Daniel S. Grégoire

A generalizable time-gated Raman spectroscopy-machine learning workflow for accurate identification of challenging plastics in electronic waste.

Electronic waste (E-waste), the fastest-growing solid waste stream, is projected to reach 82 million metric tons globally by 2030. E-waste plastics are difficult to sort due to diverse polymer compositions and the widespread use of pigments and other additives, which introduce complexity and/or strong fluorescence inte...