We use in-situ 3D microscopy and electronic transport measurement to visualize and understand the emergent properties of these materials.
We aim to understand how the energy landscape of nanoscale ferroic materialscan be influenced through geometric patterning or confinement and through interfacial interactions. We aim to explore and control the formation of novel distributions of spin and charge, for example stable topological spin structures in nanoscale magnetic disks, magnetic monopole defects in artificial spin ices, as well as flux-closure and metastable domains, in ferroelectric nanostructures. We are also exploring how confinement and charged defects affect the charge distribution in resistance switching oxides, including artificial nanoscale. networks. Our goal is to understand the emergent physical phenomena that are observed in these systems in response to external stimuli, such as the synaptic behavior of the conducting filaments in resistive oxides and domain walls in artificial magnetic networks, the behavior of magnetic skyrmions, and the negative capacitance effect in ferroelectric materials.
A particular focus of our research is the use of in-situ 3D microscopy to visualize and understand this nanoscale behavior. Our approach involves a combination of aberration-corrected Lorentz transmission electron microscopy and advanced scanning force microscopy that we use to address the scientific questions related to ferroic nanostructure behavior and resistance switching oxides. We have developed in-situ techniques that allow us to visualize domain behavior, local structural and electronic environment, and transport behavior in nanostructures as a function of external stimuli. such as applied fields, temperature and time. We combine these experiments with measurement of charge, potential and current in the mesoscopic length scale, and with simulations. We also apply the microscopy and electronic transport measurement techniques to explore materials for applications in batteries and solar cell coatings.
More broadly, we interact with the wider imaging community at Argonne to develop experimental and computational capabilities for multi-modal imaging at the nanoscale, including incorporating machine learning approaches.