Superlattice-shelled nanocrystalline core structural design for highly sensitive GMI sensors
Source abstract
Abstract
Giant magnetic impendence (GMI) sensors are known as a significant pillar of modern technology, and has many applications in smart society such as automation, navigation and bioengineering. Developing high-sensitive GMI sensors operating under extremely weak magnetic fields is highly desirable yet challenging. Here, we proposed superlattice-shelled nanocrystalline core structural design in the cobalt-based amorphous sensor-core microwires for the highly sensitive GMI sensors. Continuous Co-based amorphous microwires with a length up to thousands of meters and uniform diameter is first fabricated by a scalable modified Taylor–Ulitovsky method and then processed by the picosecond lasering heat treatment. As a result, the microwire exhibits a unique nanostructure consisting of ultrafine nanocrystalline cores surround by the thermal-stable Cr-rich superlattice shell, and hence circumferential magnetic domains of the higher density and intensity. The GMI sensors made from nanostructured Co-based microwires show a sensitivity up to 1743 mV/Oe, the maximum GMI ratio up to 78%, increasing the performance by up to twice and nine times respectively over the GMI sensor made from the amorphous counterpart, and a wide linear range of ± 70000 nT. The correlation between the microstructure, magnetic domain structure and GMI performance is also established and discussed in the nanostructured Co-based microwires. Furthermore, we demonstrate the successful application of the highly sensitive GMI sensor in geomagnetic communication to detect underground energy transmission metal pipes with a detection resolution down to pT-level weak magnetic fields.
Source: expertdata · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Metallic Glasses and Amorphous Alloys · Magnetic properties of thin films · Magnetic Field Sensors Techniques
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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