AI-enhanced sorting enabling direct high-purity tantalum urban mining: a novel pathway from e-waste to critical materials

Tantalum’s supply chain instability demands efficient urban mining from e-waste. Here, we present an AI-enhanced process that combines intelligent sorting with sustainable hydrometallurgy for high-yield/high-purity Ta recovery. A hybrid sorting system, cascading an interpretable convolutional neural network (CNN) with automated multi-energy X-ray transmission (MEXRT) spectroscopy, achieved 99.6 % precision and 96.9 % recall at 3000 components/hour, resolving the Ta/Nb ambiguity. Spatial activation mapping illustrated the visual sorting mechanism, facilitating feature-driven upgrading. Meanwhile, Canny edge detection and K-edge detection enabled real-time and pixel-wise spectral analysis under multithreaded processing. Downstream, streamlined physical separation and thermodynamically guided reverse leaching selectively recovered Ta with 98.2 % efficiency under mild conditions. Advanced characterization using transmission electron microscopy and ion beam analysis revealed a quantifiable core-shell Ta/Ta 2 O 5 structure in leached products, guiding calcination into >99.8 % pure Ta 2 O 5 . This work establishes a closed-loop urban mining framework, demonstrating how AI and tailored refining enable a circular economy for critical metals.