Deep learning reveals nanoparticle shape from routine tracking analysis without new hardware

Researchers at the University of Tokyo and the Innovation Center of NanoMedicine (iCONM) have developed an artificial intelligence (AI) approach that identifies the morphology of nanoparticles in liquid using data from standard nanoparticle tracking analysis (NTA), a widely used technique for particle sizing. The method achieved classification accuracies exceeding 80% for non-spherical nanoparticles without requiring modification of existing instruments.