Research Projects - Micromechanics

Within this project, we will investigate the micromechanical properties of STO materials with low and higher content of dislocations at a wide range of strain rates (0.001/s-1000/s). Oxide ceramics have increasing importance as superconductors and their dislocation-based electrical functionalities that will affect these electrical properties. Hence it is fundamental to understand the deformation limits to introduce dislocations for both the fabrication process and in-use performance.
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Smaller is stronger” is well known in micromechanics, but the properties far from the quasi-static regime and the nominal temperatures remain unexplored. This research will bridge this gap on how materials behave under the extreme conditions of strain rate and temperature, to enhance fundamental understanding of their deformation mechanisms. The mechanical behavior of metals with different crystal topologies, i.e. FCC, BCC and HCP and alloy systems, will be investigated in a statistically relevant manner using dewetted microparticles as the test-beds. more

The goal of this project is the investigation of interplay between the atomic-scale chemistry and the strain rate in affecting the deformation response of Zr-based BMGs. Of special interest are the shear transformation zone nucleation in the elastic regime and the shear band propagation in the plastic regime of BMGs. more

This project will aim at addressing the specific knowledge gap of experimental data on the mechanical behavior of microscale samples at ultra-short-time scales by the development of testing platforms capable of conducting quantitative micromechanical testing under extreme strain rates upto 10000/s and beyond. more

Statistical significance in materials science is a challenge that has been trying to overcome by miniaturization. However, this process is still limited to 4-5 tests per parameter variance, i.e. Size, orientation, grain size, composition, etc. as the process of fabricating pillars and testing has to be done one by one. With this project, we aim to fabricate arrays of well-defined and located particles that can be tested in an automated manner. With a statistically significant amount of samples tested per parameter variance, we expect to apply more complex statistical models and implement machine learning techniques to analyze this complex problem. more