## Publicly Accessible Penn Dissertations

2022

Dissertation

#### Degree Name

Doctor of Philosophy (PhD)

Chemical and Biomolecular Engineering

Robert A. Riggleman

Zahra Fakhraai

#### Abstract

Glasses of small organic molecules are a ubiquitous material type of wide interest due to their unique amorphous packings. However, their properties can vary widely based on preparation method, aging time, or type of molecule. Stable glasses, prepared via physical vapor-deposition, have been shown to exhibit properties equivalent to those that been aged for thousands of years. Molecular dynamics simulations of coarse-grained molecules provide a simple model for closely examining the relevant properties of glasses, both traditionally liquid-quenched and vapor-deposited. In this dissertation, several projects are presented focusing on systematic changes to inter-molecular interactions and intra-molecular degrees of freedom and how they impact glass properties $\textit{in silico}$. In Chapter 2, we study the effects of inter-molecular interactions and microstructure formation on vapor-deposited glass films, using a coarse-grained model of molecules with fluorinated tails. By altering the length of the tail, we can tune the degree of microstructure formation, and we observe how this affects vapor-deposited glass stability, while also proposing, and supporting a mechanism for this behavior. In Chapter 3, models of organic molecules are developed focusing on the strength of the rotational barriers placed on their side groups in order to study intra-molecular degrees of freedom. Here we see the effect this has on vapor-deposited glass stability and make connections to surface mobility and the depth of the mobile region. In Chapter 4, vapor-deposited and liquid-quenched glasses are tested for their mechanical response to shear. The large differences in their properties, as well as the length scales over which they take place, are correlated with local particle mobility. Finally, in Chapter 5, the same molecular models are used to tune fragility behavior. The machine-learned structural property, softness, is implemented for a molecular system for the first time and provides new insight into the origins of glass fragility.

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