Transmission electron microscopy (TEM) has come a long way since its inception in 1931, yet most efforts have been focused on improving spatial resolution. During her talk at the Multimodal In Situ Characterization symposium, Mitra Taheri, associate professor of materials science and engineering at Drexel University, argued that other important areas for improvement are time resolution, chemical evolution, and data mining/intelligent microscopy.
To this end, Taheri’s group has been pioneering direct detection electron energy-loss spectroscopy (DD-EELS). By installing a direct electron detector at the end of an energy filter on a standard JEOL microscope, an upgrade that Taheri referred to as “making a racecar out of a Toyota Camry,” a technique with temporally and spatially resolved elemental and chemical information was born.
Compared to a traditional indirect detector in which electrons are converted to photons via a scintillator, direct detectors skip the scintillation step, resulting in reduced signal spreading, improved resolution, reduced readout noise, and the advantage of electron counting. Taheri showed several applications for which DD-EELS has already proved advantageous: spectrum imaging of beam sensitive core-shell polymers, valence state mapping in oxide heterostructures, and spectroscopy at high energies such as the Ni K edge and Cu K edge that are normally inaccessible with EELS due to the increase in noise at higher energy losses. DD-EELS does, however, result in large data sets that can often be difficult to analyze. Moving forward, Taheri proposes that artificial intelligence and machine learning will be instrumental to taking DD EELS to the next level.
