Reduced electron exposure for energy-dispersive spectroscopy using dynamic sampling.

Analytical electron microscopy and spectroscopy of biological specimens, polymers, and other beam sensitive materials has been a challenging area due to irradiation damage. There is a pressing need to develop novel imaging and spectroscopic imaging methods that will minimize such sample damage as well as reduce the data acquisition time. The latter is useful for high-throughput analysis of materials structure and chemistry. In this work, we present a novel machine learning based method for dynamic sparse sampling of EDS data using a scanning electron microscope. Our method, based on the supervised learning approach for dynamic sampling algorithm and neural networks based classification of EDS data, allows a dramatic reduction in the total sampling of up to 90%, while maintaining the fidelity of the reconstructed elemental maps and spectroscopic data. We believe this approach will enable imaging and elemental mapping of materials that would otherwise be inaccessible to these analysis techniques.

Catalyst incorporation at defects during nanowire growth.

Scanning and transmission electron microscopy was used to correlate the structure of planar defects with the prevalence of Au catalyst atom incorporation in Si nanowires. Site-specific high-resolution imaging along orthogonal zone axes, enabled by advances in focused ion beam cross sectioning, reveals substantial incorporation of catalyst atoms at grain boundaries in <110> oriented nanowires. In contrast, (111) stacking faults that generate new polytypes in <112> oriented nanowires do not show preferential catalyst incorporation. Tomographic reconstruction of the catalyst-nanowire interface is used to suggest criteria for the stability of planar defects that trap impurity atoms in catalyst-mediated nanowires.