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Engineering topics

Zhang, Wenrui

Publications and source records attributed to Zhang, Wenrui.

Exploring the Spatial Control of Topotactic Phase Transitions Using Vertically Oriented Epitaxial Interfaces

Engineering oxygen vacancy formation and distribution is a powerful route for controlling the oxygen sublattice evolution that affects diverse functional behavior. The controlling of the oxygen vacancy formation process is particularly important for inducing topotactic phase transitions that occur by transformation of the oxygen sublattice. Here we demonstrate an epitaxial nanocomposite approach for exploring the spatial control of topotactic phase transition from a pristine perovskite phase to an oxygen vacancy-ordered brownmillerite (BM) phase in a model oxide La 0.7 Sr 0.3 MnO 3 (LSMO). Incorporating a minority phase NiO in LSMO films creates ultrahigh density of vertically aligned epitaxial interfaces that strongly influence the oxygen vacancy formation and distribution in LSMO. Combined structural characterizations reveal strong interactions between NiO and LSMO across the epitaxial interfaces leading to a topotactic phase transition in LSMO accompanied by significant morphology evolution in NiO. Using the NiO nominal ratio as a single control parameter, we obtain intermediate topotactic nanostructures with distinct distribution of the transformed LSMO-BM phase, which enables systematic tuning of magnetic and electrical transport properties. The use of self-assembled heterostructure interfaces by the epitaxial nanocomposite platform enables more versatile design of topotactic phase structures and correlated functionalities that are sensitive to oxygen vacancies.

36 MATERIALS SCIENCE↗

Deep Bayesian local crystallography

Abstract The advent of high-resolution electron and scanning probe microscopy imaging has opened the floodgates for acquiring atomically resolved images of bulk materials, 2D materials, and surfaces. This plethora of data contains an immense volume of information on materials structures, structural distortions, and physical functionalities. Harnessing this knowledge regarding local physical phenomena necessitates the development of the mathematical frameworks for extraction of relevant information. However, the analysis of atomically resolved images is often based on the adaptation of concepts from macroscopic physics, notably translational and point group symmetries and symmetry lowering phenomena. Here, we explore the bottom-up definition of structural units and symmetry in atomically resolved data using a Bayesian framework. We demonstrate the need for a Bayesian definition of symmetry using a simple toy model and demonstrate how this definition can be extended to the experimental data using deep learning networks in a Bayesian setting, namely rotationally invariant variational autoencoders.

36 MATERIALS SCIENCE↗