Data Analytics Applied to Chemical Transformations in Liquids

Elucidating the fundamental mechanisms of nanocrystal growth necessitates the utilization of high spatial resolution imaging techniques that are capable of directly imaging individual nucleation and growth events within a liquid phase. By combining time-resolved imaging datasets with quantitative image analysis algorithms, the factors controlling chemical transformations can be determined by analyzing the nanocrystal size and morphological evolution. In situ liquid cell microscopy has recently been used to directly image and elucidate mechanisms of nanocrystal nucleation and growth from a precursor solution [1]. In these experiments, the electron beam is used as a source for ionizing radiation to generate radiolytic species, which in turn act as a reducing agent to chemically transform metallic species from their organometallic precursors. The concentration of radiolytic species is influenced by the electron dose, which consequently affects the nanocrystal growth mechanisms and kinetics. For example, Woehl et al. has shown that a change in the electron dose can effectively alter the growth mechanisms from reaction-controlled growth (at low dose) to diffusion-limited growth (at high dose) [2]. The motivation for this work is to develop a robust analytical framework to quantitatively analyze changes in nanocrystal size and morphology using data acquired from electron beam-induced chemical transformations and electrochemical transformation during in situ liquid cell experiments [3].