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Machine learning uncovers how battery interphases can boost lithium-ion transport

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Ashley Piccone
Machine learning uncovers how battery interphases can boost lithium-ion transport
AI Summary

Researchers at Lawrence Livermore National Laboratory are using machine learning to analyze the complex atomic structures of battery interphases. This study aims to understand how these thin layers affect battery performance and longevity.

Why it matters

Improving battery interphase design could significantly increase the capacity and lifespan of lithium-ion batteries, accelerating the transition to renewable energy.

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by Ashley Piccone, Lawrence Livermore National Laboratory

This article has been reviewed according to Science X's editorial process and policies . Editors have highlighted the following attributes while ensuring the content's credibility:

Add as preferred source Interphases — the multicomponent interlayer structures that emerge between electrodes and electrolytes in batteries — critically determine the performance and durability of electrochemical cells. Credit: Sabrina Wan Sandwiched between the electrolyte and electrodes in a lithium-ion battery is a remarkably thin layer of material that strongly dictates battery performance and durability: the interphase. Although typically only a few to tens of nanometers thick, interphases are among the least understood components of an operating battery cell. Their complex and constantly evolving structures make it challenging to determine how their atomic features and microscopic variations translate into macroscopic battery performance.

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