Surface roughening effects on nanoparticle superlattice morphology: kinetic Monte Carlo and mean-field approaches.

Nanoparticle self-assembly has emerged as an interesting bottom-up materials design technique with applications ranging across medicine, analytical methods, and energetics. The large variety of molecular and synthetic building blocks suggests a potential for creating materials with targeted properties. However, many precursors and reaction conditions lead to difficult-to-predict products and an unclear set of design principles. In this work, we consider surface roughness effects and investigate their impact on cubic nanoparticle superlattice morphology. Experimental studies show low surface coverage 3-dimensional cubic superlattices when nanoparticles self-assemble on rough substrates and high surface coverage 2-dimensional superlattices on smooth substrates. Through control of the substrate roughness, on-lattice kinetic Monte Carlo simulations reveal nanoparticle clustering in 3-dimensional growths and rationalize the origin of grain boundaries in 2-dimensional growths. Additionally, a mean-field theory effectively captures the dynamics of 2- and 3-dimensional growth on different surfaces and qualitatively agrees with simulation results.