Accurate SEM-EDS quantification, automation, and machine learning enable high-throughput compositional characterization of powders — UC Berkeley

Researchers introduce AutoEMX, an automated SEM-EDS framework that overcomes particle-shape quantification artifacts to resolve the individual compositions in multi-phase powders with 5-10% error, integrating into self-driving labs for accelerated material discovery