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Research Papers: Techniques and Procedures

Optical Diffusometry Techniques and Applications in Biological Agent Detection

[+] Author and Article Information
Aloke Kumar

School of Mechanical Engineering, and Birck Nanotechnology Center, Purdue University, West Lafayette, IN 47907

Venu M. Gorti1

School of Mechanical Engineering, Purdue University, West Lafayette, IN 47907

Hao Shang2

School of Chemical and Biomedical Engineering, Purdue University, West Lafayette, IN 47907

Gil U. Lee

School of Chemical and Biomedical Engineering, and Birck Nanotechnology Center, Purdue University, West Lafayette, IN 47907

Nung Kwan Yip

Department of Mathematics, Purdue University, West Lafayette, IN 47907

Steve T. Wereley3

School of Mechanical Engineering, and Birck Nanotechnology Center, Purdue University, West Lafayette, IN 47907wereley@purdue.edu

1

Present address: Hindustan Unilever Limited, Mumbai, India.

2

Present address: MagSense Life Sciences Inc., USA.

3

Corresponding author.

J. Fluids Eng 130(11), 111401 (Sep 22, 2008) (8 pages) doi:10.1115/1.2969430 History: Received June 22, 2007; Revised June 11, 2008; Published September 22, 2008

Optical diffusometry is a technique used for measuring diffusion. This work explores the possibility of directly measuring diffusion coefficients of submicron particles for pathogen detection. The diffusion coefficient of these particles is a function of the drag coefficient of the particle at constant temperatures. Particles introduced into a sample containing an analyte bind with the analyte if functionalized with the appropriate antibodies. This leads to an increase in the hydrodynamic drag of the particles and hence a decrease in their diffusion coefficient. This study uses the above principle to effectively measure the diffusion coefficient of the particles using two different experimental approaches. The measured reduction in the diffusion coefficient can be correlated to the amount of analyte present and thus forms the basis of biological agent detection. Sensitivity to experimental conditions is analyzed. It is observed that alternative techniques such as optical trapping hold promise: the diffusive behavior of particles in optical traps is found to be quantitatively different from that of a free particle. Hence preconditions are identified to make optical trapping appropriate for agent detection.

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Copyright © 2008 by American Society of Mechanical Engineers
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References

Figures

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Figure 1

Experimental setup for optical trapping. The lenses on the rail expand the beam, while the objective lens focuses the beam onto a diffraction limited spot. Epifluorescent imaging enhances the image quality.

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Figure 2

Polystyrene beads are made to undergo a series of chemical processes so that a monolayer of poly(ethylene glycol) (PEG) is formed, and subsequently antibodies are immobilized on the PEG monolayer. The viruses bind onto the antibodies, thus leading to functionalized particles.

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Figure 3

Red microparticles (0.69μm) functionalized with viruses are visualized using fluorescent microscopy. Note that fluorescent particles of only one type can be imaged at a time. (a) Fluorescent red particles can be seen as white dots on a black background. (b) The same image is filtered to enhance image quality. For particle tracking only, particles with intensity above a certain threshold are taken into account.

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Figure 4

A large number of images are processed to yield particle tracks. The terminated paths indicate loss of particle from the viewing volume.

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Figure 5

Diffusion coefficients of antibody functionalized particles and unmodified particles as a function of virus concentration

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Figure 6

Experimental error in the calculation of diffusion coefficient versus number of data points. These errors were calculated from simulated images of particles exhibiting Brownian motion.

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Figure 11

A particle in a trap exhibits a linear trend on small time scales. This linear trend can be used for diffusion based differentiation. Note the extremely small time scales involved.

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Figure 10

Percentage change in the diffusion coefficient of virus-tagged beads as a function of the hydrodynamic radius of the pathogen

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Figure 9

Typical response of a bead confined to a small spatial volume by an optical trap

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Figure 8

Simulation runs of particles with and without a trap. The particle in a potential well exhibits a modified Brownian motion.

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Figure 7

A 0.69μm particle (center) in an optical trap

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