From iron coordination compounds to metal oxide nanoparticles

• Mihail Iacob,
• Carmen Racles,
• Codrin Tugui,
• George Stiubianu,
• Adrian Bele,
• Liviu Sacarescu,
• Daniel Timpu and
• Maria Cazacu

Beilstein J. Nanotechnol. 2016, 7, 2074–2087, doi:10.3762/bjnano.7.198

• light scattering. The parameters were varied within each route to fine tune the size and shape of the formed nanoparticles. Keywords: iron coordination compounds; mixed oxide nanoparticles; morphology control; nanoparticle shape control; optimization procedure; Introduction The iron oxide-based

High-bandwidth multimode self-sensing in bimodal atomic force microscopy

• Michael G. Ruppert and
• S. O. Reza Moheimani

Beilstein J. Nanotechnol. 2016, 7, 284–295, doi:10.3762/bjnano.7.26

• charge sensor. In order to experimentally verify the model Equation 22, a parameter optimization procedure is employed to fit the model Equation 22 to the experimentally obtained voltage to charge frequency response shown in Figure 6c. The optimization method aims to minimize the difference in magnitude

• and phase of the measured transfer function and Equation 22. The resulting parameters are also shown in Table 1. We note that the optimization procedure did not converge for the second, third and fourth mode due to the excessive amount of feedthrough. The differences in the estimated feedthrough of

Structural and magnetic properties of ternary Fe_1–xMn_xPt nanoalloys from first principles

• Markus E. Gruner and
• Peter Entel

Beilstein J. Nanotechnol. 2011, 2, 162–172, doi:10.3762/bjnano.2.20

• below for FePt). The ferromagnetic, ordered icosahedron, which is nearly degenerate for Fe265Pt296, has become unstable in the Mn–Pt system for sizes above 147 atoms. During the geometric optimization procedure it transforms downhill to a perfect L10 cuboctahedron. This proves that the Mackay path is a