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基于 DEP 的粒子操控的混合建模方法。

Hybrid modeling method for a DEP based particle manipulation.

机构信息

Polytechnique Montreal, 2900 Edouard-Montpetit, Montreal, QC H3T 1J4, Canada.

出版信息

Sensors (Basel). 2013 Jan 30;13(2):1730-53. doi: 10.3390/s130201730.

DOI:10.3390/s130201730
PMID:23364197
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC3649439/
Abstract

In this paper, a new modeling approach for Dielectrophoresis (DEP) based particle manipulation is presented. The proposed method fulfills missing links in finite element modeling between the multiphysic simulation and the biological behavior. This technique is amongst the first steps to develop a more complex platform covering several types of manipulations such as magnetophoresis and optics. The modeling approach is based on a hybrid interface using both ANSYS and MATLAB to link the propagation of the electrical field in the micro-channel to the particle motion. ANSYS is used to simulate the electrical propagation while MATLAB interprets the results to calculate cell displacement and send the new information to ANSYS for another turn. The beta version of the proposed technique takes into account particle shape, weight and its electrical properties. First obtained results are coherent with experimental results.

摘要

本文提出了一种新的基于电介质电泳(DEP)的粒子操纵建模方法。所提出的方法弥补了有限元建模中多物理模拟与生物行为之间的缺失环节。该技术是开发更复杂平台的第一步,该平台涵盖了多种操作,例如磁泳和光学操作。该建模方法基于使用 ANSYS 和 MATLAB 的混合接口,将微通道中的电场传播与粒子运动联系起来。ANSYS 用于模拟电场传播,而 MATLAB 则解释结果以计算细胞位移,并将新信息发送回 ANSYS 进行下一轮计算。所提出技术的测试版考虑了粒子的形状、重量及其电特性。首先获得的结果与实验结果一致。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6cee/3649439/1bfdffdc1b6a/sensors-13-01730f12.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6cee/3649439/01066c4d9df6/sensors-13-01730f1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6cee/3649439/77041fa92e19/sensors-13-01730f2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6cee/3649439/48fbd5a4534a/sensors-13-01730f3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6cee/3649439/574fd37152df/sensors-13-01730f4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6cee/3649439/2348eab18c18/sensors-13-01730f5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6cee/3649439/3fbb2292d613/sensors-13-01730f6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6cee/3649439/0cbd46e87b96/sensors-13-01730f7.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6cee/3649439/529698539174/sensors-13-01730f9.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6cee/3649439/0bf261d032f5/sensors-13-01730f10.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6cee/3649439/ceda2da75176/sensors-13-01730f11.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6cee/3649439/1bfdffdc1b6a/sensors-13-01730f12.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6cee/3649439/01066c4d9df6/sensors-13-01730f1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6cee/3649439/77041fa92e19/sensors-13-01730f2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6cee/3649439/48fbd5a4534a/sensors-13-01730f3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6cee/3649439/574fd37152df/sensors-13-01730f4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6cee/3649439/2348eab18c18/sensors-13-01730f5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6cee/3649439/3fbb2292d613/sensors-13-01730f6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6cee/3649439/0cbd46e87b96/sensors-13-01730f7.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6cee/3649439/529698539174/sensors-13-01730f9.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6cee/3649439/0bf261d032f5/sensors-13-01730f10.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6cee/3649439/ceda2da75176/sensors-13-01730f11.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6cee/3649439/1bfdffdc1b6a/sensors-13-01730f12.jpg

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本文引用的文献

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Modelling a Peroxidase-based Optical Biosensor.基于过氧化物酶的光学生物传感器建模
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