Investigation On Effects of Different Factors On Biosensor Performance Via Numerical Simulation

Friday, November 13, 2009: 8:30 AM
Cheekwood F (Gaylord Opryland Hotel)

M.H. Akanda, Department of Chemical Engineering, Auburn University, Auburn, AL
Jin Wang, Department of Chemical Engineering, Auburn University, Auburn, AL
Z.-Y. Cheng, Materials Engineering, Auburn University, Auburn, AL
B. Chin, Materials Engineering, Auburn University, Auburn, AL

In the US, more than ninety percent of foodborne illnesses are attributable to bacterial contamination of consumed food. Therefore, microbial detection is one of the most important steps to ensure enhanced safety in the food production and distribution network [1]. Also, the need to improve our detection capabilities is highlighted to prevent different forms of intentional bioterrorism. Biosensors are considered as having great potential for future pathogen detection due to their high sensitivity and near real time detection capability. The recently developed magnetoelastic biosensors [2,3] have offered several advantages in terms of sensitivity, working environment, cost effectiveness etc over other existing biosensors. Thus, magnetoelastic biosensors show great promise to detect foodborne pathogens and hold huge potential to improve the safety of the food network. In order to detect the pathogens early, it is desirable to improve the biosensor's sensitivity, which can be achieved through proper biosensor design. To design biosensors with optimal sensitivity, we perform numerical simulations to investigate the effects of different factors such as sensor geometry, flow condition etc on the sensor performance (such as detecting limit, and response time), as experiments alone are not sufficient due to practical limitations such as cost, time and technology.

In this work, we develop a first principles model that incorporate fluid mechanics, mass transfer and surface reaction/ diffusion mechanism to describe stagnant as well as convective flow biosensing system. A set of coupled partial differential equations with proper boundary conditions are derived based on first principles model (such as equation of continuity for mixtures with reaction term) for different geometries and flowing conditions. Numerical solver COMSOL Multiphysics® 3.4 has been used to solve the 3D/2D system. COMSOL results are validated by the analytical results for simplified geometries (such as hemisphere, disk etc) with stagnant flow to ensure the model correctness when applied to more complex systems. The dynamics of biosensing with more realistic sensor geometry (such as rectangular bar) in 2D and 3D forms are investigated. For the detection of bacillus anthracis spores using phage-based magnetoelastic biosensors, analysis of simulation results reveals that the binding ratio is 2:1 for antigen and bacteriophage, instead of typically observed 1:1 binding for antigen and antibody binding [4]. This finding is verified by experiments. In addition, simulation results indicate that the reverse binding constant is higher than forward binding constant, which is also verified by experiments. To optimize the performance of the biosensor, biosensing dynamic response are analyzed and the effects of different factors such as mass transfer, bulk concentration, reaction mechanism and binding constant are investigated. The methodology and results obtained in this work can be used to guide biosensor design to optimize sensitivity. In addition, this work demonstrates how the numerical simulation can help us understand the biosensing mechanism.

Reference:

1. Sheehan P. E., and Whitman L. J., “Detection limits for nanoscale biosensors”, Nano Letters, 5(4), 803-807, 2005

2. Wan J., Shu H., Huang S., Fiebor B., Chen I., Petrenko V., and Chin B., “Phage-Based Magnetoelastic Wireless Biosensors for Detecting Bacillus Anthracis Spores” IEEE Sensor Journal, 7(3), March 2007

3. Wan J., Johnson M., Guntupalli R., Petrenko V., and Chin B., “Detection of Bacillus anthracis spores in liquid using phage-based magnetoelastic micro-resonators”, Sensors and Actuators B 127 (2007) 559–566

4. Hua G., Gaob Y., Li D.,“Modeling micropatterned antigen–antibody binding kinetics in a microfluidic chip” Biosensors and Bioelectronics 22 (2007) 1403–1409

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See more of this Session: Biosensors
See more of this Group/Topical: Materials Engineering and Sciences Division