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  5. Graphene oxide–polyamine preprogrammable nanoreactors with sensing capability for corrosion protection of materials

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Article
English
2023

Graphene oxide–polyamine preprogrammable nanoreactors with sensing capability for corrosion protection of materials

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English
2023
Proceedings of the National Academy of Sciences
Vol 120 (35)
DOI: 10.1073/pnas.2307618120

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Konstantin ‘kostya’  Novoselov
Konstantin ‘kostya’ Novoselov

The University of Manchester

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Kou Yang
Zhitao Hu
Xiaolai Li
+11 more

Abstract

Corrosion is one of the major issues for sustainable manufacturing globally. The annual global cost of corrosion is US$2.5 trillion (approximately 3.4% of the world’s GDP). The traditional ways of corrosion protection (such as barriers or inhibiting) are either not very effective (in the case of barrier protection) or excessively expensive (inhibiting). Here, we demonstrate a concept of nanoreactors, which are able to controllably release or adsorb protons or hydroxides directly on corrosion sites, hence, selectively regulating the corrosion reactions. A single nanoreactor comprises a nanocompartment wrapped around by a pH-sensing membrane represented, respectively, by a halloysite nanotube and a graphene oxide/polyamine envelope. A nanoreactor response is determined by the change of a signaling pH on a given corrosion site. The nanoreactors are self-assembled and suitable for mass-line production. The concept creates sustainable technology for developing smart anticorrosion coatings, which are nontoxic, selective, and inexpensive.

How to cite this publication

Kou Yang, Zhitao Hu, Xiaolai Li, Konstantin G. Nikolaev, Gan Kai Hong, Natalia A. Mamchik, Ivan Erofeev, Utkur Mirsaidov, A. H. Castro Neto, Daniel John Blackwood, Dmitry G. Shchukin, Maxim Trushin, Konstantin ‘kostya’ Novoselov, Daria V. Andreeva (2023). Graphene oxide–polyamine preprogrammable nanoreactors with sensing capability for corrosion protection of materials. Proceedings of the National Academy of Sciences, 120(35), DOI: 10.1073/pnas.2307618120.

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Publication Details

Type

Article

Year

2023

Authors

14

Datasets

0

Total Files

0

Language

English

Journal

Proceedings of the National Academy of Sciences

DOI

10.1073/pnas.2307618120

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