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Issue 10, 2018
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Emerging investigator series: design of hydrogel nanocomposites for the detection and removal of pollutants: from nanosheets, network structures, and biocompatibility to machine-learning-assisted design

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Abstract

Due to serious pollution of the world's water resources and the increasing need for clean water sources worldwide, effective methods for the detection and removal of pollutants must be developed. Given the remarkable advantages of low cost, trace detection limits and high removal rates of hydrogel nanocomposites, we focus on the most important and emerging issues in recent research into hydrogel nanocomposites for the detection and removal of pollutants. Such issues include visual detection, enzyme-based biosensors, visible-light photocatalysis, design of 3D network structures, 4D printing and multi-stimuli-responsive hydrogels. The shortcomings and challenges in the practical application of hydrogel nanocomposites are also discussed. Finally, an outlook for future work is proposed, including regeneration of hydrogels promoting commercial and practical applications, extending hydrogels to soil-water systems, improving nanomaterial biocompatibility and advancing machine-learning-assisted materials discovery. This review provides comments and suggestions for the preparation and applications of hydrogel nanomaterials for the detection and treatment of contaminates.

Graphical abstract: Emerging investigator series: design of hydrogel nanocomposites for the detection and removal of pollutants: from nanosheets, network structures, and biocompatibility to machine-learning-assisted design

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Article information


Submitted
24 May 2018
Accepted
09 Sep 2018
First published
10 Sep 2018

Environ. Sci.: Nano, 2018,5, 2216-2240
Article type
Critical Review

Emerging investigator series: design of hydrogel nanocomposites for the detection and removal of pollutants: from nanosheets, network structures, and biocompatibility to machine-learning-assisted design

X. Hou, L. Mu, F. Chen and X. Hu, Environ. Sci.: Nano, 2018, 5, 2216
DOI: 10.1039/C8EN00552D

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