High throughput glass transition temperature measurement

The glass transition temperature (Tg) of a polymer is an essential parameter in materials design as the transition is accompanied by changes in properties including the conductivity. In order to identify the suitable materials for electrolytes in lithium-ion batteries, the Tg is a key parameter that needs to be established. Various traditional methods exist for Tg analysis including dynamic scanning calorimetry (DSC), thermomechanical analysis (TMA), and dynamic mechanical analysis (DMA). Whereas these methods provide a highly accurate measurement of Tg, their limitations include the manual and time-consuming nature of the procedure. This shortcoming has a larger impact especially when large datasets need to be assessed. In recent years, high throughput characterization techniques have played a critical role in novel materials design. By incorporating computational methods, a high number of datasets can be screened in a short time to deduce a smaller list of potential candidates. Computer vision is a rapidly growing computational field that is being explored. Computer vision allows the computer to understand the features within an image and utilize that knowledge to deduce further information or provide a new image. In this research, high throughput characterization techniques are explored to analyze the Tg of polymers. The automated sample preparation process, the hardware, and software set up for image collection, and two approaches for the image analysis have been introduced. The performance of the two methodologies has been discussed further, evaluating their accuracy in Tg determination and their time and labor efficiency.Bachelor of Engineering (Materials Engineering