Featured Application This work proposes combining the efficient and adaptable analytical technique of LIBS with a robust, accurate multivariate non-linear model. This approach offers a valid method for rapidly analyzing lithium in situ in various lithium-rich waste materials, such as ceramics, lubricants, and pharmaceuticals.Abstract Although approximately half of global lithium consumption is used in the rechargeable battery industry, lithium is also in demand for other specialized applications, such as high-temperature lubricants, ceramics, glass, and pharmaceuticals. The growing need for efficient lithium recovery and recycling underscores the importance of fast and accurate analytical tools for determining lithium concentrations in non-compliant and waste materials generated by industrial processes. In this paper, we present a machine learning-based procedure utilizing Laser-Induced Breakdown Spectroscopy (LIBS) to accurately quantify lithium concentrations in lithium-rich non-compliant materials derived from the industrial production of enamels used for coating metallic surfaces. This procedure addresses challenges such as strong self-absorption and matrix effects, which limit the effectiveness of conventional univariate calibration methods. By employing a multivariate approach, we developed a single model capable of quantifying lithium content across a wide concentration range. A comparison of the LIBS results with those obtained using conventional laboratory analysis (Inductively Coupled Plasma-Optical Emission Spectrometry, ICP-OES) confirms that LIBS can deliver the speed, precision, and reliability required for potential routine applications in the lithium recovery and recycling industry.

Fast Quantification of Lithium Concentration in Non-Compliant Materials Using Laser-Induced Breakdown Spectroscopy

Palleschi, Vincenzo;Poggialini, Francesco;Campanella, Beatrice;Lorenzetti, Giulia;Morelli, Guia;Legnaioli, Stefano
2025

Abstract

Featured Application This work proposes combining the efficient and adaptable analytical technique of LIBS with a robust, accurate multivariate non-linear model. This approach offers a valid method for rapidly analyzing lithium in situ in various lithium-rich waste materials, such as ceramics, lubricants, and pharmaceuticals.Abstract Although approximately half of global lithium consumption is used in the rechargeable battery industry, lithium is also in demand for other specialized applications, such as high-temperature lubricants, ceramics, glass, and pharmaceuticals. The growing need for efficient lithium recovery and recycling underscores the importance of fast and accurate analytical tools for determining lithium concentrations in non-compliant and waste materials generated by industrial processes. In this paper, we present a machine learning-based procedure utilizing Laser-Induced Breakdown Spectroscopy (LIBS) to accurately quantify lithium concentrations in lithium-rich non-compliant materials derived from the industrial production of enamels used for coating metallic surfaces. This procedure addresses challenges such as strong self-absorption and matrix effects, which limit the effectiveness of conventional univariate calibration methods. By employing a multivariate approach, we developed a single model capable of quantifying lithium content across a wide concentration range. A comparison of the LIBS results with those obtained using conventional laboratory analysis (Inductively Coupled Plasma-Optical Emission Spectrometry, ICP-OES) confirms that LIBS can deliver the speed, precision, and reliability required for potential routine applications in the lithium recovery and recycling industry.
2025
Istituto di Chimica dei Composti Organo Metallici - ICCOM - Sede Secondaria Pisa
Istituto di Geoscienze e Georisorse - IGG - Sede Secondaria Firenze
lithium
enamel
LIBS
ICP-OES
machine learning
artificial neural networks
recovery and recycling
circular economy
File in questo prodotto:
File Dimensione Formato  
Appl. Sci. 2025, 15, 9583.pdf

accesso aperto

Tipologia: Versione Editoriale (PDF)
Licenza: Creative commons
Dimensione 1.62 MB
Formato Adobe PDF
1.62 MB Adobe PDF Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.14243/553821
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 0
  • ???jsp.display-item.citation.isi??? 0
social impact