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Biological Surface Adsorption Index of Nanomaterials: Modelling Surface Interactions of Nanomaterials with Biomolecules

  • Ran Chen
  • Jim E. Riviere
Chapter
Part of the Advances in Experimental Medicine and Biology book series (AEMB, volume 947)

Abstract

Quantitative analysis of the interactions between nanomaterials and their surrounding environment is crucial for safety evaluation in the application of nanotechnology as well as its development and standardization. In this chapter, we demonstrate the importance of the adsorption of surrounding molecules onto the surface of nanomaterials by forming biocorona and thus impact the bio-identity and fate of those materials. We illustrate the key factors including various physical forces in determining the interaction happening at bio-nano interfaces. We further discuss the mathematical endeavors in explaining and predicting the adsorption phenomena, and propose a new statistics-based surface adsorption model, the Biological Surface Adsorption Index (BSAI), to quantitatively analyze the interaction profile of surface adsorption of a large group of small organic molecules onto nanomaterials with varying surface physicochemical properties, first employing five descriptors representing the surface energy profile of the nanomaterials, then further incorporating traditional semi-empirical adsorption models to address concentration effects of solutes. These Advancements in surface adsorption modelling showed a promising development in the application of quantitative predictive models in biological applications, nanomedicine, and environmental safety assessment of nanomaterials.

Keywords

Nanoparticles Surface interactions BSAI Bicorona Physicochemistry In situ characterization 

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Ran Chen
    • 1
  • Jim E. Riviere
    • 2
  1. 1.Institute of Computational Comparative Medicine, Nanotechnology Innovation Center of Kansas StateKansas State UniversityManhattanUSA
  2. 2.Institute of Computational Comparative Medicine, Department of Anatomy and PhysiologyKansas State UniversityManhattanUSA

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