Superseeds & Seeds

Superseed – Defects in 3D Topological Photonic and Acoustic Materials: Challenges Across Length Scales

Funding Period: September 1, 2020 – August 31, 2021

Principal Investigators

Randall Goldsmith
Associate Professor, Chemistry
Chu Ma
Professor, Biomedical Engineering


Paul Capagnola
Professor, Biomedical Engineering and Medical Physics
Amalio Fernández-Pacheco
Associate Professor, University of Glasgow, School of Physics & Astronomy
Tim Osswald
Professor, Mechanical Engineering
Zongfu Yu
Associate Professor, Electrical and Computer Engineering

Students & Postdocs

Brandon Hacha Boyuan Liu Michael Mattei
Gerardo Mazzei Capote Camila Montoya Ospina Alec Redmann
Emily Shelton Luka Skoric Michael Wang

Superseed – Validation of Soft Composite Characterization via Microcavitation and Correlation with Macroscopic Mechanical Behavior

Funding Period: September 1, 2020 – August 31, 2021

Principal Investigators

Andrew Boydston
Associate Professor, Chemistry
Padma Gopalan
Professor, Materials Science and Engineering


Stephan Rudykh
Assistant Professor, Mechanical Engineering
Ramathasan Thevamaran
Assistant Professor, Engineering Physics

Students & Postdocs

Jizhe Cai Yuhai Xiang


Seed – Characterization of Materials in Extreme Environments Using Quantum Probes

Funding Period: September 1, 2020 – August 31, 2021

Principal Investigators

Jennifer Choy
Assistant Professor, Engineering Physics
Adrien Couet
Assistant Professor, Engineering Physics and Materials Science and Engineering


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Seed Highlights

  • (2020) Energy Transfer Inside of a Topological Photonic Materials

    The Wisconsin MRSEC has shown that molecules inside in a type of topological photonic material called a Weyl crystal can exchange energy over much larger distances. The intricate twisting structure of the material uses light to connect one molecule to others much farther away. Developing photonic Weyl crystals may contribute to more efficient LEDs and solar cells and improve molecular sensors.

  • (2020) Machine Learning Algorithms

    The Wisconsin MRSEC has developed machine learning techniques that enable the design of new toxin sensors using liquid crystal droplets that respond to the presence of different bacterial toxins and at extremely low concentrations by changing shape and appearance. Machine learning enables computers to automatically analyze the droplet responses to measure toxin concentration and type automatically at high accuracy. More generally, these results demonstrate that the machine learning approach can quickly extract valuable information from complex datasets.

  • Newly Awarded Superseed and Seed Projects Will Forge Research Paths for MRSEC

    Two Superseed projects and one Seed project have been awarded funding to pursue research as part of the Wisconsin Materials Research Science and Education Center (MRSEC). The collaborative Superseed and Seed projects will enhance the ongoing materials research of the Center and support the exploration of transformative new directions.

  • Calls for Seed and Superseed Proposals Funded by MRSEC

    The Wisconsin Materials Research Science and Engineering Center (MRSEC) seeks proposals for interdisciplinary, collaborative Superseed and Seed projects.

  • (2019) Design Rules for Soft Materials with Integrated Natural and Synthetic Building Blocks

    Bacteria communicate via molecular signals that they produce in high concentrations. Bacterial communication promotes the formation of biofilms that can be harmful to humans and costly to industry. We have shown that collections of individual bacterial signaling molecules interact in water to form soft materials (“self-assemble”) with spherical, layered, or cylindrical structures. Simulation images showing the formation of a spherical structure (“micelle”) are shown with corresponding experimental images.

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