Data-efficient crack quantification in lithium-ion cathodes using foundation model transfer

2026-08-27

AI Summary

研究使用自监督视觉变换编码器和轻量级可训练解码器解决锂离子阴极颗粒裂纹难以测量的问题，实现高效数据量化。

Abstract

Battery lifetime is central to sustainable electrification, yet the particle cracking that drives lithium-ion cathode aging is hard to measure: quantitative microscopy of this degradation is bottlenecked by annotation, because each destructive electron-microscopy cross-section spans hundreds of megapixels and pixel-level expert labelling requires hours per image. We show that a frozen self-supervised vision-transformer encoder, combined with a lightweight trainable decoder and iterative model-assisted annotation, turns this sparse labelling budget into population-scale degradation measurements. Applied to three 120-megapixel NMC cathode cross-sections representing initial, cycled-aged and calendar-aged states, the framework distinguishes intragranular cracks from early- and late-stage intergranular cracks and yields per-particle distributions of crack width, tortuosity and area fraction. Late intergranular crack coverage reaches 4.6% in the cycled sample versus 0.5% in the initial and calendar-aged samples, forming more tortuous, higher-coverage networks, consistent with degradation from repeated electrochemical cycling rather than elevated-temperature storage alone. A single destructive image yields the population-level statistics needed for lifetime-extending design, aging assessment and second-life decisions.

Problem

• lithium-ion cathode
• particle cracking
• quantitative microscopy
• annotation bottleneck
• electron-microscopy cross-section

Innovation

• self-supervised vision-transformer
• iterative model-assisted annotation
• crack quantification