Abstract
This study examines the development of an automated particle tracking algorithm to predict the hindered Brownian movement of fluorescent nanoparticles within an evanescent wave field created using total internal reflection fluorescent microscopy. The two-dimensional motion of the fluorescent nanoparticles was tracked, with sub-pixel resolution, by fitting the intensity distribution of the particles to a known Gaussian distribution, thus providing the particle center within a single pixel. Spherical yellow-green polystyrene nanoparticles (200, 500, and 1000 nm in diameter) were suspended in deionized water (control), 10 wt% d-glucose, and 10 wt% glycerol solutions, with 1 mM of NaCl added to each. The motion of tracked nanoparticles was compared with the theoretical tangential hindered Brownian motion to estimate particle diameters and fluid viscosity using a nonlinear regression technique. The automatic tracking algorithm was initially validated by comparing the automated results with manually tracked particles, 1 µm in size. Our results showed that both particle size and solution viscosity were accurately predicted from the experimental mean square displacement. Specifically, the results show that the error of particle size prediction is below 10 % and the error of solution viscosity prediction is less than 1 %. The proposed automatic analysis tool could prove to be useful in bio-application fields for examination of single protein tracking, drug delivery, and cytotoxicity. Furthermore, the proposed tool could be useful in microfluidic areas such as particle tracking velocimetry and noninvasive viscosimetry.
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Acknowledgments
This research was sponsored in part through a Michigan Technological University Research Excellence Fund award (R01339, Choi). This work was partially supported by the National Research Foundation of Korea (NRF) Grant funded by the Korea government (MSIP) (No. 2014R1A2A2A01006186, Lee).
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Mohammad Mamun and Jose Almonte have contributed equally to this work as a second author.
Charles H. Margraves, Seong-Hyuk Lee, and Chang Kyoung Choi have contributed equally to this work.
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Shin, D.H., Mamun, M., Almonte, J. et al. A noninvasive method to predict fluid viscosity and nanoparticle size using total internal reflection fluorescence microscopy (TIRFM) imaging. Microfluid Nanofluid 20, 45 (2016). https://doi.org/10.1007/s10404-016-1710-9
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DOI: https://doi.org/10.1007/s10404-016-1710-9