Granular materials are ubiquitous and play an important role in many industries. Their mechanical response is determined by a number of factors including particle size distribution, packing arrangement, and the characteristics of the grains themselves. Control of these attributes provides opportunities to functionalize them.
Current research in Purdue’s geotechnical engineering lab is exploring the use of soft coatings to control the stiffness and attenuation properties of granular assemblies. A method has been developed for coating model grains (glass beads ~ 4 mm I diameter) with a silicone-based paint. Preliminary experiments using the resonant column apparatus indicate that a silicone coating as thin as 10-15 microns is effective in significant in enhancing the damping characteristics of the material.

Responsibilities of the SURF student will include:
- Performing coating experiments on both the model beads and natural granular materials (fine gravels)
- Characterizing the coated grains through microscopy and measurements of the coefficient of rebound
- Measuring the packing characteristics of the grains as a function of different thicknesses of the coating
- Possibly exploring coating with an additional material.

West Lafayette

Engineering the Built Environment, Material Processing and Characterization

• Materials Engineering
• Civil Engineering
• Mechanical Engineering
• Chemical Engineering
• Agricultural and Bio Engineering

Lyles School of Civil Engineering

Marika Santagata

The project aims to utilize Neural Networks (NN)-based segmentation tools for analyzing the time series of 3D membrane-labeled microscopy image data in whole-mount zebrafish embryos. At the onset of embryonic development, the epiboly process involves the ectoderm and mesodermal progenitor cells spreading and thinning to encompass the entire embryo. Quantifiying the cell shape changes helps to understand how the mechanical forces drive the epiboly prograss. Live images of membrane-labeled cells in the enveloping layer are imaged using confocal microscopy. The student’s role in this project is to develop a Python-based image analysis framework by using the Neural Network (NN)-based 3D Membrane Segmentation tool, CellPose, to segment 3D images, trace the cells' movement, and quantitatively analyze the cell shape changes during epiboly.

Big Data/Machine Learning, Cellular Biology

• No Major Restriction

Weldon School of Biomedical Engineering

David Umulis

This is a test project description

Human Factors, Internet of Things (IoT)

International, DACA Status

• No Major Restriction

Any, this is a test

Elmore Family School of Electrical and Computer Engineering

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