(164b) Impact of Confinement on Directed Self-Assembly of Sub 10 Nm Particles into Textured Substrates

There has been a great deal of interest in nanomanufacturing using directed self-assembly of sub 10 nm particles into ordered structures. Such structures have potential applications in various emerging fields such as nanobiotechnology, nanoelectronics, nanosensors, and heterogeneous catalysis. In these applications, conventional pattering techniques have already reached their limits in resolution. However, strategically placing sub 10 nm particles as active agents on textured substrates could potentially make devices with enhanced functionalities. Using coarse-grained molecular dynamics simulations of directed self-assembly with flow field, we find an optimal nanoparticle density at which maximum yield is achieved. Regardless of density, we also find that confinement generally increases the deposition yield. Our simulations suggest that deviation from this optimal density will decrease the yield. Accounting for the nanoparticle density and degree of confinement, we find a direct correlation between these two parameters and yield. Our simulations indicate that high density gives rise to large order parameter, suggesting that nanoparticles are energetically less favorable to get adsorbed on the surface. On the other hand, at low density, nanoparticles are entropically less favorable to get adsorbed either. These results suggest that confinement before directed self-assembly plays a significant role in deposition yield. Overall, our computational platform offers an innovative approach for fundamental understanding of the dynamics of directed self-assembly of sub 10 nm particles into textured surfaces with single particle positioning.