Supercapacitive properties of manganese nitride thin film electrodes prepared by reactive magnetron sputtering: Effect of different electrolytes
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Abstract
Abstract Development of metal nitride-based thin film binder-free electrodes is a rapidly emerging area of research for the development of supercapacitors. The manganese nitride (Mn3N2) binder-free thin film electrodes were prepared using DC magnetron sputtering process. X-ray diffraction and the Raman spectroscopy characterization confirmed the formation of the tetragonal phase of Mn3N2 thin film. Field emission scanning electron microscopy revealed that the Mn3N2 particles are in nanoscale range and the particles with pyramidal shape are distributed uniformly on the surface of the film. Further, the Mn3N2 electrodes were examined by cyclic voltammetry and galvanostatic charge-discharge measurements to investigate the supercapacitive properties. The electrochemical measurements were performed on Mn3N2 deposited on conducting stainless steel substrates in different electrolytes (KOH, KCl and, Na2SO4 at 1 M concentration). The effect of various electrolytes on the areal capacitance, cycling stability, capacitance retention of the Mn3N2 electrodes was investigated. The Mn3N2 electrodes show high areal capacitance of 118 mF cm−2 for KOH, 68 mF cm−2 for KCl and 27 mF cm−2 for Na2SO4 at a scan rate of 10 mV/s. Moreover, the Mn3N2 electrodes indicated excellent cycling stability with capacitance retention of 98.5%, 89% and 83% for KOH, KCl, and Na2SO4, electrolytes respectively after 4,000 cycles. A comparative study on the electrochemical supercapacitive properties of the Mn3N2 electrode in different aqueous electrolytes is reported for the next generation electrochemical energy storage devices.
Source: openaire · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Supercapacitor Materials and Fabrication · Advanced battery technologies research · Electrocatalysts for Energy Conversion
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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