Connecting structure and function in nanoscale engineered materials and devices relies on the analysis of the fundamental arrangement of matter, frequently under dynamic conditions. The demand to image structures at fundamental length scales has touched inorganic materials, biology, and frequently hybrid hard/soft materials with unique phenomena driven by heterogeneous components....
Recent developments have enabled L12-strengthened Co-based superalloys, which have thepotential to surpass Ni-based superalloys as the material of choice for the hottest sections of turbine
blades due to cobalt’s 40 ºC higher melting point. The most-studied branch of Co-based
superalloys are based on the L12 phase Co3(Al,W); however, there is...
Supervised learning model is one of the most fundamental machine learning models. It can provide powerful capability of prediction by learning complex patterns hidden in many, sometimes thousands, predictors. It can also be used as a building block of other machine learning tasks, like unsupervised learning and reinforcement learning. Such...
Soft materials such as colloids and polymers often exhibit a variety of mesoscopic structures that are governed merely by weak physical interactions. Due to these intermediate structures, they can be easily taken out of thermal equilibrium by introducing external stimuli such as a shear flow and electromagnetic fields. This thesis...
The ability to control the crystalline ordering and morphology of polymeric nanomaterials is a grand challenge in the field of materials science, which could enable the development of functional materials able to solve long-standing problems in renewable energy and medicine. In this work, we explore a combination of supramolecular chemistry...