Simulations Demonstrate that Nanoparticles Can Self-Assemble into Ordered Structures

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Researchers at the Nanomanufacturing Systems for Mobile Computing and Mobile Energy Technologies (NASCENT) center, an NSF-funded Nanosystems Engineering Research Center (NERC) headquartered at The University of Texas at Austin, have demonstrated computationally that a physical pattern produced by lithography can directly self-assemble rectangular nanoparticles into aligned, ordered structures.


Nanoparticles can by synthesized with shapes, sizes, and chemical functionality, but assemble with a high defect rate when left alone. The simulations demonstrate that efficient directed self-assembly of particles is possible, provided the right combinations of the size ratio of the particle and the size of the template are chosen.


Little is known about how to self-assemble particles on surfaces with precision. The high defect rate of current techniques makes it very difficult to arrange nano-particles with high precision over large areas in desired 2D patterns that could be used for bit-patterned magnetic media, photonics, or as patterning masks.

The new simulations demonstrated that directed self-assembly is a viable route to arrange block polymers on surfaces with desired structures. The computations used density functional theory and grand canonical Monte Carlo to rapidly and relatively inexpensively explore the parameter that directs self-assembly of particles to achieve desirable structures. Pre-patterning the surface at length scales an order of magnitude larger than the desired particle spacing provides efficient directed self-assembly of the particles.

The simulations demonstrated that rectangular particles can be ordered using graphoepitaxy. This computational tool has been used to understand how to guide the placement of spherical and rectangular particles. The researchers are developing experiments guided by the simulations to further demonstrate the process.