Generalized nano-thermodynamic model for capturing size-dependent surface segregation in multi-metal alloy...

Area of Science:

• Materials Science
• Nanotechnology
• Computational Chemistry

Background:

• Multi-metal alloy nanoparticles (NPs) possess tunable properties for catalysis, electronics, and optics.
• Accurate prediction of elemental distribution in NPs is crucial for nanomaterial design but currently lacking.
• Existing models struggle to predict size-, shape-, and composition-dependent behavior.

Purpose of the Study:

• Introduce a novel nano-thermodynamic model to predict elemental distribution in alloy NPs.
• Develop a method to accurately forecast surface segregation as a function of NP size and composition.
• Enable the creation of thermodynamic tables for predicting nanomaterial phase diagrams.

Main Methods:

• Developed a nano-thermodynamic model based on distribution coefficients (Δ).
• Utilized Monte Carlo simulations in the canonical ensemble with an embedded atom method (EAM) potential for calculations.
• Validated the model using ternary alloy systems like Au-Pt-Pd, Ag-Au-Pd, and Ni-Pt-Pd.

Main Results:

• The distribution coefficient (Δ) becomes independent of NP size for sizes beyond 2 nm.
• Complex size-dependent segregation behavior is observed for NPs in the 2-6 nm range.
• Segregation shows weaker size-dependence for NPs larger than 6 nm.

Conclusions:

• The developed nano-thermodynamic model accurately predicts elemental distribution and surface segregation in alloy NPs.
• The size-independence of Δ beyond 2 nm simplifies nanomaterial phase diagram prediction.
• The findings facilitate the rational design of multi-metal alloy nanoparticles with tailored properties.