Kent State University has received nearly $1 million for scientific research and technology.
An $831,761 grant from the National Science Foundation will be used to teach students to “study ferroelectric nematic materials,” which are liquid crystals used in LCD displays, for example, and how they can be used in other apps.
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Ferroelectric nematic materials are useful for generating green electricity and have applications in so-called “soft robotics”, a subfield of robotics concerning the design of robots using materials that are more flexible than hard plastic , ceramic or steel.
Antal Jakli, a professor in the Kent State Department of Physics and the Institute for Advanced Materials and Liquid Crystals, provided an abstract written by himself and his team that reads: “Studies of “Research team on these materials aim to demonstrate new technological solutions for motion control and energy conversion,” reads the abstract.
Additionally, the project aims to educate students in physics, chemistry and materials science at every stage of their development. They specifically focused on underrepresented groups through the McNair Scholar and Research Experience for Undergraduates programs.
The REU provides aspiring scientists with training in a variety of disciplines and gives them the opportunity to pursue careers in STEM fields through faculty mentorship and academic research.
The second grant, valued at $101,020, will be used to expand students’ knowledge of the use of graph analysis in various research, specifically “collaborative research in a probabilistic end-to-end graph management system end that will provide learning, representation, aggregation and analysis.”
Probabilistic graphs measure the probability of a certain outcome under certain conditions.
The research conducted with this funding is a collaborative effort between Dr. Xiaofei Zhang of the University of Memphis and Dr. Xiang Lia and Dr. Qiang Guan, both of Kent State University.
Lian, associate professor of computer science at KSU explained some of the applications of probabilistic graphs in an email to the Record-Courier.
“There are many scenarios in which we may encounter probabilistic graphs,” Lian wrote. “For example, social networks where a user can influence his friends (or friends of friends) with certain odds/probabilities, road network graphs where traffic conditions are uncertain, knowledge graphs where relationships between resources are missing or inferred by some learning models, biological networks where genes or proteins interact with each other with certain probabilities.”
Their work, he wrote, has the end goal of developing an open-source software tool that researchers can use.
Contact reporter Derek Kreider at dkreider@gannett.com.
