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ACS Nano. 2014 Sep 23;8(9):9446-56. doi: 10.1021/nn503573s. Epub 2014 Aug 25.

Nanoparticle surface characterization and clustering through concentration-dependent surface adsorption modeling.

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Institute of Computational Comparative Medicine, ‡Nanotechnology Innovation Center of Kansas State, §Electrical and Computer Engineering Department, and ∥Anatomy and Physiology Department, Kansas State University , Manhattan, Kansas 66506, United States.


Quantitative characterization of nanoparticle interactions with their surrounding environment is vital for safe nanotechnological development and standardization. A recent quantitative measure, the biological surface adsorption index (BSAI), has demonstrated promising applications in nanomaterial surface characterization and biological/environmental prediction. This paper further advances the approach beyond the application of five descriptors in the original BSAI to address the concentration dependence of the descriptors, enabling better prediction of the adsorption profile and more accurate categorization of nanomaterials based on their surface properties. Statistical analysis on the obtained adsorption data was performed based on three different models: the original BSAI, a concentration-dependent polynomial model, and an infinite dilution model. These advancements in BSAI modeling showed a promising development in the application of quantitative predictive modeling in biological applications, nanomedicine, and environmental safety assessment of nanomaterials.


BSAI; in situ characterization; nanomedicine; nanoparticles; nanotoxicology; surface physicochemistry

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