(162c) Thermodynamic and Data-Centric Computational Methods to Guide Nanoparticle Design | AIChE

(162c) Thermodynamic and Data-Centric Computational Methods to Guide Nanoparticle Design


Van Lehn, R. - Presenter, University of Wisconsin-Madison
Dallin, B. C., University of Wisconsin-Madison
Chew, A. K., University of Wisconsin
Kelkar, A., University of Wisconsin-Madison
Functionalized, monolayer-protected nanoparticles (NPs) are versatile materials with surface properties dictated by the composition of the protecting ligand monolayer. Ultrasmall gold NPs (<10 nm in diameter) are of particular interest for a range of biomedical applications, including targeted drug delivery, biosensing, and bioimaging, because their small size enables unique interactions with serum proteins and the cell membrane. However, structure-property-interaction relationships correlating NP compositions to their biological interactions remain unclear and can be challenging to determine experimentally. NP design is particularly confounded by the large number of design parameters, including core size, ligand physical and chemical properties, and ligand grafting densities, that can influence NP surface properties and thus behavior in biological environments.

Here, we present computational approaches from our group that can guide the design of NPs for biomedical applications. We will first discuss the use of atomistic molecular dynamics simulations to gain physical insight into the effect of specific components of the NP design space on NP surface properties. In particular, we show that ligand fluctuations promoted by the length of the ligand backbone or the NP core size result in spatial variations in surface properties, even for uniform monolayer coatings, and illustrate how these spatial variations influence the magnitude of water-mediated interactions. We then discuss how the specific chemical properties of ligand end groups influence these fluctuations and resulting interactions. To complement these thermodynamic studies, we further apply deep learning to rapidly screen NP surface properties as a function of monolayer composition using minimal simulation input. In conjunction, these thermodynamic and data-centric techniques provide new computational tools that can be leveraged to tune NP surface properties and provide fundamental insight into behavior at nanomaterial interfaces.