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Quantitative Analysis of Vacuum Induction Melting by Laser-induced Breakdown Spectroscopy

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Published on: June 10, 2019

Laser-Induced Breakdown Spectroscopy Analysis of Lithium: A Comprehensive Review.

Stefano Legnaioli¹, Giulia Lorenzetti¹, Francesco Poggialini¹

¹Institute of Chemistry of Organometallic Compounds, National Research Council, 56124 Pisa, Italy.

Sensors (Basel, Switzerland)

|December 31, 2025

Summary

This summary is machine-generated.

Laser-Induced Breakdown Spectroscopy (LIBS) offers a sustainable and accurate method for lithium analysis across its value chain. This technique supports the energy transition by enabling real-time monitoring and optimizing lithium extraction, refining, and recycling.

Keywords:

LIBS circular economy lithium lithium-ion batteries recycling

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Dependence of Laser-induced Breakdown Spectroscopy Results on Pulse Energies and Timing Parameters Using Soil Simulants

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Area of Science:

Analytical Chemistry
Materials Science
Geochemistry

Background:

Lithium is critical for the global energy transition, but supply is constrained by limited resources and increasing demand.
Accurate, sustainable, and versatile analytical methods are essential for the entire lithium value chain.

Purpose of the Study:

To review recent advancements in using Laser-Induced Breakdown Spectroscopy (LIBS) for lithium detection and quantification.
To explore LIBS applications in diverse matrices and emerging recycling processes.
To highlight future prospects of integrating LIBS with AI/ML for enhanced analysis.

Main Methods:

Review of recent scientific literature on LIBS applications for lithium analysis.
Examination of LIBS capabilities in geological, industrial, biological, and extraterrestrial samples.
Discussion of LIBS integration with artificial intelligence and machine learning.

Main Results:

LIBS provides advantages such as minimal sample preparation, real-time, in situ analysis, and potential for portable/automated systems.
LIBS has demonstrated effectiveness in detecting and quantifying lithium across various complex matrices.
Emerging applications include closed-loop recycling and advanced material classification through AI/ML integration.

Conclusions:

LIBS is a powerful and adaptable tool for addressing lithium supply chain challenges.
The integration of LIBS with AI/ML promises to significantly improve analytical accuracy and material classification.
LIBS technology is poised to play a vital role in sustainable lithium resource management and the energy transition.