@article{PourshahidiEngelmannOffenhaeusseretal.2022,
  author    = {Pourshahidi, Ali Mohammad and Engelmann, Ulrich M. and Offenh{\"a}usser, Andreas and Krause, Hans-Joachim},
  title     = {Resolving ambiguities in core size determination of magnetic nanoparticles from magnetic frequency mixing data},
  series = {Journal of Magnetism and Magnetic Materials},
  volume    = {563},
  journal   = {Journal of Magnetism and Magnetic Materials},
  number    = {In progress, Art. No. 169969},
  publisher = {Elsevier},
  address   = {Amsterdam},
  issn      = {0304-8853},
  doi       = {10.1016/j.jmmm.2022.169969},
  year      = {2022},
  abstract  = {Frequency mixing magnetic detection (FMMD) has been widely utilized as a measurement technique in magnetic immunoassays. It can also be used for the characterization and distinction (also known as "colourization") of different types of magnetic nanoparticles (MNPs) based on their core sizes. In a previous work, it was shown that the large particles contribute most of the FMMD signal. This leads to ambiguities in core size determination from fitting since the contribution of the small-sized particles is almost undetectable among the strong responses from the large ones. In this work, we report on how this ambiguity can be overcome by modelling the signal intensity using the Langevin model in thermodynamic equilibrium including a lognormal core size distribution fL(dc,d0,σ) fitted to experimentally measured FMMD data of immobilized MNPs. For each given median diameter d0, an ambiguous amount of best-fitting pairs of parameters distribution width σ and number of particles Np with R2 > 0.99 are extracted. By determining the samples' total iron mass, mFe, with inductively coupled plasma optical emission spectrometry (ICP-OES), we are then able to identify the one specific best-fitting pair (σ, Np) one uniquely. With this additional externally measured parameter, we resolved the ambiguity in core size distribution and determined the parameters (d0, σ, Np) directly from FMMD measurements, allowing precise MNPs sample characterization.},
  language  = {en}
}