Machine Learning based Analytical Framework for Automatic Hyperspectral Raman Analysis of Lithium-ion Battery...

Area of Science:

• Materials Science
• Analytical Chemistry
• Spectroscopy

Background:

• Synchronous identification of multiple spectral signatures in lithium-ion battery (LIB) electrodes is crucial for inline quality control and product development.
• Current analytical techniques often require significant human intervention, limiting real-time applications.

Purpose of the Study:

• To develop an automated analytical framework (AF) for identifying spectral signatures in hyperspectral Raman datasets of LIB electrodes.
• To enable real-time quality control and capacity degradation assessment in LIB manufacturing and research.

Main Methods:

• An end-to-end pipeline involving intelligent data pre-processing (noise and baseline elimination).
• Automated extraction of reliable spectral signatures and assignment of class labels.
• Training and validation of a neural network (NN) for interoperability and reusability on new datasets.

Main Results:

• The AF successfully automates the identification of spectral signatures in LIB electrode hyperspectral Raman data.
• Demonstrated quantitative assessment of LIB capacity degradation using a capacity retention coefficient derived from extracted LMO signatures.
• Validated the reusability of trained NNs for inline, real-time analytics across different LIB specimens.

Conclusions:

• The developed analytical framework offers a fully automated solution for spectral signature identification in LIB electrodes.
• This approach facilitates real-time vibrational spectroscopy applications, including quality control, product development, and degradation monitoring.
• The AF is adaptable for various industrial applications involving complex spectral data analysis.