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Characterization of Electrode Materials for Lithium Ion and Sodium Ion Batteries Using Synchrotron Radiation Techniques

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Analysis of Lithium Aging Using Machine Learning-Enhanced Spectroscopy Techniques.

James T Stofel¹, Ashwin P Rao², Anil K Patnaik¹

¹Department of Engineering Physics, Air Force Institute of Technology, Wright-Patterson AFB, Ohio, USA.

Applied Spectroscopy

|August 21, 2024

Summary

This summary is machine-generated.

This study uses laser-induced breakdown spectroscopy (LIBS) and Raman spectroscopy with machine learning to identify lithium compounds. The methods accurately classify lithium hydride, hydroxide, and carbonate, and quantify compound ingrowth.

Keywords:

LIBS‌Laser-induced breakdown spectroscopy Raman spectroscopy data fusion lithium lithium aging machine learning

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Area of Science:

Analytical Chemistry
Materials Science

Background:

Lithium compounds like LiH and LiOH are industrially important but reactive.
Reactions with H2O and CO2 lead to secondary compound ingrowth, affecting material homogeneity and applications.

Purpose of the Study:

To develop and validate spectroscopic methods for analyzing lithium compound mixtures.
To quantitatively assess the ingrowth of lithium hydroxide (LiOH) in lithium hydride (LiH).

Main Methods:

Utilized laser-induced breakdown spectroscopy (LIBS) and Raman spectroscopy for spectral data acquisition.
Employed machine learning, including support vector machine (SVM) classifiers, for high-fidelity classification of LiH, LiOH, and Li2CO3.
Applied multivariate regression techniques, specifically partial least-squares regression (PLSR), for quantitative analysis.

Main Results:

Achieved perfect prediction accuracy in classifying LiH, LiOH, and Li2CO3 using SVM classifiers.
Developed a data fusion model combining LIBS and Raman features using PLSR.
The optimized model demonstrated a root mean square error of 2.5 wt% and a detection limit of 6.3 wt% for LiOH ingrowth in LiH.

Conclusions:

LIBS and Raman spectroscopy, coupled with machine learning, provide a robust approach for analyzing lithium compound mixtures.
The developed methods enable accurate classification and quantification of secondary lithium compound formation.
This technique is crucial for ensuring the quality and performance of lithium chemicals in industrial applications.