journal article·2017-07

A new insight into PAM/graphene-based adsorption of water-soluble aromatic pollutants

Huawen Hu, Menglei Chang, Min Zhang, Xiaowen Wang, Dongchu Chen

Journal of Materials Science· Volume 52· Issue 14
Publisher page

Abstract

Graphene materials have been extensively verified as a good adsorbent for tackling wastewater containing various aromatic pollutants; however, little attention has been paid to understanding the graphene-based adsorption mechanism. Here, a systematic work is performed to prepare a series of graphene oxide (GO)-incorporated polyacrylamide hydrogels, with a three-dimensional (3D) monolithic structure, followed by in situ conversion of GO to reduced graphene oxide. Such a method not only enables the prevention of irreversible aggregation of graphene sheets during the in situ reduction, but also facilitates the clarification of the relationship between the structure and adsorption properties of the graphene materials. This work presents two kinds of graphene-based 3D monolithic adsorbents for either selective separation of the cationic aromatic pollutant from anionic one or uptake both of them for the total purification purpose. More importantly, we effectively unravel that the sp2-conjugated carbon network of the graphene materials plays a pivotal role in purifying the aromatic organic pollutants through π–π stacking interactions that outstrip electrostatic attraction interactions. Therefore, the present work is expected to provide an impetus toward exploration of high-performance graphene-based materials for various applications, especially environmental remediation, on the basis of effectively impeding self-aggregation of graphene sheets and judiciously modulating their intrinsic structure.
Source: openaire · Quality: candidate unverified · Source record ↗

Research context

OpenAlex topics: Graphene research and applications · Graphene and Nanomaterials Applications · Adsorption and biosorption for pollutant removal

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

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