Hydrodynamic Chromatography with Deterministic Lateral Displacement Effect

IF 6.7 1区 化学 Q1 CHEMISTRY, ANALYTICAL
Valentina Biagioni
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Abstract

Hydrodynamic chromatography (HDC) is a flow-driven passive method for separating micrometric/nanometric particles based on the interaction between a nonuniform velocity profile and Brownian diffusion, which causes particles of different size to migrate at different average velocity throughout the separation column. Despite its conceptual simplicity and relative ease of implementation, HDC remains to date an underutilized technique in view of the lengthy channels and large operational times required. In the search for optimal geometries enhancing separation efficiency, micro-Pillar Array Columns (μPACs), constituted by a doubly periodic obstacle lattice aligned with the direction of the flow, have been successfully proposed and tested. The aim of this article is to show that a further improvement of HDC efficiency in μPACs is possible by enforcing a symmetry breakup, where the lattice is misaligned by an angle θl with respect to the flow direction. The mismatch between the flow direction and the lattice axes triggers a new separation mechanism, referred to as Deterministic Lateral Displacement (DLD), which causes particles of different size to migrate along different directions through the lattice. So far, DLD has been enforced exclusively in continuous separations run under steady-state conditions.. If an unsteady (chromatographic) operating mode in a slanted μPACs is enforced, differences in migration velocities and migration angles act simultaneously as two independent mechanisms. Theoretical/numerical evidence is provided, showing that the synergy between the two separation drives can shorten device lengths and analysis times by a factor of 10 or even higher (depending on the analytical target) when compared to plain-HDC. The results presented are based on an advection-diffusion template enforcing the classical excluded-volume model to account for particle–wall interactions, an approach previously validated against experimental data by different research groups, both in standard μPACs-HDC and in continuous DLD devices. Numerical results of the average particle migration angle and velocity magnitude are obtained by two independent (Eulerian and Lagrangian) computational approaches. A case study of geometry is used throughout to illustrate the concrete implementation of the method for a multidispersed mixture of particles of five nominal diameters ranging from 1 to 1.6 μm.

Abstract Image

确定横向位移效应的流体动力色谱
水动力色谱(HDC)是一种基于非均匀速度分布与布朗扩散相互作用的流动驱动被动分离微/纳米颗粒的方法,这种相互作用导致不同粒径的颗粒在分离柱中以不同的平均速度迁移。尽管HDC的概念简单且相对容易实现,但由于需要较长的通道和较长的操作时间,迄今为止它仍然是一种未充分利用的技术。为了寻找提高分离效率的最佳几何结构,我们成功地提出并测试了由与流动方向一致的双周期障碍晶格构成的微柱阵柱(μPACs)。本文的目的是表明μ pac中进一步提高HDC效率是可能的,通过强制对称破裂,其中晶格相对于流动方向有一个θl角的错位。流动方向和晶格轴之间的不匹配触发了一种新的分离机制,称为确定性横向位移(DLD),它导致不同大小的颗粒沿不同方向通过晶格迁移。到目前为止,DLD只在稳定状态下运行的连续分离中强制执行。如果在倾斜μ pac中强制采用非定常(色谱)工作模式,则迁移速度和迁移角的差异同时作为两个独立的机制起作用。提供了理论/数值证据,表明与普通hdc相比,两个分离驱动器之间的协同作用可以将设备长度和分析时间缩短10倍甚至更高(取决于分析目标)。本文给出的结果是基于一个平流扩散模板,该模板强制执行经典的排除体积模型来解释粒子-壁相互作用,该方法先前通过不同研究小组在标准μ pac - hdc和连续DLD器件中的实验数据进行了验证。采用欧拉和拉格朗日两种独立的计算方法,得到了粒子平均迁移角和速度大小的数值结果。一个几何学的案例研究贯穿始终,以说明该方法的具体实现,用于五种公称直径范围为1至1.6 μm的多分散颗粒混合物。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Analytical Chemistry
Analytical Chemistry 化学-分析化学
CiteScore
12.10
自引率
12.20%
发文量
1949
审稿时长
1.4 months
期刊介绍: Analytical Chemistry, a peer-reviewed research journal, focuses on disseminating new and original knowledge across all branches of analytical chemistry. Fundamental articles may explore general principles of chemical measurement science and need not directly address existing or potential analytical methodology. They can be entirely theoretical or report experimental results. Contributions may cover various phases of analytical operations, including sampling, bioanalysis, electrochemistry, mass spectrometry, microscale and nanoscale systems, environmental analysis, separations, spectroscopy, chemical reactions and selectivity, instrumentation, imaging, surface analysis, and data processing. Papers discussing known analytical methods should present a significant, original application of the method, a notable improvement, or results on an important analyte.
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