RAJ LAB Advanced Multiscale Design, Fabrication, and Characterization

Research Themes and Projects

Interested in developing rapid multiscale characterization and synthesis techniques with the ultimate aim to design damage-tolerant structural materials that can accommodate and even repair structural damage. I study transport phenomenon and the emergent mechanical properties across multiple length scales with nanoscale resolution, aiming to identify the indicators for ability to mitigate damage, accommodate defects, and heal.

High-Throughput Local Diffusion Mapping
High-Throughput Local Diffusion Mapping Multiscale diffusion measurement with nanoscale resolution

Introducing a high-resolution, high-throughput characterization technique. LDM determines what diffuses locally through microstructures, experimentally realized by pressing a nanomold onto a microstructure, forming a nanorod array

Thermomechanical Nanofabrication
Thermomechanical Nanofabrication Fast diffusion in eutectic systems enables nanofabrication at room temperature

Revealing enhanced eutectic interface diffusion, which provides a thermomechanical nanofabrication method at low temperatures.

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Metallic Glass under Irradiation
Metallic Glass under Irradiation Strengthening of Zr-based metallic glass at low dose helium ion irradiation

A strengthening effect in Zr-based metallic glass under low-dose helium ion irradiation in the absence of nanocrystals is observed. This is measured through nanoindentation hardness measurements revealing an initial increase in hardness with low irradiation doses, followed by a rapid decrease at higher doses.

Machine learning performance in metallic glass physics
Machine learning performance in metallic glass physics Comparing machine learning versus human learning in predicting glass-forming ability of metallic glasses

The limited performance of ML model originates from the inability to accurately represent alloy features through elemental features, showing that physical insights about mixing behavior are required to develop predictable ML models.