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  5. Graphene oxide–DNA/graphene oxide–PDDA sandwiched membranes with neuromorphic function

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Article
English
2024

Graphene oxide–DNA/graphene oxide–PDDA sandwiched membranes with neuromorphic function

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0 Files

English
2024
Nanoscale Horizons
Vol 9 (5)
DOI: 10.1039/d3nh00570d

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Konstantin ‘kostya’  Novoselov
Konstantin ‘kostya’ Novoselov

The University of Manchester

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Jia Hui Bong
Sergey Yu. Grebenchuk
Konstantin G. Nikolaev
+7 more

Abstract

The behavior of polyelectrolytes in confined spaces has direct relevance to the protein mediated ion transport in living organisms. In this paper, we govern lithium chloride transport by the interface provided by polyelectrolytes, polycation, poly(diallyldimethylammonium chloride) (PDDA) and, polyanion, double stranded deoxyribonucleic acid (dsDNA), in confined graphene oxide (GO) membranes. Polyelectrolyte-GO interfaces demonstrate neuromorphic functions that were successfully applied with nanochannel ion interactions contributed, resulting in ion memory effects. Excitatory and inhibitory post-synaptic currents were tuned continuously as the number of pulses applied increased accordingly, increasing decay times. Furthermore, we demonstrated the short-term memory of a trained vs untrained device in computation. On account of its simple and safe production along with its robustness and stability, we anticipate our device to be a low dimensional building block for arrays to embed artificial neural networks in hardware for neuromorphic computing. Additionally, incorporating such devices with sensing and actuating parts for a complete feedback loop produces robotics with its own ability to learn by modifying actuation based on sensing data.

How to cite this publication

Jia Hui Bong, Sergey Yu. Grebenchuk, Konstantin G. Nikolaev, C Chee, Kou Yang, Siyu Chen, Denis G. Baranov, Colin R. Woods, Daria V. Andreeva, Konstantin ‘kostya’ Novoselov (2024). Graphene oxide–DNA/graphene oxide–PDDA sandwiched membranes with neuromorphic function. Nanoscale Horizons, 9(5), pp. 863-872, DOI: 10.1039/d3nh00570d.

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Publication Details

Type

Article

Year

2024

Authors

10

Datasets

0

Total Files

0

Language

English

Journal

Nanoscale Horizons

DOI

10.1039/d3nh00570d

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