Electronic waste analysis using laser-induced breakdown spectroscopy (LIBS) and X-ray fluorescence (XRF): Critical evaluation of data fusion for the determination of Al, Cu and Fe

• Main Author: Ferreira, Dennis S.
• Publication Date: 2024
• Other Authors: Pereira, Fabiola M.V. [UNESP], Olivieri, Alejandro C., Pereira-Filho, Edenir R.
• Format: Article
• Language: eng
• Source: Repositório Institucional da UNESP
• Download full: http://dx.doi.org/10.1016/j.aca.2024.342522; https://hdl.handle.net/11449/304530
• topic: Data fusion; E-waste; Figure of merit; LIBS; Multivariate calibration

Background: Electronic waste (e-waste) proliferation and its implications underscore the imperative for advanced analytical methods to mitigate its environmental impact. It is estimated that e-waste production stands at a staggering 20–50 million tons yearly, of which merely 20–25% undergo formal recycling. The e-waste samples evaluated contain computers, laptops, smartphones, and tablets. Results: Forty-one samples were processed, involving the disassembly and separation of components. Subsequently, two analytical techniques, laser-induced breakdown spectroscopy (LIBS) and energy dispersive X-ray fluorescence (ED-XRF), were applied to quantify aluminum (Al), copper (Cu), and iron (Fe) in the e-waste samples. The samples were then analyzed after acid mineralization with 50% v v−1 aqua regia in a digester block and finally by ICP OES. A solid residue composed of Si and Ti was observed after the digestion of the samples. Multivariate calibration strategies such as partial least-squares regression (PLS), principal component regression (PCR), maximum likelihood principal component regression (MLPCR), and error covariance penalized regression (ECPR) were used for calibration. Finally, the figures of merit were calculated to verify the most suitable models. The results revealed robust models with notable sensitivity, varying from 8.98 to 35.04 Signal (a.u.)(% w w−1) −1, low Limits of Detection (LoD) within the range of 0.001–0.2 % w w−1, and remarkable relative errors ranging from 2% to 33%, particularly for Cu and Fe. Significance: Notably, the models for Al faced inherent challenges, thus highlighting the complexities associated with its quantification in e-waste samples. In conclusion, this research represents an important step toward a more sustainable and efficient future for electronic waste recycling, signifying its relevance to global environmental welfare and resource conservation.