journal article·2018-12

Ultralight and compressible mussel-inspired dopamine-conjugated poly(aspartic acid)/Fe3+-multifunctionalized graphene aerogel

Bo Wang, YingBo Kang, Tian-Zi Shen, Jang-Kun Song, Ho Seok Park, Ji-Heung Kim

Journal of Materials Science· Volume 53· Issue 24
Publisher page

Abstract

The reduced graphene oxide (rGO) aerogels are particularly attractive owing to their ultralight-weight, high surface area and interconnected macroporosity for energy storage applications. However, pure rGO aerogels are generally weak and brittle to limit their practical applications. To overcome this drawback, a small amount of synthetic dopamine-conjugated poly(aspartic acid) was mixed with graphene oxide to fabricate ultralight rGO aerogels with high porosity and mechanical integrity via hydrothermal reactions at 80 °C and freeze-drying process. In addition, the Fe3+ ionic species was chosen for an additional cross-linker to further strengthen the ultralight poly(aspartic acid/dopamine) functionalized rGO aerogel, abbreviation for PAAD/rGO, through the coordination bonding between Fe3+ and carboxylic acid or catechol groups of both polymer and rGO sheets at pH 9 (PAAD/rGO-Fe❾). The hybrid electrodes of PAAD/rGO-Fe❾ showed the reversible transformation of the Fe3+ tris-catecholate complexes into mono-catecholate promoting Quinone (Q)-hydroquinone (QH2) in 1.0 mol L−1 H2SO4 electrolyte, thus delivering a high specific capacitance of 276.4 F g−1 at 0.5 A g−1 and capacitance retention of 88.2% after 5000 cycles. Moreover, this compressible aerogel provided high strength with 150 kPa without noticeable structural fracture after 80% compression and repeated deformation processes suggesting applications in energy storage and absorption.
Source: openaire · Quality: candidate unverified · Source record ↗

Research context

OpenAlex topics: Supercapacitor Materials and Fabrication · Advanced Sensor and Energy Harvesting Materials · Electrochemical sensors and biosensors

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

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