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With Nanoparticles, Slower May Be Better

Molecular dynamics simulations provide unprecedented understanding of nanoparticle structure and symmetry

July 5, 2013

The last decade has seen a flurry of research and development involving nanoparticles — chemicals or objects in the 1-100 nanometer range. These tiny structures are of great scientific interest because they are effectively a bridge between bulk materials and atomic and molecular structures.

And thanks to their promise of tunability, nanoparticle-based composites are also of great commercial interest for applications ranging from medicine and manufacturing to energy and electronics. The enhanced electro-optical responses of metallic particles, for example, could result in the development of new photonic and plasmonic sensing devices, while magnetic nanoparticles have demonstrated potential for fast memory and storage devices.

Although the concept of using nanoparticles for such applications is well accepted, the ability to control and manipulate their behavior remains a challenge. Experimental studies have shown that nanoparticles tend to randomly aggregate into clusters or migrate to interfacial regions, resulting in the loss of tunability. So researchers are working on methods that will allow them to control how different kinds of nanoparticles assemble and disperse.

“We are mostly interested in how to assemble them into organized arrays,” said Gary Grest, a computational physicist at Sandia National Laboratories who is using NERSC supercomputers to run simulations that help explain nanoparticle behavior. “How do you control the interactions, the lattices and structures they build? How do you keep them dispersed? If you want to make, say, a photonics crystal, you want to get them arranged into a nice 2D or 3D array. You want a nice ordered grain crystal structure with no defects.”

Snapshots of a nanoparticle solution: (a) the equilibrated solution before the evaporation of solvent; (b) after 52% of the solvent had evaporated at a fixed rate; (c) after 52% of the solvent had evaporated into a vacuum ; (d) the same system as in (c), but the system had re-equilibrated after the evaporation was stopped. (e)–(h) Side views of the nanoparticle distribution for systems in (a)–(d), respectively; the dashed lines indicate the location of the liquid/vapor interface. (i)–(l) Top views of nanoparticles in the top layer for systems in (a)–(d).

 

Over the last two years he and his colleagues at Sandia have performed a series of simulations at NERSC using LAMMPS (Large-scale Atomic/Molecular Massively Parallel Simulator), a molecular dynamics code that is widely used on large-scale parallel machines such as NERSC’s Hopper and Edison systems. In 2014, the third year of their three-year ALCC grant, they will continue this research to further explore ways to control the symmetry of nanoparticle assemblies.

As part of this effort, Grest and Shengfeng Cheng, formerly a post-doctoral researcher at Sandia and now an assistant professor of physics at Virginia Tech, used millions of computing hours at NERSC to conduct the first large-scale molecular dynamics simulations of the evaporation-induced assembly of nanoparticles. Their findings were published in The Journal of Chemical Physics (JCP).

Evaporation-induced assembly of nanoparticles—also known as solvent annealing—is commonly used to organize nanoparticles into structures such as nanoclusters, rings, wires, stripes, films and superlattices, Grest explained. In this process, the nanoparticles are suspended in a solvent, the suspended solution is spread on a surface and the solvent is then evaporated. But few studies have looked at how the assembled structures are affected by the evaporation process, primarily because of the huge computing resources required to run numerical simulations of solvent effects on nanoparticles, Grest said.

“These are very large simulations – in our research, one set of simulations used 10 million hours alone and some ran for almost a year,” he said. “The issue is that you’ve got a nanoparticle that is only 5-10 nanometers in size, but even if you only simulate a few, you still need a large amount of solvent around them–hundreds of thousands to millions of atoms that you are following, and these are big particles that don’t move that fast. So NERSC’s facilities have been essential for these studies, allowing us to follow lots of particles for long periods of time.”

In the JCP study, the LAMMPS simulations revealed that the strength of the polymer/nanoparticle interaction controls the position of the nanoparticles at the interface after the solvent is removed. In particular, the researchers found that the slower the evaporation rate, the better the quality of the nanoparticle crystal.

“We found that by controlling the evaporation rate we could control how the nanoparticles organize and control the quality of the crystal structure in the arrays,” Grest said. “If you evaporate the solvent slowly enough, you can organize the nanoparticles nicely.”

Defining the role of specific interactions such as this in nanoparticles will help researchers design nanocomposites that can be integrated into large-scale devices without compromising the mechanical and optical features offered at the nanoscale level, he added.

“These computational methods provide an unprecedented understanding of the structure and symmetry of individual nanoparticles,” Grest said.


About NERSC and Berkeley Lab
The National Energy Research Scientific Computing Center (NERSC) is a U.S. Department of Energy Office of Science User Facility that serves as the primary high-performance computing center for scientific research sponsored by the Office of Science. Located at Lawrence Berkeley National Laboratory, the NERSC Center serves more than 7,000 scientists at national laboratories and universities researching a wide range of problems in combustion, climate modeling, fusion energy, materials science, physics, chemistry, computational biology, and other disciplines. Berkeley Lab is a DOE national laboratory located in Berkeley, California. It conducts unclassified scientific research and is managed by the University of California for the U.S. DOE Office of Science. »Learn more about computing sciences at Berkeley Lab.