Pd(n)Ag(4-n) and Pd(n)Pt(4-n) clusters on MgO (100): a density functional surface genetic algorithm investigation

Area of Science:

• Computational chemistry
• Surface science
• Materials science

Background:

• Subnanometer metal clusters exhibit unique electronic and catalytic properties.
• Understanding cluster-substrate interactions is crucial for designing novel materials.
• Noble metal clusters are of interest for catalysis and electronics.

Purpose of the Study:

• To optimize the global structure of noble metal tetramers on an MgO (100) surface.
• To investigate the effects of element identity and alloying on cluster stability and CO binding.
• To explore the electronic behavior of subnanometer clusters.

Main Methods:

• Utilized the surface mode of the Birmingham Cluster Genetic Algorithm (S-BCGA).
• Employed Generalized Gradient Approximation-Density Functional Theory (GGA-DFT) for calculations.
• Analyzed energetic comparisons and charge localization.

Main Results:

• Binding strengths to the MgO surface followed the order Pt > Pd > Ag.
• Mixed Pd-Ag and Pd-Pt clusters exhibited higher stability than pure clusters.
• CO adsorption site was predictable by electrophilicity, with weakened cluster-surface interaction upon CO binding.

Conclusions:

• Charge localization dictates cluster structure, CO binding, and surface site preferences.
• Electronic properties of subnanometer clusters are tunable, falling between molecular and metallic behaviors.
• Mixed noble metal clusters offer promising avenues for tailored material design.