4.5 Article

Numerical analysis of a magnetic nanoparticle-enhanced microfluidic surface-based bioassay

Journal

MICROFLUIDICS AND NANOFLUIDICS
Volume 8, Issue 5, Pages 641-652

Publisher

SPRINGER HEIDELBERG
DOI: 10.1007/s10404-009-0497-3

Keywords

Magnetic nanoparticles; Microfluidics; Reaction kinetics; Bio-assay; Modeling

Funding

  1. National Science Foundation [EEC-0823974]
  2. Div Of Chem, Bioeng, Env, & Transp Sys
  3. Directorate For Engineering [0823974] Funding Source: National Science Foundation

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An innovative magnetic nanoparticle (MNP)-based strategy for enhancing the dynamics and kinetics of surface-based antigen-antibody binding in a microfluidic platform is presented in this study. Finite element technique was employed for quantifying the effect of convection, diffusion, reaction and magnetic field on the detection performance of surface-based bio-assay. It was identified that diffusion is rate limiting when compared with reaction and convection. In order to reduce the detection time, increasing diffusion transport or in general bringing more target antigen towards the surface-bound antibody will be most effective. A novel and simple strategy based on tagging the antigen with MNPs was demonstrated using the numerical model. It was found that local concentration of antigen-MNP complex in the vicinity of sensing surface was increased when magnetic field was used. Different configurations of magnetic field around the microchannel for focusing target antigen towards the sensing surface were simulated and the most optimized configuration was identified. Furthermore, it was quantitatively demonstrated that MNP enhanced the surface-binding kinetics and reduced the detection time of target antigen by almost 42%. Moreover, when compared with physical means of reducing diffusion barrier, MNP-based detection was 35% more efficient. Overall, MNPs enhanced the mass transport of target antigen towards sensing surface which resulted in considerable reduction in detection time. The simulations performed using the developed model will not only help to investigate a wide range of design parameters but also provide generic strategy that can be exploited at the concept stage for designing, optimizing and developing efficient and fast small-scale surface-based bioassays.

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