Engineering topics

Zhang, Zimeng

Publications and source records attributed to Zhang, Zimeng.

3 records

DOE OSTI2024

Phase Transformation Driven by Oxygen Vacancy Redistribution as the Mechanism of Ferroelectric Hf 0.5 Zr 0.5 O 2 Fatigue

As a promising candidate for nonvolatile memory devices, the hafnia-based ferroelectric system has recently been a hot research topic. Although significant progress has been made over the past decade, the endurance problem is still an obstacle to its final application. In perovskite-based ferroelectrics, such as the well-studied Pb[Zr x Ti 1-x ]O 3 (PZT) family, polarization fatigue has been discussed within the framework of the interaction of charged defects (such as oxygen vacancies) with the moving domains during the switching process, particularly at the electrode-ferroelectric interface. Armed with this background, a hypothesis is set out to test that a similar mechanism can be in play with the hafnia-based ferroelectrics. The conducting perovskite La-Sr-Mn-O is used as the contact electrode to create La 0.67 Sr 0.33 MnO 3 / Hf 0.5 Zr 0.5 O 2 (HZO)/ La 0.67 Sr 0.33 MnO 3 capacitor structures deposited on SrTiO 3 -Si substrates. Nanoscale X-ray diffraction is performed on single capacitors, and a structural phase transition from polar o-phase toward non-polar m-phase is demonstrated during the bipolar switching process. The energy landscape of multiphase HZO has been calculated at varying oxygen vacancy concentrations. Based on both theoretical and experimental results, it is found that a polar to non-polar phase transformation caused by oxygen vacancy redistribution during electric cycling is a likely explanation for fatigue in HZO.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Machine learning assisted phase and size-controlled synthesis of iron oxide particles

Synthesis of iron oxides with specific phases and particle sizes is a crucial challenge in various fields, including materials science, energy storage, biomedical applications, environmental science, and earth science. However, despite significant advances in this area, much of the current palette of particle outcomes has been based on time-consuming trial-and-error exploration of synthesis conditions. The present study was designed to explore a very different approach to 1) predict the outcome of synthesis from specified reaction parameters based on using machine learning (ML) techniques, and 2) correlate sets of parameters to obtain products with desired outcomes by a newly designed recommendation algorithm. To achieve this, four ML algorithms were tested, namely random forest, logistic regression, support vector machine, and k-nearest neighbor. Among the models, random forest outperformed the others, attaining 96% and 81% accuracy when predicting the phase and size of iron oxide particles in the test dataset. Surprisingly, the permutation feature importance analysis revealed that volume, which may strongly relate to pressure, was one of the important features, along with precursor concentration, pH, temperature, and time, influencing the phase and size of iron oxide particles during synthesis. To verify the robustness of the random forest models, prediction and experimental results were compared based on 24 randomly generated methods in additive and non-additive systems not included in the datasets. The predictions of product phase and particle size from the models agreed well with the experimental results. Furthermore, a searching and ranking algorithm was developed to recommend potential synthesis parameters for obtaining iron oxide products with the desired phase and particle size from previous studies in the dataset. Furthermore, this study lays the foundation for a closed-loop approach in materials synthesis and preparation, beginning with suggesting potential reaction parameters from the dataset and predicting potential outcomes, followed by conducting experiments and analyses, and ultimately enriching the dataset.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2022

A Three-Dimensional Reconstruction Algorithm for Scanning Transmission Electron Microscopy Data from a Single Sample Orientation

Abstract Increasing interest in three-dimensional nanostructures adds impetus to electron microscopy techniques capable of imaging at or below the nanoscale in three dimensions. We present a reconstruction algorithm that takes as input a focal series of four-dimensional scanning transmission electron microscopy (4D-STEM) data. We apply the approach to a lead iridate, PbIrO, and yttrium-stabilized zirconia, YZrO, heterostructure from data acquired with the specimen in a single plan-view orientation, with the epitaxial layers stacked along the beam direction. We demonstrate that Pb–Ir atomic columns are visible in the uppermost layers of the reconstructed volume. We compare this approach to the alternative techniques of depth sectioning using differential phase contrast scanning transmission electron microscopy (DPC-STEM) and multislice ptychographic reconstruction.

47 OTHER INSTRUMENTATION↗