Metal substituted ferrite nanoparticles (MxFe3-xO4, M = Mn, Co, Ni, or Zn) have been synthesized and applied for a variety of biomedical applications such as magnetic hyperthermia treatment, drug delivery, and MRI contrast agents with tunable magnetic properties comparing to iron oxide. To optimize the magnetic performance of the materials, we performed quantum chemical simulations through VASP and computationally optimize the lattice parameters (LPs) of different compositions and configurations of substituted metal ferrites to find the correlation between a material’s composition and its magnetic properties. As a result, we found that computationally optimized LPs generally matched the experimentally observed LPs well. Composition was shown to affect the optimal LP differently based on the substituted metal. While configuration (spinel or inverse spinel) did not significantly impact the optimal LP, randomized distribution impacted magnetic moment greatly. To validate the computational model and back feed the model with experimental data manganese ferrite particles with different compositions were synthesized via chemical reaction and characterized afterwards. Ultimately, better understanding of the lattice parameter enhances our ability to tune ferrites’ magnetic properties and eventually allows for a calibrated computational model of substituted metal ferrites.

Computationally guided synthesis of manganese substituted ferrites

Macromolecular Chemistry: The Second Century

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ACS Spring 2020 National Meeting & Expo

The official archive and repository for all ACS Meetings & Expositions abstracts and select posters or presentations.