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Large-Area Superelastic Graphene Aerogels Based on Room-Temperature Reduction Self-Assembly Strategy for Sensing and Particulate Matter (PM2.5 and PM10) Capture

Abstract

Graphene aerogels are emerging low density and superelasticity macroscopic porous materials for varied applications. However, it still remains a challenge to develop a versatile strategy for large-area, high-performance graphene aerogels, which is crucial for their practical applications. Here, we reported a novel room-temperature reduction self-assembly (RTRS) strategy to fabricate large-area graphene aerogels at ambient conditions. The strategy is based on the unique hydrazine hydrate as reducing agents to generate stable microbubbles beneficial to the formation of macroporous graphene hydrogel. Interestingly, the resultant hydrogel followed by a simple pre-freeze treatment, can be naturally dried into graphene aerogels without noticeable volume shrinkage or structure cracking. Benefiting from the mild conditions, a large-area graphene aerogel with a diameter of up to 27 cm was prepared as an example. The as-formed aerogels exhibit stable honeycomb-like coarse-pores structure, low density of 3.6 mg cm-3 and superelasticity (rapidly recoverable from 95% compression) which is suitable for the pressure/strain sensors. Moreover, the aerogel exhibits superior particulate matter adsorption efficiency (PM2.5: 93.7%, PM10: 96.2%) and good recycling ability. Importantly, the preparation process is cost-effective and easily scalable without any special drying techniques and heating processes, which provides an ideal platform for mass production of graphene aerogels toward practical applications.

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Supplementary files

Publication details

The article was received on 09 Mar 2019, accepted on 24 Apr 2019 and first published on 24 Apr 2019


Article type: Paper
DOI: 10.1039/C9NR02071C
Nanoscale, 2019, Accepted Manuscript

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    Large-Area Superelastic Graphene Aerogels Based on Room-Temperature Reduction Self-Assembly Strategy for Sensing and Particulate Matter (PM2.5 and PM10) Capture

    S. Yan, G. Zhang, F. Li, L. Zhang, S. Wang, H. Zhao, Q. Ge and H. Li, Nanoscale, 2019, Accepted Manuscript , DOI: 10.1039/C9NR02071C

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