(402a) Stochastic- Based Method To Determine Biased Diffusion of Bio-Polyelectrolyte (Protein, Aerosole, Dust) Under Biased Forces
AIChE Annual Meeting
Wednesday, November 6, 2013 - 8:30am to 8:48am
STOCHASTIC- BASED METHOD TO DETERMINE BIASED DIFFUSION OF BIO-POLYELECTROLYTE (PROTEIN, AEROSOLE, DUST) UNDER BIASED FORCES
PARVIN GOLBAYANI1, MOTOYA MACHIDA2, J. R.SANDERS1, PEDRO E. ARCE1
1Department of Chemical Engineering, Tennessee Technological University, Cookeville, Tennessee
2Department of Mathematics, Tennessee Technological University, Cookeville, Tennessee
Key to this research is the determination of the effective diffusion (or biased diffusion) and effective mobility (under biased applied forces whose competition determine the quality of the separation for a given type of proteins. Under suitable conditions, this competition can be efficiently assessed by the time of separation [1-2]that incorporates the two effects in a convenient manner to study the impact of the two transport based parameters, i.e. when the effective mobility wins over the dispersion (or biased effects due to the electrical field) the device leads to an efficient separation. Otherwise, we will achieve an efficient mixing.
The novelty of this research is on the use of stochastic- based approach to obtain such coefficients. The analysis involves the solution of the equation of motion of a Brownian particle under both non-convective and convective media by using a Langevin equation . In this research, separation time of Lysozyme (LYZ, MW: 14.4 KDa) and Cytochrome c (CYC, MW: 11.7 KDa)in presence of biased forces such as electrical and hydrodynamic based have been studied. The analysis involves the solution of the equation of motion of a Brownian particle by using a Langevin equation. Results of studying Couette flow from stochastic-based approach, as an example case, this investigation are consistent with results based on continuum mechanics. This, to the best of our knowledge, would be the first effort to determine biased diffusion coefficient directly from fundamental, i.e. stochastics-based principles by analytical methods. In summary, this project offers a very efficient path to obtain vital information to guide both experiments and new research relevant to both environmental proteomics and clinical diagnostics.
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