C 11 = 155.7 GPa C 12 = 65.6 GPa C 44 = 80.9 GPa

1. High-Throughput Measurements for High-Fidelity Thermodynamic DatabasesJi-Cheng Zhao, Ohio State University Research Foundation, DMR 0804833 Single-crystal anisotropic elastic modulus data from polycrystalline samples Anisotropic single-crystal elastic modulus data are important parameters for both property modeling and applications. Only ~ 200 out of the 50,000 to 200,000 known solid compounds have reported single-crystal elastic modulus data. We have now developed a method to measure anisotropic elastic modulus from large-grained polycrystalline sample. This method will greatly accelerate the anisotropic elastic modulus data gathering for large numbers of compounds and solid solutions without the need to grow large single crystals. Idea for the new method Pure Si (111) surface 6.5% deviation from literature values Literature data This study C11 = 165.7 GPa C12 = 63.9 GPa C44 = 79.6 GPa C11 = 155.7 GPa C12 = 65.6 GPa C44 = 80.9 GPa

2. High-Throughput Measurements for High-Fidelity Thermodynamic DatabasesJi-Cheng Zhao, Ohio State University Research Foundation, DMR 0804833 Services: • Performed thermal conductivity and specific heat capacity mapping for GE on Ti diffusion multiples; • Designed and made diffusion multiples for Carpenter Technology Corporation (Reading, PA) to study the R phase stability in advanced steels; • Designed and made diffusion multiple for GM to perform localized property mapping on Al-Mg-Gd & Mg-Gd-Sn systems to expand the approach to both Al and Mg alloys; • Obtained funding from Outokumpu (Sweden) to use diffusion multiples to study phase stability in the Fe-Cr-Ni system at intermediate temperatures; • The methodology developed under this NSF project will be applied to a newly awarded DOE grant to explore new precipitate phases that will enable high temperature (> 760 °C) steels to be designed for advanced ultrasupercritical (AUSC) boilers and steam turbines for coal energy. This DOE award is a direct result of research performed under this NSF award. Broader Impacts: The tools developed under this NSF project will be a very important part of the experimental tools for the Materials Innovation Infrastructure in the Materials Genome Initiative (MGI) for accelerated design of new materials. Applications of these tools on diffusion multiples are extremely effective in constructing composition-phase-structure-property relationships for materials informatics, testing of materials theories and designing new materials. These materials property microscopy tools may become as widely used as SEM, thus reshaping the way experimental materials research is performed in the future.