Polyacrylic acid-coated iron oxide magnetic nanoparticles: The polymer molecular weight influence

Magnetic nanoparticles (MNPs), in particular, magnetic iron oxide-based nanoparticles were found to be useful as catalysts and as devices for data storage, environmental remediation and several biomedical applications, due to their excellent properties, such as biocompatibility and high magnetic moment. Polyacrylic acid (PAA) is a weak polyelectrolyte that can be used to stabilize the MNPs. To the best of our knowledge, the influence of PAA molecular weight and PAA concentration over the magnetic and structural properties of iron oxide nanoparticles has not been previously reported. The aim of this paper is to describe the differences evidenced in the properties of different magnetic materials by using PAA for iron oxides stabilization by one-pot coprecipitation synthesis. Iron oxide-based magnetic nanoparticles stabilized by polyacrylic acid (PAA) polymers were efficiently prepared and exhaustively characterized. The influence on the employment of two different low PAA molecular weights, Mw 1800 g/mol and 5000 g/mol, in three different iron salts: PAA ratios was analyzed. In summary, the main results showed that: for a certain PAA reactor feed higher oligomer quantities are present in MNPs as higher is the involved molecular weight of the polymeric chain; when molecular weight raises the contribution of loops and tails also does it, allowing having higher polymer contents. For both PAA’s Mw employed as the adsorbed PAA increases particles hydrodynamic diameters decreases, and their distribution becomes narrower; the PAA adsorbs onto iron oxides by chemisorption (the most probable interaction is the bidentate bridging). For the studied cases z potential values depend much more on the PAA’s quantity adsorbed onto the iron oxides than on the PAA’s Mw. MNPs are superparamagnetic and choosing the right shape of particle distribution is not central for getting estimates of the magnetization saturation, the average particle diameter and its standard deviation, while better fits are found with Normal and Log-Normal particle size distributions.