A comparative experimental and theoretical investigation on energy storage performance of CoSe2, NiSe2 and MnSe2 nanostructures
Source abstract
Abstract
Abstract We have investigated a comparative supercapacitor performance of the transition metal (Co, Ni and Mn) diselenides nanostructures assembled in a symmetric electrode configuration. Supercapacitor electrodes based on the transition metal selenides fabricated on Ni-foam which delivered specific capacitance values of 148, 139 and 50 F/g respectively for CoSe2, NiSe2 and MnSe2 which demonstrates the better performance of CoSe2 as compared to NiSe2 and MnSe2. Moreover, CoSe2 electrode showed good cycling stability with capacitance retention of 98.46% over 3000 cycles, whereas NiSe2 and MnSe2 showed the cycling stability of 59.44% and 86.70% respectively. We have explored the origin of enhanced energy storage performance of CoSe2 both by supported experiments and density functional theory investigation. For theoretical studies, we have computed electronic and structural properties of CoSe2, NiSe2 and MnSe2, using state of art Density Functional Theory (DFT) simulations. Enhanced density of states around fermi level, largest tunnel size, highest surface area per atom and highest quantum capacitance for CoSe2 justifies superior supercapacitance behavior of CoSe2 compared to NiSe2 and MnSe2 as observed in the experiment. Quantum capacitance is highest for CoSe2 and it follows the trend CoSe2 > NiSe2 > MnSe2, similar to the trend for specific capacitance obtained in the experiment. Thus, the present work on comparative investigation on energy storage performance of transition metal diselenides demonstrate that CoSe2 can be further exploited as a potential material for energy storage application.
Source: openaire · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Supercapacitor Materials and Fabrication · Electrocatalysts for Energy Conversion · Advanced battery technologies research
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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