All-atom Molecular Dynamics simulation methods employing a well-tested intermolecular potential model, MM3 (Molecular Mechanics 3), demonstrate the propensity for diindenoperylene (DIP) molecules to insert between molecules of a self-assembled monolayer (SAM) during a deposition process intended to grow a thin film of this organic semiconductor molecule onto the surface of self-assembled monolayers. The tendency to insert between SAM molecules is fairly prevalent at normal growth temperatures and conditions, but is most strongly dependent on the density and the nature of the SAM. We posit the existence of an optimal density to favor surface adsorption over insertion for this system. DIP is less likely to insert in fluorinated SAMs, like FOTS (fluorooctatrichlorosilane), than its unfluorinated analog, OTS (octatrichlorosilane). It is also less likely to insert between shorter SAMs (e.g., less insertion in OTS than ODTS (octadecyltrichlorosilane)). Very short length, surface-coating molecules, like HDMS (hexamethyldisilazane), are more likely to scatter energetic incoming DIP molecules with little insertion on first impact (depending on the incident energy of the DIP molecule). Grazing angles of incidence of the depositing molecules generally favor surface adsorption, at least in the limit of low coverage, but are shown to be dependent on the nature of the SAM. The validity of these predictions is confirmed by comparison of the predicted sticking coefficients of DIP at a variety of incident energies on OTS, ODTS, and FOTS SAMs with results obtained experimentally by Desai et al. (2010) . The simulation predictions of the tendency of DIP to insert can be explained, in large part, in terms of binding energies between SAM and DIP molecules. However, we note that entropic and stochastic events play a role in the deposition outcomes. Preliminary studies of multiple deposition events, emulating growth, show an unexpected diffusion of DIP molecules inserted within the SAM matrix in a clear attempt of the DIP molecules to aggregate together. © 2011 Elsevier B.V.
Bibliographical noteKAUST Repository Item: Exported on 2020-10-01
Acknowledged KAUST grant number(s): KUS-C1-018-02
Acknowledgements: This publication was based on work supported by Award No. KUS-C1-018-02, made by King Abdullah University of Science and Technology (KAUST). The Engstrom research group at Cornell is thanked for access to their experimental data in advance of publication. Intel Corporation is thanked for the donation of computing resources.
This publication acknowledges KAUST support, but has no KAUST affiliated authors.