4.7 Article

Room temperature CPP-giant magnetoresistance in Ni/Cu multilayered nanowires

Journal

JOURNAL OF ALLOYS AND COMPOUNDS
Volume 894, Issue -, Pages -

Publisher

ELSEVIER SCIENCE SA
DOI: 10.1016/j.jallcom.2021.162286

Keywords

Magnetoresistance; CPP-GMR; AAO; Multilayered nanowires; FORC; RKKY

Funding

  1. Nanoscience and Nanotechnology Research Center of Kashan University [159023/78]

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In this study, Ni/Cu multilayered nanowires grown by alternating current pulsed electrodeposition into anodic aluminum oxide template were investigated for their current-flow perpendicular to the layer planes giant magnetoresistance (CPP-GMR). The structure and thickness of layers were analyzed using X-ray diffraction and transmission electron microscopy, showing well-ordered multilayers. Magnetic properties were studied through vibrating sample magnetometer and first-order reversal curve analysis, revealing a maximum CPP-GMR of 2.6% for Ni/Cu multilayered NWs.
Current-flow perpendicular to the layer planes giant magnetoresistance (CPP-GMR) of Ni/Cu multilayered nanowires (NWs) grown by an alternating current (AC) pulsed electrodeposition technique into anodic aluminum oxide (AAO) template with a pore diameter of 50 +/- 5 nm is studied at room temperature. The method used for obtaining GMR percentage is the 2-probe resistance measurement of the contact of released and aligned NWs on a glass substrate in the presence of a magnetic field. The structure and thickness of layers are investigated by X-ray diffraction (XRD) and transmission electron microscopy (TEM), indicating the formation of well-ordered multilayers. The magnetic properties are studied by a vibrating sample magnetometer (VSM) and the first-order reversal curve (FORC) analysis. The maximum CPP-GMR of Ni/Cu multilayered NWs is obtained to be 2.6% for 17 and 2.7 nm thick Ni and Cu layers, respectively. The results show that Ruderman-Kittel-Kasuya-Yosida (RKKY) variation behavior of GMR values as a function of Cu spacer thickness is in accordance with theoretical models. (c) 2021 Elsevier B.V. All rights reserved.

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