Magnetorheology of alginate ferrogels

Abstract

Magnetorheological (MR) effect is a phenomenon typical of suspensions of magnetizable particles in a liquid carrier, characterized by strong changes of their mechanical properties in response to applied magnetic fields. Its origin is on the migration of magnetized particles and their aggregation into chain-like structures. However, for ferrogels, consisting of dispersions of magnetic particles in a polymer matrix, migration of particles is hindered by the elastic forces of the polymer network, preventing from strong MR effect. Interestingly, in this manuscript, we demonstrate that strong MR effect in robustly cross-linked polymer ferrogels is still possible. Experimental results showed enhancement of the storage modulus of more than one order of magnitude for alginate ferrogels containing less than about 10 vol% of iron particles under moderate magnetic fields. The differential feature of these ferrogels is that, instead of individual particles, the disperse phase consisted of large clusters of iron microparticles homogeneously distributed within the polymer networks. These clusters of magnetic particles were formed at the stage of the preparation of the ferrogels and their presence within the polymer networks had two main consequences. First, the volume fraction of clusters was considerably larger than this of individual particles, resulting in a larger effective volume fraction of solids. Second, since the force of magnetic attraction between magnetic bodies is roughly proportional to the cube of the body size, the existence of such clusters favored inter-cluster interaction under a magnetic field and the appearance of strong MR effect. On this basis, we demonstrated by theoretical modeling that the strong MR effect displayed by the alginate ferrogels of the present work can be quantitatively explained by assuming the existence of large, roughly spherical particle aggregates formed at the stage of the preparation of the ferrogels. Our theoretical model provides a reasonable quantitative prediction of the experimental results. © 2019 IOP Publishing Ltd.