Printable Nanocomposites with Superparamagnetic Maghemite ($\gamma$-Fe$_2$O$_3$) Particles for Microinductor-core Applications
Mathias Zambach, Miriam Var\'on, Thomas Veile, Bima N. Sanusi, Matti Knaapila, Anders M. J{\o}rgensen, L\'aszl\'o Alm\'asy, Christer Johansson, Ziwei Ouyang, M. Beleggia, Cathrine Frandsen

TL;DR
This paper introduces printable nanocomposites with superparamagnetic maghemite particles suitable for microinductor cores, demonstrating high magnetic susceptibility, low hysteresis, and potential for integration into microfabrication processes.
Contribution
It presents a new printable magnetic nanocomposite with well-dispersed superparamagnetic particles, suitable for microinductor applications, with detailed magnetic characterization and fabrication demonstrations.
Findings
High magnetic volume susceptibility (up to 17)
Negligible hysteresis at low frequency
Eddy current-free and suitable for microfabrication
Abstract
We here present printable and castable magnetic nanocomposites containing superparamagnetic 113 nm -FeO particles in an insulating poly-vinyl alcohol polymer matrix. The nanocomposites feature well-dispersed particles with volume fractions between 10 and 45 \%, as confirmed by small-angle neutron scattering. The magnetic volume susceptibility is as high as 17, together with negligible hysteresis at low frequency, and constant AC-response up to the high-kHz range. Measured hysteresis curves at 100-900 kHz with up to 110 mT induced -fields in the nanocomposite show that power losses depend on -field squared, and frequency to the power of 1-1.3. The only loss mechanism in the nanocomposite is hysteresis losses at 100 kHz frequencies, where the largest particles in the 113 nm distribution transition from the superparamagnetic to blocked regime. To mitigate…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
