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730d

Elucidation of Biosensing Processes through Simulation

M. H. Akanda1, Jin Wang2, B. Chin3, and Z.-Y. Cheng3. (1) Chemical Engineering, Auburn University, Auburn, AL 36849, (2) Department of Chemical Engineering, Auburn University, Auburn, AL 36849, (3) Materials Engineering, Auburn University, Auburn, AL 36849

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. Biological weapons have long been recognized by the United States as considerable threats to the country that could kill thousands of people and create an unparalleled medical, political and social crisis. Therefore, the need to improve our detection capabilities is also 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 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 sensor design. In this work, we perform simulations to investigate the effects of different factors such as 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 first principle models that incorporate fluid mechanics, mass transfer and surface reaction mechanism to describe stagnant as well as convective flow biosensing system. We consider both simplified geometries of biosensor such as hemisphere, disk, hemicylinder as well as more realistic cases such as cylindrical, rectangular bar. A set of partial differential equations with proper boundary conditions are derived based on first principle model (such as equation of continuity DCA/Dt=DAB2CA+rA for mixtures) for different geometries and flowing conditions. Diffusion of pathogens from bulk fluid to biosensor surface may be diffusion controlled or adsorption/reaction controlled. Both conditions are considered for a set of specific biosensor geometries. Suitable coordinate systems are used to handle different sensor geometries. Numerical solutions are generated using COMSOL Multiphysics® to examine the effect of sensor shape and size on its performance. Simulation results are visualized with different plots to illustrate the dynamic behaviors of the biosensing system. In addition, analytical solutions for the simplified cases are derived and compared with numerical solutions. Model parameters such as diffusion coefficient, surface reaction/binding rate constant are estimated to fit available experimental results with appropriate regression techniques. Finally, model predictions are validated using experimental data. The simulation prediction enables one to analyze the performance of biosensors with different shapes and flowing conditions. Thus, the results obtained in this work will be used to guide sensor design in the future.

Reference:

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