abstract
We present an investigation into the magnetic sensing performance of magnetoelectric bilayered metglas/bidomain LiNbO3 long thin bars operating in a cantilever or free vibrating regime and under quasi-static and low-frequency resonant conditions. Bidomain single crystals of Y + 128 degrees-cut LiNbO3 were engineered by an improved diffusion annealing technique with a polarization macrodomain structure of the 'head-to-head' and 'tail-to-tail' type. Long composite bars with lengths of 30, 40 and 45 mm, as well as with and without attached small tip proof masses, were studied. ME coefficients as large as 550 V (cm.Oe)(-1), corresponding to a conversion ratio of 27.5 V Oe(-1), were obtained under resonance conditions at frequencies of the order of 100 Hz in magnetic bias fields as low as 2 Oe. Equivalent magnetic noise spectral densities down to 120 pT Hz(-1/2 )at 10 Hz and to 68 pT Hz(-1/2) at a resonance frequency as low as 81 Hz were obtained for the 45mm long cantilever bar with a tip proof mass of 1.2g. In the same composite without any added mass the magnetic noise was shown to be as low as 37 pT Hz(-1/2) at a resonance frequency of 244 Hz and 1.2 pT Hz(-1/2) at 1335 Hz in a fixed cantilever and free vibrating regimes, respectively. A simple unidimensional dynamic model predicted the possibility to drop the low-frequency magnetic noise by more than one order of magnitude in case all the extrinsic noise sources are suppressed, especially those related to external vibrations, and the thickness ratio of the magnetic-to-piezoelectric phases is optimized. Thus, we have shown that such systems might find use in simple and sensitive room-temperature low-frequency magnetic sensors, e.g. for biomedical applications.
keywords
LITHIUM-NIOBATE; DEMAGNETIZING FACTORS; NOISE; OPTIMIZATION; ENHANCEMENT; SENSORS
subject category
Physics
authors
Turutin, AV; Vidal, JV; Kubasov, IV; Kislyuk, AM; Malinkovich, MD; Parkhomenko, YN; Kobeleval, SP; Kholkin, AL; Sobolev, NA
our authors
Groups
G2 - Photonic, Electronic and Magnetic Materials
G6 - Virtual Materials and Artificial Intelligence
acknowledgements
This work was developed in the framework of the projects: Increase Competitiveness Program of NUST "MISiS" project No. B100-H1-Pi 71 supported by the Ministry of Education and Science of the Russian Federation, I3N/FSCOSD (Ref. FCT UID/CTM/50025/2013) and CICECO-Aveiro Institute of Materials-POCI-01-0247-FEDER-007678 SGH 'Smart Green Homes' financed by national funds through the FCT/MEC and when applicable co-financed by FEDER under the PT2020 Partnership Agreement, and European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No. 778308 'SPIN-MULTIFILM'. JVV acknowledges BOSCH for the grant BI/UI89/5339/2017. AVT was supported by the Ministry of Education and Science of the Russian Federation (Scholarship of the President of the Russian Federation for study abroad in the academic year 2017/18 No. 564) and grant 'UMNIK' by the Fund of Promoting Innovation No. 11028GU/2016.