveness. To better understand how these batteries lose their juice, scientists have looked to ancient scrolls and high-tech imaging for inspiration.
In the study, published Friday in the journal Nature Communications, a team of researchers from Helmholtz-Zentrum Berlin (HZB) and University College London looked at a typical Duracell battery using both X-ray and neutron tomography. The authors write that these two imaging approaches, which look at thin slices of material, are typically used separately but that combining them together gave them the opportunity to view aspects of the battery that either approach missed on its own. X-ray tomography, for example, is better at looking at the battery's microstructure and deformations. While neutron tomography, one of the study's co-authors and tomography expert at HZB, Ingo Manke, said in a statement, is better for looking at changes in the lithium itself.
"Neutron tomography, on the other hand, made it possible to directly observe the migration of lithium ions and also to determine how the distribution of the electrolyte in the battery cell changes over time," said Manke.
The authors write that these complementary approaches lent themselves to a 4D imaging of the battery -- in which the fourth dimension is time, not scent or water effects at 4D movies might have you believe. In order to get a better look inside these tightly wound batteries using their tomographical approaches, the authors also adapted an algorithm originally designed for unrolling ancient scrolls.
"The algorithm was originally meant for virtually unrolling papyrus scrolls," said Manke. "But it can also be used to find out exactly what happens in compact densely wound batteries."
The yellow arrow points to plays where neutrons detected 'dry' regions where the elecrolyte is lacking. The blue arrow shows areas with a deficiency of lithium.
After virtually rolling out these batteries and investigating their internal properties via tomography, the team was able to determine some key points of weakness lurking inside the curves of the battery. In particular, the team noticed that the battery's inner windings showed less electrochemical activity (thus, lithium capacity) than the outer windings of the battery. They also observed "dry patches" throughout the battery where electrolyte appeared to be missing. Because electrolytes serve as a kind of river to allow for the flow of lithium ions, dry patches would negatively affect the flow of this charge.
While the study of these batteries didn't offer scientists any immediate answers as to how to fix these problems in lithium batteries, Manke says it gives them another effective way to better understand these batteries and hopefully develop better designs in the future.
"The process we have developed gives us a unique tool for looking inside a battery during operation and analysing where and why performance losses occur. This allows us to develop specific strategies for improving the design of wound batteries," said Manke.
Abstract: The temporally and spatially resolved tracking of lithium intercalation and electrode degradation processes are crucial for detecting and understanding performance losses during the operation of lithium-batteries. Here, high-throughput X-ray computed tomography has enabled the identification of mechanical degradation processes in a commercial Li/MnO2 primary battery and the indirect tracking of lithium diffusion; furthermore, complementary neutron computed tomography has identified the direct lithium diffusion process and the electrode wetting by the electrolyte. Virtual electrode unrolling techniques provide a deeper view inside the electrode layers and are used to detect minor fluctuations which are difficult to observe using conventional three dimensional rendering tools. Moreover, the ‘unrolling’ provides a platform for correlating multi-modal image data which is expected to find wider application in battery science and engineering to study diverse effects e.g. electrode degradation or lithium diffusion blocking during battery cycling.
