Building up accurate atomistic models of biofunctionalized magnetite nanoparticles from first-principles calculations

IF 11.9 1区 材料科学 Q1 CHEMISTRY, PHYSICAL npj Computational Materials Pub Date : 2025-01-25 DOI:10.1038/s41524-024-01476-3
Paulo Siani, Enrico Bianchetti, Cristiana Di Valentin
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Abstract

Biofunctionalized magnetite nanoparticles offer unique multifunctional capabilities that can drive nanomedical innovations. Designing synthetic bioorganic coatings and controlling their molecular behavior is crucial for achieving superior performance. However, accurately describing the interactions between bio-inorganic nanosystem components requires reliable computational tools, with empirical force fields at their core. In this work, we integrate first-principles calculations with mainstream force fields to construct and simulate atomistic models of pristine and biofunctionalized magnetite nanoparticles with quantum mechanical accuracy. The practical implications of this approach are demonstrated through a case study of PEG (polyethylene glycol)-coated magnetite nanoparticles in physiological conditions, where we investigate how polymer chain length, in both heterogeneous and homogeneous coatings, impacts key functional properties in advanced nanosystem design. Our findings reveal that coating morphology controls polymer ordering, conformation, and polymer corona hydrogen bonding, highlighting the potential of this computational toolbox to advance next-generation magnetite-based nanosystems with enhanced performance in nanomedicine.

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从第一性原理计算建立生物功能化磁铁矿纳米颗粒的精确原子模型
生物功能化磁铁矿纳米颗粒提供了独特的多功能能力,可以推动纳米医学创新。设计合成生物有机涂层并控制其分子行为是获得优异性能的关键。然而,准确地描述生物无机纳米系统组件之间的相互作用需要可靠的计算工具,以经验力场为核心。在这项工作中,我们将第一性原理计算与主流力场相结合,以量子力学精度构建和模拟原始和生物功能化磁铁矿纳米颗粒的原子模型。通过对生理条件下PEG(聚乙二醇)包裹的磁铁矿纳米颗粒的案例研究,我们证明了这种方法的实际意义,我们研究了聚合物链长度,在非均相和均相涂层中,如何影响先进纳米系统设计中的关键功能特性。我们的研究结果表明,涂层形态控制着聚合物的有序、构象和聚合物电晕氢键,这突出了该计算工具箱在推进下一代磁性纳米系统方面的潜力,并增强了纳米医学的性能。
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来源期刊
npj Computational Materials
npj Computational Materials Mathematics-Modeling and Simulation
CiteScore
15.30
自引率
5.20%
发文量
229
审稿时长
6 weeks
期刊介绍: npj Computational Materials is a high-quality open access journal from Nature Research that publishes research papers applying computational approaches for the design of new materials and enhancing our understanding of existing ones. The journal also welcomes papers on new computational techniques and the refinement of current approaches that support these aims, as well as experimental papers that complement computational findings. Some key features of npj Computational Materials include a 2-year impact factor of 12.241 (2021), article downloads of 1,138,590 (2021), and a fast turnaround time of 11 days from submission to the first editorial decision. The journal is indexed in various databases and services, including Chemical Abstracts Service (ACS), Astrophysics Data System (ADS), Current Contents/Physical, Chemical and Earth Sciences, Journal Citation Reports/Science Edition, SCOPUS, EI Compendex, INSPEC, Google Scholar, SCImago, DOAJ, CNKI, and Science Citation Index Expanded (SCIE), among others.
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