Thursday, 5 October 2017: 10:40
Chesapeake D (Gaylord National Resort and Convention Center)
A rational co-design process involves a synergistic approach towards the synthesis and development of new dielectric materials with high throughput screening and testing. This approach is gradually replacing the traditional Edisonian approach of using trial and error to a more sophisticated, data driven, approach for designing next generation dielectric materials. Recently, advancements in dielectric materials are achieved by the incorporation of group 14 metal in the main chain of polymer as an organometallic systems. Here in, we introduce transition metal based metal-organic framework (MOF) polymers as a dielectric materials. Transition metals zinc and cadmium are incorporated in the main chain of polymer through metal-oxygen bonds with varying number of methylene spacers in the repeat units. High molecular weight coordination complex polyesters are obtained, which exhibit high band gaps and low loss compared to their oxide counterparts while maintaining high dielectric constants. The rational co-design of these materials are motivated by DFT computations of the electronic, ionic and total dielectric constant contributions, band gap energies, along with their structural predictions of the local environments. Polymers are characterized with Nuclear Magnetic Resonance Spectroscopy (1H-NMR), Fourier Transform Infrared Spectroscopy (FTIR) and with X-Ray Diffraction (XRD) for their structural properties and features which shows strong correlation to the experimental findings. Thermal behaviors are observed with Thermogravimetric Analysis (TGA) and Differential Scanning Calorimetry (DSC). Dielectric Spectroscopy and Optical Band Gap are measured and found strong correlation with the Density Functional Theory (DFT) predictions. These initial studies give insight into the potential advantages an expansion of chemical space for energy storage applications through a rational co-design approach. This work provides valuable validation and feedback to computational predictions to fulfill the goals of this approach.