journal article·2023-06

Enhanced performance with ionic and organic redox-couple electrolytes on MTMO anchored CQD nanocomposites and renewable carbon-based asymmetric flexible supercapacitor

Prerna Sinha, Kamal K. Kar

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Abstract

The work utilizes a hybrid approach at electrode and electrolyte systems to fabricate a high-energy density supercapacitor. Hybrid nanocomposite electrodes were fabricated using a facile hydrothermal synthesis. The kinetics were studied by preparing in-situ and ex-situ nanocomposites. The in-situ preparation delivers a homogeneous flower petal-like interconnected porous morphology with an appreciable BET surface area of 97 m2 g−1. The actual electrochemical performance was analyzed by accounting for discharge area instead of discharge time to avoid discrepancies related to hybrid energy storage systems. The three-electrode measurement suggests that the electrode delivers an excellent specific capacitance of 3802 F g−1 at 1 A g−1. In the asymmetric configuration, the device reveals excellent actual specific capacitance of 292 F g−1 at 1 A g−1. However, upon adding 0.02 M KI as redox mediators in a traditional electrolyte (6 M KOH), a ∼37% increase in specific capacitance is achieved without the degradation in the device stability. The nanoarchitecture porous morphology and modification in traditional electrolyte enables a high-performance device having an energy density of 165 Wh kg−1 at a power density of 1000 W kg−1, which retain till 50 Wh kg−1 at 25 kW kg−1. The nanocomposite also shows excellent compatibility in a flexible device assembly, delivering a specific capacitance of 192 mF cm−1 at 1 mA cm−1 without deteriorating its performance upon different bending angles. The study claims the design of a low-cost, environmentally benign, and facile approach for developing a high-performance hybrid supercapacitor.
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Research context

OpenAlex topics: Supercapacitor Materials and Fabrication · Advanced battery technologies research · Conducting polymers and applications

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

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