新工具允许磁性纳米颗粒的前所未有的建模
For Immediate Release
北卡罗来纳州立大学的研究人员开发了一种新的计算工具，允许用户在前所未有的细节中对多功能磁性纳米颗粒进行模拟。该预期为旨在开发磁性纳米颗粒的新工作，以用于从药物输送到传感器技术的应用中的新工作。
“Self-assembling magnetic nanoparticles, or MNPs, have a lot of desirable properties,” says Yaroslava Yingling, corresponding author of a paper on the work and a Distinguished Professor of Materials Science and Engineering at NC State. “But it has been challenging to study them, because computational models have struggled to account for all of the forces that can influence 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 will behave in complex environments, such as using MNPs to deliver a specific protein or drug molecule to a targeted cancer affected cell using external magnetic fields. In these cases, it is important to be able to accurately model how MNPs will respond to different chemical environments. Previous computational modeling techniques that looked at MNPs were unable to account for all of the chemical interactions MNPs experience in a given colloidal or biological environment, instead focusing primarily on physical interactions.
“Those 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 paper and a Ph.D. student at NC State. “And detailed computational modeling of MNPs is important because models offer an efficient path for us to engineer MNPs for specific applications.
“这就是为什么我们开发了一种占这些交互的方法，并创建了材料科学社区可以用来实施它的开源软件。”
“We’re optimistic that this will facilitate significant new research on multi-functional MNPs,” Yingling says.
To demonstrate the accuracy of the new tool, the researchers focused on oleic acid ligand-functionalized magnetite nanoparticles, which have already been studied and are well-understood.
“We found that our tool’s predictions of the behavior and properties of these nanoparticles was consistent with what we know about these nanoparticles based on experimental observation,” Mahmood says.
更重要的是，该模型还提供了新的洞察自组装期间这些MNP的行为。
“We think the demonstration not only shows that our tool works, but highlights the additional value that it can provide in terms of helping us understand how best to engineer these materials in order to leverage their properties,” Yingling says.
The paper, “All-Atom Simulation Method for Zeeman Alignment and Dipolar Assembly of Magnetic Nanoparticles，“发表在里面Journal of Chemical Theory and Computation。The work was done 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.
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编辑报告s:The study abstract follows.
“磁纳米粒子的塞曼对齐和双极组装的全原子仿真方法”
Authors: Akhlak U. Mahmood and Yaroslava G.Yingling, North Carolina State University
Published: March 10,Journal of Chemical Theory and Computation
DOI: 10.1021/acs.jctc.1c01253
Abstract:磁性纳米颗粒（MNP）可以以优异的顺序和独特的几何形状组织成新的结构。然而，由于外部磁场和范德瓦尔斯，静电，偶极，空间和流体动力学相互作用的复杂相互作用，对较小MNP的自组装的研究具有挑战性。在这里，我们提出了一种新的全原子分子动力学（AMD）模拟方法，以便能够详细研究MNP的动态，自组装，结构和性质，作为核心尺寸和形状，配体化学，溶剂性能和外场的函数。我们通过模拟油酸配体 - 官能化磁铁矿的自组装（Fe）来证明模型的使用和有效性（Fe3O4) nanoparticles, with spherical and cubic shapes, into rings, lines, chains, and clusters under a uniform external magnetic field. We found that the long-range electrostatic interactions can favor the formation of a chain over a ring, the ligands promote MNP cluster growth, and the solvent can reduce the rotational diffusion of the MNPs. The algorithm has been parallelized to take advantage of multiple processors of 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 insights into the physics and chemistry of different magnetic assembly processes with atomistic details.
