Smart Determination of Gold Content in PCBs of Waste Mobile Phones by Coupling of XRF and AAS Techniques

Quantitative determination of most economic valuable metals in waste is the first fundamental operation of evaluating the feasibility of recycling processes. Field-portable X-ray fluorescence spectrometers (FPXRFs) represent a more practical, efficient, and economic tool in determining the elemental...

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Bibliographic Details
Main Authors: Nicolò Maria Ippolito, Gianmaria Belardi, Valentina Innocenzi, Franco Medici, Loris Pietrelli, Luigi Piga
Format: Article
Language:English
Published: MDPI AG 2021-09-01
Series:Processes
Subjects:
Online Access:https://www.mdpi.com/2227-9717/9/9/1618
Description
Summary:Quantitative determination of most economic valuable metals in waste is the first fundamental operation of evaluating the feasibility of recycling processes. Field-portable X-ray fluorescence spectrometers (FPXRFs) represent a more practical, efficient, and economic tool in determining the elemental composition of samples with respect to conventional analytical techniques, such as atomic absorption spectrometry (AAS) and inductively coupled plasma emission spectrometry (ICP). In this paper, quick and smart determination of gold content in printed circuit boards (PCBs) of waste mobile phones was studied. The aim of the research was to combine the practicality of FPXRFs with the reliability of quantitative spectrometry analysis and evaluate the error between the two techniques. Several samples (33) of PCBs were ground to a size below 0.5 mm, and then, the powders were analyzed by FPXRFs at different acquisition times with five replications for each sample. The same analyzed samples then underwent chemical attack to determine the quantitative gold content by AAS. The obtained results were associated with FPXRFs response with the purpose of realizing a calibration curve (100–1000 mg/kg Au). The curve was validated for accuracy and precision by other PCBs waste samples; the control samples were added as standards to obtain a more reliable calibration curve. The curve was evaluated with RPD classification, regression linear, and Bolt–Altman analysis.
ISSN:2227-9717