Colossal dielectric response of HfxZr1-xO2 nanoparticles
Oleksandr S. Pylypchuk, Victor V. Vainberg, Vladimir N. Poroshin, Oksana V. Leshchenko, Victor N. Pavlikov, Irina V. Kondakova, Serhii E. Ivanchenko, Lesya P. Yurchenko, Lesya Demchenko, Anna O. Diachenko, Myroslav V. Karpets, Eugene A. Eliseev, and Anna N. Morozovska

TL;DR
This study demonstrates a colossal dielectric response in small oxygen-deficient HfxZr1-xO2 nanoparticles, revealing potential for silicon-compatible nanomaterials with high dielectric permittivity and ferroelectric-like behavior.
Contribution
It reports the first observation of colossal dielectric permittivity in HfxZr1-xO2 nanoparticles and models their behavior using the Heywang barrier and variable range hopping conduction models.
Findings
Dielectric permittivity reaches up to 1.5×10^5 at low frequencies.
Dielectric response shows a diffuse ferroelectric-paraelectric transition.
Resistivity and dielectric permittivity features are correlated and well-described by combined models.
Abstract
We reveal a colossal dielectric response of small (5 - 10 nm) oxygen-deficient HfxZr1-xO2 nanoparticles (x = 1 - 0.4), prepared by the solid-state organonitrate synthesis. The effective dielectric permittivity of the pressed HfxZr1-xO2 nanopowders has a pronounced maximum at 38 - 88 C, which shape can be fitted by the Curie-Weiss type dependence modified for the diffuse ferroelectric-paraelectric phase transition. The maximal value of the dielectric permittivity increases from 1.5*10^3 (for x = 1) to 1.5*10^5 (for x= 0.4) at low frequencies (~4 Hz); being much smaller, namely changing from 7 (for x = 1) to 20 (for x = 0.4) at high frequencies (~500 kHz). The frequency dispersion of the dielectric permittivity maximum position is almost absent, meanwhile the shape and width of the maximum changes in a complex way with increase in frequency. The temperature dependencies of the dielectric…
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.
