Predicting the effect of chain-length mismatch on phase separation in noble metal nanoparticle monolayers with chemically mismatched ligands

Abstract

Nanoparticles (NPs) protected with a ligand monolayer hold promise for a wide variety of applications, from photonics and catalysis to drug delivery and biosensing. Monolayers that include a mixture of ligand types can have multiple chemical functionalities and may also self-assemble into advantageous patterns. Previous work has shown that both chemical and length mismatches among these surface ligands influence phase separation. In this work, we examine the interplay between these driving forces, first by using our previously-developed configurationally-biased Monte Carlo (CBMC) algorithm to predict, then by using our matrix assisted laser desorption/ionization mass spectrometry (MALDI-MS) technique to experimentally probe, the surface morphologies of a series of two-ligand mixtures on the surfaces of ultrasmall silver NPs. Specifically, we examine three such mixtures, each of which has the same chemical mismatch (consisting of a hydrophobic alkanethiol and a hydrophilic mercapto-alcohol), but varying degrees of chain-length mismatch. This delicate balance between chemical and length mismatches provides a challenging test for our CBMC prediction algorithm. Even so, the simulations are able to quantitatively predict the MALDI-MS results for all three ligand mixtures, while also providing atomic-scale details from the equilibrated ligand structures, such as patch sizes and co-crystallization patterns. The resulting monolayer morphologies range from randomly-mixed to Janus-like, demonstrating that chain-length modifications are an effective way to tune monolayer morphology without needing to alter chemical functionalities.