Influence of Cu doping on the local electronic and magnetic properties of ZnO nanostructures.
Richa BhardwajAmardeep BhartiJitendra Pal SinghKeun-Hwa ChaeNavdeep GoyalPublished in: Nanoscale advances (2020)
In this paper, we report the existence of defect induced intrinsic room-temperature ferromagnetism (RTFM) in Cu doped ZnO synthesized via a facile sol-gel route. The wurtzite crystal structure of ZnO remained intact up to certain Cu doping concentrations under the present synthesis environment as confirmed by the Rietveld refined X-ray diffraction pattern with the average crystallite size between 35 and 50 nm. Field emission scanning electron microscopy reveals the formation of bullet-like morphologies for pure and Cu doped ZnO. Diffuse reflectance UV-vis shows a decrease in the energy band gap of ZnO on Cu doping. Further, these ZnO samples exhibit strong visible photoluminescence in the region of 500-700 nm associated with defects/vacancies. Near-edge X-ray absorption fine-structure measurements at Zn, Cu L 3,2 - and O K-edges ruled out the existence of metallic Cu clusters in the synthesized samples (up to 2% doping concentration) supporting the XRD results and providing the evidence of oxygen vacancy mediated ferromagnetism in Cu : ZnO systems. The observed RTFM in Cu doped ZnO nanostructures can be explained by polaronic percolation of bound magnetic polarons formed by oxygen vacancies. Further, extended X-ray absorption fine-structure data at Zn and Cu K-edges provide the local electronic structure information around the absorbing (Zn) atom. The above findings for ZnO nanostructures unwind the cause of magnetism and constitute a significant lift towards realizing spin-related devices and optoelectronic applications.
Keyphrases
- room temperature
- quantum dots
- metal organic framework
- electron microscopy
- ionic liquid
- aqueous solution
- visible light
- high resolution
- reduced graphene oxide
- heavy metals
- risk assessment
- magnetic resonance imaging
- healthcare
- air pollution
- computed tomography
- machine learning
- diabetic rats
- deep learning
- endothelial cells
- mass spectrometry
- crystal structure