A predictive model of iron oxide nanoparticles flocculation tuning Z-potential in aqueous environment for biological application

Iron oxide nanoparticles are the most used magnetic nanoparticles in biomedical and biotechnological field because of their nontoxicity respect to the other metals. The investigation of iron oxide nanoparticles behaviour in aqueous environment is important for the biological applications in terms of polydispersity, mobility, cellular uptake and response to the external magnetic field. Iron oxide nanoparticles tend to agglomerate in aqueous solutions; thus, the stabilisation and aggregation could be modified tuning the colloids physical proprieties. Surfactants or polymers are often used to avoid agglomeration and increase nanoparticles stability. We have modelled and synthesised iron oxide nanoparticles through a co-precipitation method, in order to study the influence of surfactants and coatings on the aggregation state. Thus, we compared experimental results to simulation model data. The change of Z-potential and the clusters size were determined by Dynamic Light Scattering. We developed a suitable numerical model to predict the flocculation. The effects of Volume Mean Diameter and fractal dimension were explored in the model. We obtained the trend of these parameters tuning the Z-potential. These curves matched with the experimental results and confirmed the goodness of the model. Subsequently, we exploited the model to study the influence of nanoparticles aggregation and stability by Z-potential and external magnetic field. The highest Z-potential is reached up with a small external magnetic influence, a small aggregation and then a high suspension stability. Thus, we obtained a predictive model of Iron oxide nanoparticles flocculation that will be exploited for the nanoparticles engineering and experimental setup of bioassays.