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脂质双层包裹纳米颗粒的双电层静电学:迈向对原始细胞静电学的更好理解。

Electric double layer electrostatics of lipid-bilayer-encapsulated nanoparticles: Toward a better understanding of protocell electrostatics.

作者信息

Jing Haoyuan, Das Siddhartha

机构信息

Department of Mechanical Engineering, University of Maryland, College Park, MD, 20742, USA.

出版信息

Electrophoresis. 2018 Mar;39(5-6):752-759. doi: 10.1002/elps.201700286. Epub 2018 Jan 4.

Abstract

Lipid-bilayer-encapsulated nanoparticles (LBLENPs) or NP-supported LBL systems, such as protocells (which are lipid bilayer encapsulated mesoporous silica nanoparticles or MSNPs) have received extensive attention for applications like targeted drug and gene deliveries, multimodal diagnostics, characterization of membrane-geometry sensitive molecules, etc. Very often electrostatic-mediated interactions have been hypothesized to play key roles in the functioning of these LBLENPs. Despite that, very little has been done to theoretically quantify the fundamental electric double layer (EDL) electrostatics of such LBLENPs. In this study, we develop an EDL theory to describe the electrostatics of such LBLENPs. We show that the electrostatics is a manifestation of the charged/dielectric nature of the NP, LBL structure and charging, and the ionic environment in which the LBLENPs are present. We also establish that for certain conditions of charging of the NP one witnesses a most remarkable charge inversion like electrostatics within the LBL membrane or the NP itself. We anticipate that our findings will provide an extremely useful platform for better understanding the fabrication and functioning of such LBLENPs and discuss examples where our theory can be useful.

摘要

脂质双层包裹的纳米颗粒(LBLENPs)或纳米颗粒支撑的LBL系统,如原细胞(即脂质双层包裹的介孔二氧化硅纳米颗粒或MSNPs),在靶向药物和基因递送、多模态诊断、膜几何形状敏感分子的表征等应用中受到了广泛关注。人们常常假设静电介导的相互作用在这些LBLENPs的功能中起关键作用。尽管如此,在理论上量化此类LBLENPs的基本双电层(EDL)静电方面所做的工作却很少。在本研究中,我们开发了一种EDL理论来描述此类LBLENPs的静电学。我们表明,静电学是纳米颗粒的带电/介电性质、LBL结构和电荷以及LBLENPs所处离子环境的一种表现。我们还确定,在纳米颗粒充电的某些条件下,人们会在LBL膜或纳米颗粒本身内目睹一种极其显著的类似电荷反转的静电现象。我们预计,我们的发现将为更好地理解此类LBLENPs的制造和功能提供一个极其有用的平台,并讨论我们的理论可能有用的例子。

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