Monte Carlo simulations of surface segregation to discover new hydrogen separation membranes

Surface compositions play a predominant role in the efficiency and lifetime of membranes and catalysts. The surface composition can change during operation due to segregation, thus controlling and predicting the surface composition is essential. Computational modelling can aid in predicting alloy stability, along with designing surface alloys and near-surface alloys that can outperform existing materials. In this work, a computational model to predict surface segregation in ternary alloys is developed. The model, based on Miedema's semi-empirical model and Monte Carlo simulations, enables to predict longand short-range ordering in the surface and subsurface layers. It is used to screen a vast range of alloy compositions to design a novel ternary Pd-based material for H2 separation membranes. The addition of specific amounts of Cu and Zr to Pd is expected to reduce poisoning and enhance the permeability as compared to pure Pd.(c) 2022 The Authors. Published by Elsevier Ltd on behalf of Hydrogen Energy Publications LLC. This is an open access article under the CC BY license (http://creativecommons.org/ licenses/by/4.0/).

Automated summary

Surface compositions are crucial for the efficiency and lifespan of membranes and catalysts. Computational modeling can aid in predicting and controlling surface segregation, leading to the design of novel materials with improved performances. In this study, a computational model based on Miedema's model and Monte Carlo simulations is developed to predict surface segregation in ternary alloys. The model enables the screening of a wide range of alloy compositions and the design of a novel Pd-based ternary material for H2 separation membranes, expected to exhibit reduced poisoning and enhanced permeability compared to pure Pd.