Urban mining of unexploited spent critical metals from E-waste made possible using advanced sorting

The growing number of electronic devices has led to a surge in e-waste, making efficient recycling essential to reduce environmental impact and recover valuable metals. However, traditional recycling methods struggle to extract them due to their low concentrations in e-waste. Here, we developed a system to sort electronic com-ponents from printed circuit boards by elemental composition. It combines a convolutional neural network-based optical recognition with multi-energy X-ray transmission spectroscopy, demonstrating up to 96.9% accuracy in controlled conditions. Hence, with elemental enrichments by up to 10,000 for targeted elements, this method renders economically viable the recovery of previously unrecycled critical metals by enriching sorting bags in precious, semi-precious, refractory (Ta, Nb), transition (Co, Cr, Mn, Ni, Zn, Ga, Bi, etc.) or other (In, Sn, Sb) metals. These findings demonstrate the promising applications of this technology in mitigating the environ-mental impact of e-waste and promoting the sustainable recovery of valuable metals.