Transition metal dichalcogenides (TMDs) are crystalline layered materials that have significantly impacted the field of condensed matter physics. These materials were the first exfoliatable semiconductors to be discovered after the advent of graphene. The focus of this dissertation is utilizing multiple imaging and characterization techniques to improve and understand the impact of strain and lattice defects in these materials. These inclusions to the lattice, alter the semiconducting performance in controllable ways. A comprehensive study using scanning tunneling spectroscopy (STM), spectroscopy (STS), scanning transmission electron microscopy (STEM), and photoluminescence (PL) in this work will provide a breadth of ways to pinpoint and cross-examine the impact of these factors on these materials. In the first half of this work we focus on the control of lattice defects through two growth processes: chemical vapor transport (CVT) and self-flux. By fine tuning the growth procedure we are both able to determine the intrinsic defects of the material, their electronics, and consistently diminish their density. The second half uses an in-situ strain device to reversibly control and examine the effects of applied strain on transition metal dichalcogenide layers. Utilizing the scanning tunneling microscope to image the lattice, we characterize the change of lattice parameters and observe the formation of strain solitons within the lattice. Measuring these solitons directly we look at the dynamics of a special class of line defects, folds within the top layer of the material, that occur naturally as strain is relieved within the monolayer. With the available imaging techniques and theoretical models we uncover a host of properties of these materials that are only accessible within the high strain regime
| Identifer | oai:union.ndltd.org:columbia.edu/oai:academiccommons.columbia.edu:10.7916/d8-1426-m546 |
| Date | January 2019 |
| Creators | Edelberg, Drew Adam |
| Source Sets | Columbia University |
| Language | English |
| Detected Language | English |
| Type | Theses |
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