2026-09-20T04:24:53Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1440922022-07-28T03:35:55Zcom_1721.1_7582com_1721.1_7581col_1721.1_131022
Van Voorhis, Troy
McIsaac, Alexandra Ross
Massachusetts Institute of Technology. Department of Chemistry
2022-07-27T18:21:46Z
2022-07-27T18:21:46Z
2021-09
2022-07-27T11:43:22.994Z
https://hdl.handle.net/1721.1/144092
0000-0002-7210-1164
In the past two decades, new technologies such as organic light emitting diodes (OLEDs) and quantum dots have emerged as promising candidates for applications from displays to solid state lighting. Many phenomenological and empirical models exist to explain the properties of these materials, and have succeeded in describing some of their properties. However, both of these systems have high degrees of disorder; for OLEDs, this manifests due to the molecular makeup of the emitting layer, and for quantum dots, due to their highly non-crystalline surface. Explaining properties that arise due to this disorder requires models that go beyond the phenomenological, in particular, it requires methods that can explicitly model the atoms and molecules causing disorder. In this thesis, we investigate the properties of quantum dot surfaces using density functional theory, which is an atomistic, all-electron electronic structure method. This allows us to identify specific features on the quantum dot surface and tie these features to the optical properties of the quantum dot. We find that undercoordinated surface atoms on the surface of CdSe can cause optical traps even when there are no traps in the ground state band structure, show that surface reorganization and annealing can significantly improve the optical properties of CdSe, and also explore sources of traps in CdSe/CdS core/shell quantum dots. In addition, we develop a model for OLED kinetics, which is able to incorporate the effects of molecular disorder but is very computationally efficient. We show that this model can extract molecular rate constants from a device-level measurement, and can help identify sources of efficiency loss in OLED devices.
Ph.D.
Massachusetts Institute of Technology
In Copyright - Educational Use Permitted
Copyright MIT
http://rightsstatements.org/page/InC-EDU/1.0/
Semiconducting Devices and Nanomaterials: Insight from Computational Chemistry
Thesis
application/pdf
Doctoral
Doctor of Philosophy
Publication
unknown
unknown
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<Abstract>In the past two decades, new technologies such as organic light emitting diodes (OLEDs) and quantum dots have emerged as promising candidates for applications from displays to solid state lighting. Many phenomenological and empirical models exist to explain the properties of these materials, and have succeeded in describing some of their properties. However, both of these systems have high degrees of disorder; for OLEDs, this manifests due to the molecular makeup of the emitting layer, and for quantum dots, due to their highly non-crystalline surface. Explaining properties that arise due to this disorder requires models that go beyond the phenomenological, in particular, it requires methods that can explicitly model the atoms and molecules causing disorder. In this thesis, we investigate the properties of quantum dot surfaces using density functional theory, which is an atomistic, all-electron electronic structure method. This allows us to identify specific features on the quantum dot surface and tie these features to the optical properties of the quantum dot. We find that undercoordinated surface atoms on the surface of CdSe can cause optical traps even when there are no traps in the ground state band structure, show that surface reorganization and annealing can significantly improve the optical properties of CdSe, and also explore sources of traps in CdSe/CdS core/shell quantum dots. In addition, we develop a model for OLED kinetics, which is able to incorporate the effects of molecular disorder but is very computationally efficient. We show that this model can extract molecular rate constants from a device-level measurement, and can help identify sources of efficiency loss in OLED devices.</Abstract>
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