Researchers at North Carolina State University have developed a new computational tool that allows users to run simulations of multifunctional magnetic nanoparticles in unprecedented detail. The progress paves the way for new work aimed at developing magnetic nanoparticles for use in applications ranging from drug delivery to sensor technologies.
“Self-assembling magnetic nanoparticles, or MNPs, have many desirable properties,” says Yaroslava Yingling, corresponding author of an article on the work and distinguished professor of materials science and engineering at NC State. “But studying them was challenging because computer models had trouble accounting for all the forces that can affect these materials. MNPs are subject to a complicated interplay between external magnetic fields and van der Waals, electrostatic, dipolar, steric, and hydrodynamic interactions.”
Many applications of MNPs require an understanding of how the nanoparticles behave in complex environments such as B. Using MNPs to deliver a specific protein or drug molecule to a targeted cancerous cell using external magnetic fields. In these cases, it is important to be able to accurately model how MNPs respond to different chemical environments. Previous computational modeling techniques that addressed MNPs were unable to account for all of the chemical interactions that MNPs experience in a specific colloidal or biological environment and instead focused mainly on physical interactions.
“These chemical interactions can play an important role in the functionality of the MNPs and how they respond to their environment,” says Akhlak Ul-Mahmood, first author of the work and Ph.D. student at NC State. “And detailed computational modeling of MNPs is important because models provide us with an efficient way to design MNPs for specific applications.
“So we devised a methodology that takes all of these interactions into account and developed open-source software that the materials science community can use to implement it.”
“We are optimistic that this will enable important new research into multifunctional MNPs,” says Yingling.
To demonstrate the accuracy of the new tool, researchers focused on oleic acid-ligated magnetite nanoparticles, which have already been studied and are well understood.
“We found that our tool’s predictions about the behavior and properties of these nanoparticles were consistent with what we know about these nanoparticles from experimental observations,” says Mahmood.
In addition, 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 added value it can offer by helping us understand how best to engineer these materials to take advantage of their properties.” , says Yingling.
The paper, “All-atom simulation method for Zeeman alignment and dipolar assembly of magnetic nanoparticles“, appears in Journal of Chemical Theory and Calculation. The work was performed in collaboration with the experimental group of Joe Tracy, a professor of materials science and engineering at NC State, and with support from the National Science Foundation under grant number CMMI-1763025.
Note to the editor: The study summary follows.
“All-Atom Simulation Method for Zeeman Alignment and Dipolar Assembly of Magnetic Nanoparticles”
authors: Akhlak U Mahmood and Yaroslava G Yingling, North Carolina State University
Released: March 10, Journal of Chemical Theory and Calculation
Abstract: Magnetic nanoparticles (MNPs) can self-assemble into novel structures in solutions with excellent order and unique geometries. However, self-assembly studies of smaller MNPs are challenging due to a complicated interplay between external magnetic fields and van der Waals, electrostatic, dipolar, steric, and hydrodynamic interactions. Here we present a novel all-atom molecular dynamics (AMD) simulation method to enable detailed studies of the dynamics, self-assembly, structure, and properties of MNPs as a function of core sizes and shapes, ligand chemistry, solvent properties, and external field. We demonstrate the use and effectiveness of the model by studying the self-assembly of oleic acid-ligated magnetite (Fe3O4) nanoparticles with spherical and cubic shapes under a uniform external magnetic field into rings, lines, chains, and clusters. We found that the long-range electrostatic interactions can favor the formation of a chain over a ring, the ligands can promote MNP cluster growth, and the solvent can decrease the rotational diffusion of the MNPs. The algorithm has been parallelized to take advantage of multiple processors in a modern computer and can be used as a plugin for the popular simulation software LAMMPS to study the behavior of small magnetic nanoparticles and gain insight into the physics and chemistry of various magnetic assembly processes in atomic detail .