Year: 2026
Type: article
Abstract: Electric propulsion spacecraft increasingly require alternative propellants such as krypton and argon due to xenon’s high cost, yet sputter yield data for these propellants remain limited for many spacecraft-relevant materials. This data scarcity makes empirical parameter fitting difficult, particularly near the low-energy threshold region that is important for plume-surface interaction modeling. We develop an LSS-informed Bayesian Markov Chain Monte Carlo framework for calibrating Eckstein sputter-yield parameters for krypton and argon systems. The approach uses Lindhard-Scharff-Schiøtt (LSS) collision theory to construct LSS-informed prior information from well-characterized xenon reference systems while retaining the available experimental data in the likelihood. Predictive behavior is evaluated through comparison with data-only MCMC baselines and retrospective withheld-data validation. The method is applied to 16 krypton and 6 argon target materials spanning metallic and covalent systems, providing a consistent set of LSS-informed Eckstein parameter fits for electric-propulsion sputter-yield modeling.
Source: Journal of Electric Propulsion
Authors: Henry Huh, David L. Bilyeu, John T. Yim
Language: English
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $1,490
OpenAlex ID: W7225108957