New tool enables unprecedented modeling of magnetic nanoparticles


Researchers at North Carolina State University have developed a new computational tool that allows users to perform multifunctional magnetic nanoparticle simulations in unprecedented detail. This breakthrough paves the way for further work to develop magnetic nanoparticles for use in applications ranging from drug delivery to sensing technologies.

“Self-assembled magnetic nanoparticles, or MNPs, have many desirable properties,” says Yaroslava Yingling, corresponding author of a paper on the work and professor emeritus of materials science and engineering at NC State. “But it has been difficult to study them, because computer models have struggled to account for all the forces that can influence these materials. MNPs are subject to a complicated interaction between external magnetic fields and van der Waals, electrostatic, dipolar, steric and hydrodynamic interactions.

Many applications of MNPs require an understanding of how nanoparticles will behave in complex environments, such as using MNPs to deliver a specific protein or drug molecule to a targeted cancer-affected cell using fields external magnets. In these cases, it is important to be able to accurately model how MNPs will respond to different chemical environments. Previous computer modeling techniques that examined MNPs were unable to account for all of the chemical interactions that MNPs undergo in a given colloidal or biological environment, instead focusing primarily on physical interactions.

“These chemical interactions may play an important role in the functionality of MNPs and how they respond to their environment,” says Akhlak Ul-Mahmood, the paper’s first author and Ph.D. student at NC State. “And detailed computer modeling of MNPs is important because the models give us an efficient route to designing MNPs for specific applications.

“That’s why we developed a method that accounts for all of these interactions and created open-source software that the materials science community can use to implement it.”

“We are optimistic that this will facilitate important new research on multifunctional MNPs,” Yingling said.

To demonstrate the accuracy of the new tool, the researchers focused on magnetite nanoparticles functionalized with an oleic acid ligand, which have already been studied and are well understood.

“We found that our tool’s predictions of the behavior and properties of these nanoparticles were consistent with what we know about these nanoparticles based on experimental observations,” Mahmood says.

Moreover, the model also offered new insights into the behavior of these MNPs during self-assembly.

“We believe the demonstration not only shows that our tool works, but also highlights the additional value it can provide by helping us understand how best to design these materials to take advantage of their properties,” Yingling says.

The article, “All-Atom Simulation Method for Zeeman Alignment and Dipolar Assembly of Magnetic Nanoparticles”, is published in the Journal of Chemical Theory and Computation. The work was done in collaboration with the experimental group of Joe Tracy, professor of materials science and engineering at NC State, and with support from the National Science Foundation, under grant number CMMI-1763025.


Note to editors: The summary of the study follows.

“All-atom simulation method for Zeeman alignment and dipole assembly of magnetic nanoparticles”

Authors: Akhlak U. Mahmood and Yaroslava G. Yingling, North Carolina State University

Posted: March 10, Journal of Chemical Theory and Computation

DO I: 10.1021/acs.jctc.1c01253

Abstract: Magnetic nanoparticles (MNPs) can organize into new structures in solutions with excellent order and unique geometries. However, studies of self-assembly of smaller MNPs are challenging due to complicated interplay between external magnetic fields and van der Waals, electrostatic, dipole, steric, and hydrodynamic interactions. Here, we present a novel all-atom molecular dynamics (DMA) simulation method to enable detailed studies of the dynamics, self-assembly, structure, and properties of MNPs as a function of size and the shape of the nucleus, the chemistry of the ligand, the properties of the solvent and the external field. . We demonstrate the use and efficiency of the model by simulating the self-assembly of magnetite functionalized by an oleic acid ligand (Fe3O4) nanoparticles, in spherical and cubic shapes, in rings, lines, chains and clusters under a uniform external magnetic field. We found that long-range electrostatic interactions can promote chain formation on a ring, ligands promote MNP cluster growth, and solvent can reduce MNP rotational diffusion. The algorithm has been parallelized to take advantage of multiple processors of a modern computer and can be used as a plug-in for popular simulation software LAMMPS to study the behavior of small magnetic nanoparticles and better understand the physics and chemistry of different magnetic assembly process with atomic details.

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