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 Copyright policy )The agglomeration of ferromagnetic nanoparticles in a fluid is studied using nanoparticle-level Langevin dynamics simulations. The simulations have interdigitation and bridging between ligand coatings included using a computationally-cheap, phenomenological sticking parameter c. The interactions between ligand coatings are shown in this preliminary study to be important in determining the shapes of agglomerates that form. A critical size for the sticking parameter is estimated analytically and via the simulations and indicates where particle agglomerates transition from well-ordered (c is small) to disordered (c is large) shapes. Results are also presented for the hysteresis loops (magnetization versus applied field) for these particle systems in an oscillating magnetic field appropriate for hyperthermia applications. The results show that the clumping of particles has a significant effect on their macroscopic properties, with important consequences on applications. In particular, the work done by an oscillating field on the system has a nonmonotonic dependence on c.
Chemistry, Langevin simulation, magnetic nanoparticles, Langevin dynamics, hysteresis, magnetic nanoparticle, magnetic hyperthermia, ligand, QD1-999, Article, 620
Chemistry, Langevin simulation, magnetic nanoparticles, Langevin dynamics, hysteresis, magnetic nanoparticle, magnetic hyperthermia, ligand, QD1-999, Article, 620
| citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 8 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Top 10% | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Top 10% | 
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