@article{AchtsnichtPourshahidiOffenhaeusseretal.2019,
  author    = {Achtsnicht, Stefan and Pourshahidi, Ali Mohammad and Offenh{\"a}usser, Andreas and Krause, Hans-Joachim},
  title     = {Multiplex detection of different magnetic beads using frequency scanning in magnetic frequency mixing technique},
  series = {Sensors},
  volume    = {19},
  journal   = {Sensors},
  number    = {11},
  publisher = {MDPI},
  address   = {Basel},
  issn      = {1424-8220},
  doi       = {10.3390/s19112599},
  pages     = {13 Seiten},
  year      = {2019},
  abstract  = {In modern bioanalytical methods, it is often desired to detect several targets in one sample within one measurement. Immunological methods including those that use superparamagnetic beads are an important group of techniques for these applications. The goal of this work is to investigate the feasibility of simultaneously detecting different superparamagnetic beads acting as markers using the magnetic frequency mixing technique. The frequency of the magnetic excitation field is scanned while the lower driving frequency is kept constant. Due to the particles' nonlinear magnetization, mixing frequencies are generated. To record their amplitude and phase information, a direct digitization of the pickup-coil's signal with subsequent Fast Fourier Transformation is performed. By synchronizing both magnetic beads using frequency scanning in magnetic frequency mixing technique magnetic fields, a stable phase information is gained. In this research, it is shown that the amplitude of the dominant mixing component is proportional to the amount of superparamagnetic beads inside a sample. Additionally, it is shown that the phase does not show this behaviour. Excitation frequency scans of different bead types were performed, showing different phases, without correlation to their diverse amplitudes. Two commercially available beads were selected and a determination of their amount in a mixture is performed as a demonstration for multiplex measurements.},
  language  = {en}
}
@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}
}