journal article·2020-07

Binder assisted self-assembly of graphene oxide/Mn2O3 nanocomposite electrode on Ni foam for efficient supercapacitor application

Hoseong Han, Qadeer Akbar Sial, Shankara S. Kalanur, Hyngtak Seo

Ceramics International· Volume 46· Issue 10
Publisher page

Abstract

Abstract The electrochemical supercapacitor is the emerging technology of the future for various energy storage systems, that provides higher power density and higher energy density compared to conventional batteries. The effective performances of the supercapacitor mainly depend on the employed material systems and recently manganese oxides (Mn2O3) have attracted significant interest because of its pseudocapacitive performance, environmentally friendly and low cost. However, the poor cyclic stability and conductive properties, of Mn2O3 hinder its efficient utilization as supercapacitor electrode material. Therefore, in this work, we propose a unique synthesis strategy of fabricating nanocomposite of graphene oxide (GO)/manganese oxide (Mn2O3) on nickel foam (NiF) via a self-assembly process using poly (diallyl dimethylammonium chloride) (PDDA) as a binder. The synthesized NiF/PDDA/GO/Mn2O3 electrode exhibited an excellent capacitance of 916.5 F/g at a scan-rate 50 mV/s owing to its large surface area and porous structure. Furthermore, a specific capacitance of 998.2 F/g was recorded at a current density of 10 A/g in galvanostatic charge/discharge test having excellent stability of 5000 cycles retaining its capacitance up to 91.5%. Importantly, our experimental results indicate that the proposed self-assembly method is facile, cost-effective and produces uniformly assembled nanocomposite. It showed promising super-capacitance performance for practical and large-scale implementation.
Source: openaire · Quality: candidate unverified · Source record ↗

Research context

OpenAlex topics: Supercapacitor Materials and Fabrication · Advancements in Battery Materials · Graphene research and applications

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

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