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Optimization of Pathogen Capture in Flowing Fluids with Magnetic Nanoparticles
Author(s) -
Kang Joo H.,
Um Eujin,
Diaz Alexander,
Driscoll Harry,
Rodas Melissa J.,
Domansky Karel,
Watters Alexander L.,
Super Michael,
Stone Howard A.,
Ingber Donald E.
Publication year - 2015
Publication title -
small
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.785
H-Index - 236
eISSN - 1613-6829
pISSN - 1613-6810
DOI - 10.1002/smll.201501820
Subject(s) - magnetic nanoparticles , materials science , particle size , magnetic separation , nanoparticle , particle (ecology) , magnetic particle inspection , range (aeronautics) , biological system , nanotechnology , chemical engineering , composite material , oceanography , geology , engineering , metallurgy , biology
Magnetic nanoparticles have been employed to capture pathogens for many biological applications; however, optimal particle sizes have been determined empirically in specific capturing protocols. Here, a theoretical model that simulates capture of bacteria is described and used to calculate bacterial collision frequencies and magnetophoretic properties for a range of particle sizes. The model predicts that particles with a diameter of 460 nm should produce optimal separation of bacteria in buffer flowing at 1 L h −1 . Validating the predictive power of the model, Staphylococcus aureus is separated from buffer and blood flowing through magnetic capture devices using six different sizes of magnetic particles. Experimental magnetic separation in buffer conditions confirms that particles with a diameter closest to the predicted optimal particle size provide the most effective capture. Modeling the capturing process in plasma and blood by introducing empirical constants ( c e ), which integrate the interfering effects of biological components on the binding kinetics of magnetic beads to bacteria, smaller beads with 50 nm diameters are predicted that exhibit maximum magnetic separation of bacteria from blood and experimentally validated this trend. The predictive power of the model suggests its utility for the future design of magnetic separation for diagnostic and therapeutic applications.