Completed Superseeds & Seeds

Seed – Characterization of Electron Transport and Luminescence of Corroded Metal Alloys

Funding Period: September 1, 2020 – March 31, 2022

Principal Investigators

Jennifer Choy
Assistant Professor, Engineering Physics

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

Students & Postdocs

Ricardo Vidrio

Maryam Zahedian

Hongliang Zhang

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

Co-Investigators

Stephan Rudykh
Assistant Professor, Mechanical Engineering

Ramathasan Thevamaran
Assistant Professor, Engineering Physics

Students & Postdocs

Jizhe Cai

Yuhai Xiang

Materials Systems for Controlling Extra- and Intracellular Assembly and Function

Funding Period: April 1, 2019 – August 31, 2020

Principal Investigator

Wendy Crone
Professor, Engineering Physics
crone@engr.wisc.edu

Co-Investigators

Timothy Kamp
Professor, Medicine/Cell and Regenerative Biology
Wisconsin Institute for Medical Research

Lih-Sheng Turng
Professor, Mechanical Engineering

Students

Alana Stempien, astempien@wisc.edu

Molecularly Doped Topological Photonic Materials

Funding Period: April 1, 2019 – August 31, 2020

Principal Investigator

Randall Goldsmith
Associate Professor, Chemistry

Co-Investigators

Paul Campagnola
Professor, Biomedical Engineering

Zongfu Yu
Associate Professor, Electrical & Computer Engineering

Students & Postdocs

  • Samuel Alkmin, alkmin@wisc.edu
  • Michael Mattei, mattei2@wisc.edu
  • Lei Ying, lying8@wisc.edu
  • Ming Zhou, mzhou34@wisc.edu

Synthetic Soft Matter Inspired by Behaviors of Bacterial Communities

Funding Period: April 1, 2019 – August 31, 2020

Principal Investigators

David Lynn
Professor, Chemical and Biological Engineering

Helen Blackwell
Professor, Chemical and Biological Engineering

Co-Investigator

Reid Van Lehn
Assistant Professor, Chemical and Biological Engineering

Students & Postdocs

  • Harshit Argawal, hagarwal3@wisc.edu
  • Lawrence Chen, lmchen@wisc.edu
  • Curran Gahan, cgahan@wisc.edu
  • Kayleigh Nyffleler, nyffeler@chem.wisc.edu
  • Benjamin Ortiz, bortiz@wisc.edu
  • Samarthaben Patel, spatel46@wisc.edu
  • Thomas Polaske, polaske@wisc.edu
  • Fengrui Wang, fwang82@wisc.edu
  • Korbin West, khwest@wisc.edu
  • Ke Zhao, kzhao29@wisc.edu

Potential Energy Landscape, Two-Level System and Boson Peak in Silica

Funding Period: April 1, 2019 – August 31, 2020

Principal Investigator

Bu Wang
Asisstant Professor, Civil & Environmental Engineering
bu.wang@wisc.edu

Co-Investigator

Isabel Szlufarska
Professor, Materials Science and Engineering

Students

Zheng Yu, zheng.yu@wisc.edu

Machine Learning Algorithms for High-Throughput Materials Data

Funding Period: April 1, 2019 – August 31, 2020

Principal Investigator

Victor Zavala
Associate Professor, Chemical and Biological Engineering
victor.zavala@wisc.edu

Co-Investigator

Reid Van Lehn
Assistant Professor, Chemical and Biological Engineering

Students

  • Shengli Jiang, sjiang87@wisc.edu
  • Alexander Smith, adsmith23@wisc.edu

Complex Oxides Nanomembranes: Synthesis, Assembly, and Order-from-Disorder Transitions in Confined Geometries

Funding Period: April 1, 2019 – January 31, 2020

Principal Investigators

Francesca Cavallo
Professor, Electrical and Computer Engineering

Co-Investigator

Christoph Deneke
Professor, Applied Physics, Universidade Estadual de Campinas – UNICAMP

Students

  • Divya Jioty Prakash, University of New Mexico, dprakash@unm.edu
  • Vijay Saradhi Mangu, University of New Mexico, vjsaradhimangu@unm.edu

Seed Highlights

  • (2022) Spray-on “SLIPS” and Controlled release “SNIPS”: New Designs for Slippery Antifouling Materials

    Coatings that prevent fouling are critical in commercial, industrial, and healthcare contexts. Wisconsin MRSEC researchers have developed new spray-based methods to make nanoporous water-repelling films and spray-on ‘slippery liquid-infused porous surfaces’ (SLIPS). These coatings are antifouling to a range of substances and microorganisms and can be produced using scalable, manufacturing-compatible methods. They also developed new antifouling ‘slippery nanoemulsion-infused porous’ (SNIPS) that use water-in-oil nanoemulsions to slowly release encapsulated cargo.

  • (2022) An Underwater Topological Waveguide at MHz Frequencies

    Concepts of topology recently have been brought to bear on materials designed to control sound waves. Sound wavelengths are much longer than light, making acoustic materials easier to synthesize and their behavior easier to measure. Wisconsin MRSEC researchers are using topological acoustic materials to explore topological physics and enable applications in sensing, communication, and energy transport.

  • (2021) Controlling Waves with 3D Printed Materials

    Materials with a repetitive pattern the same size as the wavelength of a wave can be used to control the wave, causing it to bend, perfectly reflect or transmit, or even turn around corners. Where different patterns meet, even more exotic behavior occurs, including making highways for light or sound that only travel in one direction or where the waves cannot be dissipated. Synthesizing such materials is a major challenge, which  Wisconsin MRSEC researchers have met by adapting a family of 3D printing techniques.

  • (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.

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