MORE | Spring 2023
Analysis of Vibrational EELS Datasets to Unravel the Nanoscale Ferroelectric-Ferroelastic Coupling in Oxides
Perovskite materials, such as lead titanate (PbTiO3), have emerged as a promising candidate for memory devices due to their exceptional ferroelectric and piezoelectric properties. Ferroelectric memory devices based on perovskite materials offer several advantages over conventional memory devices, including low power consumption, fast switching speeds, and high-density data storage.
One of the key advantages of perovskite materials is their ability to form thin films with controlled orientation and composition. This property enables the growth of high-quality perovskite films on various substrates, including silicon and sapphire, to enhance their ferroelectric properties.
In this project, we have studied PbTiO3 grown on a lattice-mismatched substrate such as DyScO3, PbTiO3 forms two domains with the polarization pointing in two different directions, creating a domain wall. However, the relationship between the polarization and strain in the domain wall remains unknown in the field of ferroelectrics, this mystery is solved by using atomic resolution STEM and vibrational EELS to simultaneously measure displacement vectors and phonon properties. The challenge in this method is poor signal-to-noise ratio due to the dominance of the zero-loss peak in the inelastic scattering EELS signal. The proposed solution involves using a direct electron detector and optimizing the EELS background subtraction using various signal fitting routines, such as Gauss, Gauss-Lorentz, Lorentz, and power law backgrounds. The work will be conducted using python notebooks.
Materials science and engineering
Hometown: Tempe, Arizona, United States
Graduation date: Spring 2024