Gonzaga University

NSF Awards · FY 2026 · 2026-08

This project will conduct experiments on “low-wear polymer composite” materials. These materials consist of particles in a polymer matrix. They are often used to reduce friction and mechanical damage (called “wear”) between sliding surfaces. During sliding, low-wear composites form protective films on the sliding surfaces. The project will investigate how polymers, particles, and opposing surface interact to create these protective films. Results will advance predictive design of polymer composites which decrease friction and wear. Friction wastes energy, while wear causes mechanical failures. Therefore, improvements in low-wear materials will benefit a wide range of manufacturing applications. These include bearings in cars, gears in machinery, or hip implants. Further benefits will come from educational activities developed during this project. These include training undergraduate students in advanced materials testing and teaching middle and high school students about the science and engineering of materials, friction, and wear. This research will develop cause-effect relationships between composite structures and their properties. It will focus on how the wear behavior depends not just on the mechanical properties of the filler particles, matrix, and counterface material, but also chemical properties. The novelty of proposed research is that interactions between particles, matrix, and the counterface will be investigated across a wide range of length scales. This work will be conducted on model thermoplastic polymers and a select range of filler/counterface chemistries that promote low and high wear. These measurements will be enabled through custom macroscale tribometry, nanoscale mechanics using scanning probe microscopy, as well as multi-length scale spectroscopic characterization. The scientific outcome of the proposed research will be predictive design rules for mitigating wear in polymer composites. This project represents a transformative step toward predictive design of tribological polymer composites. The results will impact the design and manufacturing of mechanical components. Additionally the project will create new outreach opportunities for Gonzaga students and the broader Spokane community in the area of materials education and scientific literacy. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-10

Cybersecurity defense operations (cyber-ops) are in dire need of effective and expedited learning programs for practicing professionals to infuse data analytics (i.e., data science, artificial intelligence, and machine learning) into their day-to-day tasks. To combat the evolving threat landscape, effective cyber-ops often involve a team of cybersecurity analysts and engineers with complementary expertise. The analysts need effective, usable, and potentially customized data analytics, which are developed by or in collaboration with the engineers. Currently, in-service training for cybersecurity analysts and engineers who need to use data analytics for cyber-ops are limited. No existing program addresses the need to develop the collaborative mindsets and practices that are necessary to work effectively together. This project aims to develop an innovative dual-track learning program for working cybersecurity analysts and engineers that leverages role-playing within a simulated organization and involves tasks that are specific to each group as well as tasks where they need to work together as a team. This innovative design will help promote collaboration while also enhancing learning of specific data analytic knowledge and skills needed by each type of professional in a realistic work environment. The program features a combination of remote learning modules and tasks, team coach sessions, and team-based incident response exercises. Progressively deeper learning about data analytics will occur as the scale and complexity of the cyber-op tasks assigned to the participants, as members of a simulated organization, are gradually increased. The program was designed in collaboration with education researchers. The design is informed by education theories and principles, including Identity Theory, Project-based Design, Understanding by Design, and Universal Design for Learning. The program’s design will also build on prior successful experiences with entry-level cybersecurity bootcamps employing simulations, cybersecurity competitions, and teaching and deployment of research advances for cyber-ops. The program will be refined through three iterations following a Design-based Research approach, and its effects evaluated by an external evaluation team. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.