Scientists track how particles detach in batteries’ cathodes


An international team of scientists has found how particles in cathodes detach depending on the charge of a battery, using X-ray tomography at the ESRF and SLAC and machine-learning-assisted analysis. They publish their results in Nature Communications.

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Lithium-ion batteries lose their juice over time, but scientists have struggled to understand that process in detail. Now, scientists led by the SLAC National Accelerator Laboratory in the US have combined X-ray tomography data and sophisticated machine learning algorithms to produce one of the first detailed pictures of how one battery component, the cathode, degrades with use.

The study focuses on cathodes made of nickel-manganese-cobalt (NMC). In these cathodes, NMC particles are held together by a conductive carbon matrix, and researchers have speculated that one cause of performance decline could be particles breaking away from that matrix. The team’s goal was to develop a comprehensive picture of how NMC particles break apart and break away from the matrix and how that might contribute to performance losses. In order to reveal the 3D microstructure and composition with high spatial resolution down to the nanoscale, they employed the hard X-ray phase contrast nano-tomography technique at the beamline ID16A-NI at the European Synchrotron. They complemented these results with studies at SLAC’s Stanford Synchrotron Radiation Lightsource (SSRL).

Using phase contrast, the team managed to visualize the active particles, or carbon/binder CBD, and pore structures in a charged composite cathode. “This experiment at the ESRF was challenging”, explains ESRF scientist Yang Yang. “Because until now, the only way to observe the electrode particles together with their carbon binder, is to use electron microscopes, which will be destructive and yet very limited in statistics. The unique setup of the high-energy X-ray nanoprobe at ID16A, can visualize hundreds of particles within their carbon matric in the electrode, allowing an unprecedented complete view”, she adds.

Being it a tall order for humans to figure out what’s going on just by looking at pictures of an NMC cathode, the scientists turned to computer vision, a subfield of machine learning algorithms originally designed to scan images or videos and identify and track objects.

The team used a type of algorithm set up to deal with hierarchical objects. With input and judgments from the researchers themselves, they trained this algorithm to distinguish different kinds of particles and therefore develop a picture of how NMC particles, whether large or small, fractured or not, break away from the cathode in 3D.

They discovered that particles detaching from the carbon matrix really do contribute significantly to a battery’s decline, at least under conditions one would typically see in consumer electronics, such as smart phones.

In addition, “while large NMC particles are more likely to become damaged and break away, quite a few smaller particles break away, too, and overall, there’s more variation in the way small particles behave”, explains Yijin Liu, a staff scientist at SLAC and a senior author of the new paper. “That is important because researchers had generally assumed that by making battery particles smaller, they could make longer-lasting batteries – something our study suggests might not be so straightforward”, Liu adds.


Zhisen Jiang, et al., Nature Communications volume 11, Article number: 2310 (2020).

Top image: The 3D rendering of an electrode with a monolayer of NMC particles. Credits: Zhisen J et al, Nature Communications, 8 May 2020.