Ferreira Dennis S, Pereira Fabiola M V, Olivieri Alejandro C, Pereira-Filho Edenir R
Group of Applied Instrumental Analysis (GAIA), Department of Chemistry, Federal University of São Carlos (UFSCar), P.O. Box 676, São Carlos, São Paulo State, 13565-905, Brazil; Departamento de Química Analítica, Facultad de Ciencias Bioquímicas y Farmacéuticas, Universidad Nacional de Rosario, Suipacha 531, 2000, Rosario, Argentina; Instituto de Química Rosario (CONICET-UNR), 27 de Febrero 210 Bis, 2000, Rosario, Argentina.
Group of Alternative Analytical Approaches (GAAA), Bioenergy Research Institute (IPBEN), Institute of Chemistry, São Paulo State University (UNESP), Araraquara, São Paulo, 14800-060, Brazil.
Anal Chim Acta. 2024 May 15;1303:342522. doi: 10.1016/j.aca.2024.342522. Epub 2024 Mar 22.
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.
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 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) , low Limits of Detection (LoD) within the range of 0.001-0.2 % w w, and remarkable relative errors ranging from 2% to 33%, particularly for Cu and Fe.
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.
电子垃圾(电子废弃物)的激增及其影响凸显了采用先进分析方法以减轻其环境影响的必要性。据估计,每年电子垃圾产量高达2000万至5000万吨,其中仅有20% - 25%进行正规回收。所评估的电子垃圾样本包括电脑、笔记本电脑、智能手机和平板电脑。
处理了41个样本,包括部件的拆解和分离。随后,应用激光诱导击穿光谱法(LIBS)和能量色散X射线荧光光谱法(ED - XRF)对电子垃圾样本中的铝(Al)、铜(Cu)和铁(Fe)进行定量分析。样本在消解仪中用50%(v/v)王水进行酸矿化处理后进行分析,最后通过电感耦合等离子体发射光谱法(ICP OES)分析。样本消解后观察到由硅(Si)和钛(Ti)组成的固体残渣。采用偏最小二乘回归(PLS)、主成分回归(PCR)、最大似然主成分回归(MLPCR)和误差协方差惩罚回归(ECPR)等多元校准策略进行校准。最后,计算品质因数以验证最合适的模型。结果显示模型稳健,灵敏度显著,范围为8.98至35.04信号(任意单位)/(质量分数%),检测限低,在0.001 - 0.2%(质量分数)范围内,相对误差显著,范围为2%至33%,特别是对于铜和铁。
值得注意的是,铝的模型面临固有挑战,从而凸显了在电子垃圾样本中对其进行定量分析的复杂性。总之,本研究朝着电子垃圾回收更可持续、更高效的未来迈出了重要一步,表明其与全球环境福祉和资源保护的相关性。