Random Walk Simulation of an Asymmetric Obstacle Sieve for Continuous Molecular Separation†

doi:10.7503/cjcu20131041

Abstract

Abstract:

In order to dynamically track the diffusing molecules in the micro-separation system, and further to understand its influence on separation performance thoroughly, we have developed a software based on the random walk theory in the confined space, with which the diffusion process in an asymmetric obstacle sieve has been simulated. The results showed that the molecules which own larger diffusion coefficients would be devi-ated from the direction of the driving speed by the asymmetric obstacles more significantly. So as to achieve better separation effect, we should select appropriate drift velocity and regulate the probability difference of the different constituents’ deviating from the driving direction. In addition, the band broadening effect and predicted the performance of the separation sieve with different length were discussed. The simulation method proposed in this work has been instructive for the development of micro-separation devices and the optimization of operating parameters.

Key words: Asymmetric obstacle, Diffusion, Separation, Random walk simulation

CLC Number:

O641
O657

TrendMD:

GAO Yunqiao, CHEN Lili, FU Yingqiang, ZHAO Jianwei. Random Walk Simulation of an Asymmetric Obstacle Sieve for Continuous Molecular Separation^†[J]. Chem. J. Chinese Universities, 2014, 35(4): 818.

Figures/Tables 6

Fig.1 Schematic diagram of the asymmetric obstacle sieve

Fig.2 Basic principle of particle diffusion within the asymmetric obstacle sieveParticles are driven through the sieve hydrodynamically or electrophoretically.

Fig.3 Plots of lgDx(A), lgDy(B), rx(C) and ry(D) of particles at different random velocities v/(μm·s-1): a. 100; b. 150; c. 200; d. 250; e. 300.

Fig.4 Plots of lgDx(A), lgDy(B), rx(C) and ry(D) of particles with different drift velocities v/(μm·s-1): a. 1; b. 2; c. 5; d. 7; e. 10.

Fig.5 Typical trajectories of particles with diffusion coefficient(A) and histograms of the deflection summarized from 200 trajectories of each particle after a diffusion time of 80 s(B)(A) a. 0.25 μm2/s; b. 0.99 μm2/s. (B) Scale at bottom indicates the total number of channels that the particle has passed. Solid lines give the Gaussian fitting of the simulation data.

Fig.6 Difference between the transition probabilities of the particles with 0.25 and 0.99 μm2/s diffusion coefficient as a function of the driving velocity

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