A predictive failure framework for brittle porous materials via machine learning and geometric matching methods  Alp Karakoç1, Özgür Keleş2,* 1Aalto University, Department of Bioproducts and Biosystems, Vuorimiehentie 1, Espoo, Finland 2San Jose State University, Chemical and Materials Engineering Department, One Washington Square, San Jose, CA 95192, USA https://link.springer.com/article/10.1007/s10853-019-04339-1 ABSTRACT Brittle porous materials are used in […]

J.R. Seibert, O. Keles, J. Wang, F. Erogbogbo, Data on Thermal Conductivity and Dynamic Mechanical Properties of Graphene Quantum dots in epoxy, Data in Brief,  https://doi.org/10.1016/j.dib.2019.105008. Fig. 2. TEM images of bird charcoal at 2500x (A) and at 50000x (B), and GQDs at 50000x (C) and 500000x (D) GQDs are indicated by the red circles. […]