Eco-friendly Additive Manufacturing of Ceramics via Electron-Stimulated Vacancy Diffusion

Transition-metal-carbide (TMC) ceramics, such as zirconium carbide (ZrC) or tungsten carbide (WC), are critical high-temperature materials composed of transition metals and carbon atoms. They exhibit superior mechanical, chemical, and thermal properties and have thus attracted vast interest in harsh environment applications, such as cutting tools, engine parts, heat exchanger, and nuclear fuel cladding. Additive manufacturing (AM) offers the potential to fabricate TMC parts with much more complex geometries for their extensive applications but entails substantial carbon emissions and energy consumption. This Future Manufacturing project supports research that will lead to a novel AM process for TMC ceramics, named vacancy-assisted jet fusion (VJF), which can potentially reduce the processing temperatures and carbon emissions associated with existing AM processes for TMCs. The project will engage K-12 and underrepresented minority students through outreach activities such as ceramic art design program and the Science Undergraduate Laboratory Internships (SULI) program to help raise awareness of ceramic AM technologies among young people in Iowa and attract a diverse pipeline of workforce to the manufacturing industries and beyond. The VJF process fabricates intricate TMC parts through the combined use of locally induced crystal vacancies and layerwise triggered electron flow. The objective of this research is to determine the effects of locally induced crystal vacancies and layerwise triggered electron flow on the densification of TMC particles. This project utilizes an integrated approach including high throughput printing, multi-scale material characterizations, and machine learning (ML)-based material simulations to fill the critical knowledge gap in the effects of locally induced crystal vacancies and layerwise trigger electron flow on the densification of TMC particles. Specifically, this project will (1) quantify the effects of layerwise triggered electron flow on the diffusion of locally induced crystal vacancies using high-throughput, multi-nozzle inkjet printing and advanced microscopic techniques, (2) uncover the underlying mechanisms that govern the diffusion of locally induced crystal vacancies using large-scale atomistic simulations via machine learning force field (ML-FF), and (3) determine the effects of vacancy diffusion on densification kinetics of TMC particles utilizing meso-scale characterization tools. 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.