What question did this study set out to answer?

The primary aim is to leverage handheld LIBS and advanced machine learning for quick identification of battery materials.

March 5, 2026Journal of Analytical Atomic Spectrometry0 citations

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Rapid Identification of Spent Lithium-ion Batteries Black Powder Types Using Handheld LIBS and Interpretable MobileNet Models

NCNan CHENEast China Jiaotong University ZZZhaohua ZouEast China Jiaotong University ZLZixun LuoEast China Jiaotong University

Key Points

The primary aim is to leverage handheld LIBS and advanced machine learning for quick identification of battery materials.
Utilized handheld laser-induced breakdown spectroscopy for material analysis.
Employed a lightweight MobileNet deep learning model for classification.
Focused on classifying spent lithium-ion battery black powders.
Successfully identified different types of black powder rapidly.
Demonstrated high accuracy in classification using the combined techniques.

Abstract

This work demonstrates, for the first time, the direct application of handheld laser-induced breakdown spectroscopy (LIBS) combined with a lightweight deep learning model for the rapid classification of spent lithium-ion...

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Cite This Study

CHEN et al. (Thu,) studied this question.

synapsesocial.com/papers/69a91da8d6127c7a504c0b7b https://doi.org/https://doi.org/10.1039/d6ja00055j

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