In these days, the Journal of Nano Energy published a new paper entitled as “Self-powered elementary hybrid magnetoelectric sensor”
There are numerous magnetic field sensors available, but no simple, robust, sensitive sensor for biomedical applications that does not require cryogenic cooling or shielding has yet been developed. In this contribution, a new approach for building a magnetoelectric field sensor is presented, which has the potential to fill this gap. The sensor is based on a resonant cantilever with a piezoelectric readout layer and a pair of opposing permanent magnets. One is attached to the cantilever, and the other one is fixed to a sample holder below. This new concept can be deduced from the most basic composite-based sensor , where the magnets interact analog to two particles in a polymer matrix. The bias-free, empirical measurements show a limit-of-detection of 46 pT/√Hz with a sensitivity of 2170 V/T using the sensor’s resonance frequency of 223.5 Hz under ambient conditions. The sensor fabrication is based on low resolution silicon technology, which promises high compatibility and the possibility to be integrated into MEMS devices. The design of this new sensor can be easily altered and adjusted according to the requirements of the specific sensor application. For example, tuning of the operating resonance frequency cannot solely be modified in the production of the cantilever but also by the arrangement of the permanent magnets. In addition, the concept can also be applied to energy harvesters. Beside possible mechanical excitation, the presence of a magnetic stray field alone allows the sensor to convert 20 μT into a power of 1.31 μW/cm3⋅Oe2. The fact that the device does not require any DC bias field makes it very attractive for energy harvesting applications since this allows a purely passive operation. In this manuscript, the sensor assembly, measurements of directional sensitivity, noise level, limit-of-detection, evaluation for energy harvesting applications from magnetic fields and a quantitative sensor model are presented.