Study of the structural disorder on semiconductor nanomaterials using advanced electron diffraction techniques

• Grant number: 22/13144-0
• Support Opportunities: Scholarships in Brazil - Doctorate (Direct)
• Effective date (Start): September 01, 2023
• Effective date (End): April 30, 2027
• Field of knowledge: Physical Sciences and Mathematics - Chemistry - Physical-Chemistry
• Principal Investigator: João Batista Souza Junior
• Grantee: Victor Secco Lemos
• Host Institution: Centro Nacional de Pesquisa em Energia e Materiais (CNPEM). Ministério da Ciência, Tecnologia e Inovação (Brasil). Campinas , SP, Brazil

Abstract

Semiconductor materials have a high economic, technological, and social importance, given that society daily uses electronics devices that depend on these materials, such as TV, cell phones, computers, and countless other electronic components. Specifically for semiconductor nanomaterials, quantum dots, obtaining systems with high fluorescence quantum yield and electronic mobility is essential for optimizing the use of semiconductors in electronic devices. Invariably, to obtain nanomaterials with better performance, precise structural control of the nanoparticles is necessary and, consequently, statistically precise, and representative structural characterization techniques capable of evaluating the structural disorder at the atomic level of the synthesized QD are needed. High resolution Transmission Electron Microscopy images and Electron Diffraction are widely used for such characterizations, but it is worth noting that only a few nanoparticles can be analyzed for each sample against tens or hundreds of thousands of nanoparticles present in the sample. Furthermore, Electron Diffraction data have challenges related to the interpretation and direct correlation with the structure and defects. Thus, new advanced TEM techniques and ED data processing are needed to meet this demand for structural correlation and QD properties. In this project, we aim to develop and improve Electron Diffraction Pair Distribution Function (ePDF) data processing techniques to better understand the nanostructure and its different defects. These computational methods ranging from unity cell analysis to Reverse Monte Carlo and Machine Learning algorithms will improve knowledge and applicability of the ePDF, which has easier implementation, but has less studies, dedicated techniques or software. (AU)