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Long-term memory and synapse-like dynamics in two-dimensional nanofluidic channels.

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Title: Long-term memory and synapse-like dynamics in two-dimensional nanofluidic channels.
Authors: Robin, P., Emmerich, T., Ismail, A., Niguès, A., You, Y., Nam, G.-H., Keerthi, A., Siria, A., Geim, A. K., Radha, B., Bocquet, L.
Source: Science (pre-March 2025). 1/13/2023, Vol. 379 Issue 6628, p161-167. 7p. 5 Color Photographs.
Subjects: Ion channels, Adsorption (Chemistry), Nanofluidics, Artificial neural networks, Biomedical materials
Abstract: Fine-tuned ion transport across nanoscale pores is key to many biological processes, including neurotransmission. Recent advances have enabled the confinement of water and ions to two dimensions, unveiling transport properties inaccessible at larger scales and triggering hopes of reproducing the ionic machinery of biological systems. Here we report experiments demonstrating the emergence of memory in the transport of aqueous electrolytes across (sub)nanoscale channels. We unveil two types of nanofluidic memristors depending on channel material and confinement, with memory ranging from minutes to hours. We explain how large time scales could emerge from interfacial processes such as ionic self-assembly or surface adsorption. Such behavior allowed us to implement Hebbian learning with nanofluidic systems. This result lays the foundation for biomimetic computations on aqueous electrolytic chips. [ABSTRACT FROM AUTHOR]
Copyright of Science (pre-March 2025) is the property of American Association for the Advancement of Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Psychology and Behavioral Sciences Collection
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  Data: Long-term memory and synapse-like dynamics in two-dimensional nanofluidic channels.
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  Data: <searchLink fieldCode="JN" term="%22Science+%28pre-March+2025%29%22">Science (pre-March 2025)</searchLink>. 1/13/2023, Vol. 379 Issue 6628, p161-167. 7p. 5 Color Photographs.
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  Data: <searchLink fieldCode="DE" term="%22Ion+channels%22">Ion channels</searchLink><br /><searchLink fieldCode="DE" term="%22Adsorption+%28Chemistry%29%22">Adsorption (Chemistry)</searchLink><br /><searchLink fieldCode="DE" term="%22Nanofluidics%22">Nanofluidics</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Biomedical+materials%22">Biomedical materials</searchLink>
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  Data: Fine-tuned ion transport across nanoscale pores is key to many biological processes, including neurotransmission. Recent advances have enabled the confinement of water and ions to two dimensions, unveiling transport properties inaccessible at larger scales and triggering hopes of reproducing the ionic machinery of biological systems. Here we report experiments demonstrating the emergence of memory in the transport of aqueous electrolytes across (sub)nanoscale channels. We unveil two types of nanofluidic memristors depending on channel material and confinement, with memory ranging from minutes to hours. We explain how large time scales could emerge from interfacial processes such as ionic self-assembly or surface adsorption. Such behavior allowed us to implement Hebbian learning with nanofluidic systems. This result lays the foundation for biomimetic computations on aqueous electrolytic chips. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Science (pre-March 2025) is the property of American Association for the Advancement of Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1126/science.adc9931
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              Text: 1/13/2023
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