Colloidal nanocrystals are miniscule pieces (< 100 nm or ∼ 1/1000th the width of a humanhair) of solid materials suspended in a liquid. Semiconductor nanocrystals have controllable optical properties like color, which make them appealing for use in displays, LEDs, solar cells and many other devices that rely on the interaction of matter with light or electricity. The color and other optical properties are related to the size and composition of the crystals, which makes control over these properties crucial to any application. Creating these crystals is often more of an art than a science, with large differences in results arising between chemicals sourced from different suppliers, laboratories, practitioners and any (even slight) changes in the process of making them. The work in this thesis is part of a broader scientific movement aimed at a more scientific understanding and rational control of the synthesis process. To keep the nanocrystals small and stable requires the use of surfactants or ligands, which are chemicals that can interact both with a charged or polar environment such as the nanocrystal and a non-polar environment such as the surrounding medium. The metal halide nanocrystals we focus on here are particularly sensitive to these ligands and the surrounding medium, as they are more similar in nature to salt, whereas most other materials used to make nanocrystals are more similar to rocks. As a result, changing the environment around the nanocrystals can transform the type, shape or composition of the crystal. The studies herein investigate the formation of metal halide nanocrystals, how transformations between them occur and how both processes are interrelated. The rapid development in precision and scale of machine learning (ML) methods capable of predictions based on large sets of data has roused the interest of scientists across many disciplines. Concurrently, improvements in the automation of chemical synthesis and charac- terization methods now allow for the generation of data on hundreds to thousands of reactions necessary to use such data science methods. Due to the complexity of chemical synthesis in general, and nanocrystal synthesis in particular, using ML to predict the outcomes of reac- tions is an attractive proposition. This thesis introduces methods from machine learning and data science to the synthesis of nanocrystals, and connects them to more physical models of this process. In this thesis, Chapter 1 outlines the important concepts related to nanocrystals, their properties, characterization methods and reactions. We then briefly introduce current trends of using machine learning and data science to understand synthesis. In Chapter 2, we use high-throughput synthesis experiments coupled to automated deconvolution of optical absorption spectra to gain an overview of the reaction landscape around one well-studied metal halide nanocrystal material, CsPbBr3. In Chapter 3, we investigate in more detail the formation and transformation processes of one material we found of central importance in the overview, the atomically thin nanosheets of OLA2PbBr4 using in-situ absorption spectroscopy and joint fits of spectral and kinetic models, as well as more traditional kinetic analysis where appropriate. Further analysis of precursor reactions is provided in Chapter 4. We then examine synthesis and properties of a family of cesium silver metal halide double perovskites in Chapter 5. Finally in Chapter 6, we discuss the implications of this work on different scientific fields.

This dissertation highlights advances in using semiconductor tetrapod quantum dots (tQDs) as stress sensors in structural polymer nanocomposites. Semiconductor nanocrystals have undergone many developments in terms of synthetic control of shape and size since their inception, and one example is the ability to create branched nanocrystals such as tQDs. tQDs are core/shell tetrahedrally symmetric, branched nanocrystals. In this thesis, studies will utilize tQDs consisting of a ~4 nm cadmium selenide (CdSe) core and ~25 nm long cadmium sulfide (CdS) arms. Their type I band alignment and the modulus difference between their core and shell, along with their branching, makes them sensitive to applied mechanical environmental stresses. The CdS arms receive stress from a host matrix in which the tQDs are embedded and transmit it to the CdSe core. The tQD’s photoluminescence emission spectral maximum undergoes a monotonic red-shift, or decrease in energy, with increasing tensile stress, due to widening bond distances in the core. The tQD’s property of nanoscale stress-sensing is of relevance to fields such as polymer dynamics, sensing of premature fracture in service, and biomechanical stress sensing.

We have fabricated and characterized the structure and opto-mechanical sensing ability of a wide variety of tQD-polymer nanocomposites. We demonstrate tQD sensing of tension and compression as well as more complex stress responses, such as stress relaxation and hysteresis. We perform optical and mechanical tests simultaneously, discovering a new sensing modality, and orders of magnitude of stress amplification in the tQD core. We also discover tQD sensing of dispersion including a switch in optomechanical response characteristic when tQDs are in direct contact.

In addition to demonstrating and analyzing these new phenomena, we theoretically explore, with micromechanical finite element simulations and atomistic density functional theory, the origins of the tQD stress response. We further examine, experimentally and theoretically, the ability of tQDs to serve as mechanical fillers, finding that they have greater potential to improve the Young’s modulus of structural polymers than linear nanorods and nano-spheres due to their branched shape. The results in this dissertation contribute to the understanding of the structural, mechanical, and optical sensing properties of nanocomposites of polymers and semiconductor tQD nanocrystals.