The DOI matches its Crossref record; 39 of the 39 references it cites were found in a registry or an index.
Long-term memory and synapse-like dynamics in two-dimensional nanofluidic channels
Open accessAbstract
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.
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Strukov, D. B., Snider, G. S., Stewart, D. R., & Williams, R. S. (2008) · Nature · doi:10.1038/nature06932 · Cited by 11,828
A synaptic model of memory: long-term potentiation in the hippocampus
Bliss, T. V. P., & Collingridge, G. L. (1993) · Nature · doi:10.1038/361031a0 · Cited by 11,566
Memristor—The Missing Circuit Element
Chua, L. (1971) · IEEE Transactions on Circuit Theory · doi:10.1109/tct.1971.1083337 · Cited by 10,268
Short-Term Synaptic Plasticity
Zucker, R. S., & Regehr, W. G. (2002) · Annual Review of Physiology · doi:10.1146/annurev.physiol.64.092501.114547 · Cited by 5,068
Bi, G.-Q., & Poo, M.-M. (1998) · The Journal of Neuroscience · doi:10.1523/jneurosci.18-24-10464.1998 · Cited by 4,744
Spiking Neuron Models: Single Neurons, Populations, Plasticity
Gerstner, W., & Kistler, W. M. (2002) · Cambridge University Press eBooks · doi:10.1017/cbo9780511815706 · Cited by 3,080
Memory devices and applications for in-memory computing
Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R., & Eleftheriou, E. (2020) · Nature Nanotechnology · doi:10.1038/s41565-020-0655-z · Cited by 2,289
Nanofluidics, from bulk to interfaces
Bocquet, L., & Charlaix, E. (2010) · Chem. Soc. Rev. · doi:10.1039/b909366b · Cited by 1,582
Self-citationNeuronal Dynamics: From Single Neurons to Networks and Models of Cognition
Gerstner, W., Kistler, W. M., Naud, R., & Paninski, L. (2014) · doi:10.1017/cbo9781107447615 · Cited by 1,515
Graphene as a subnanometre trans-electrode membrane
Garaj, S., Hubbard, W., Reina, A., Kong, J., Branton, D., & Golovchenko, J. A. (2010) · Nature · doi:10.1038/nature09379 · Cited by 1,439
Metadata
- 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.
- Venue
- Science
- Published
- 2023-01-13
- Volume
- 379
- Issue
- 6628
- Pages
- 161-167
- ISSN
- 0036-8075 (ISSN-L)
- 1095-9203
- OpenAlex
- W4315784189
- Cited by
- 367
- References
- 39
- FWCI
- 26.85
- Type
- journal article
- Language
- en
- Open access
- green
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- Robin, P., Emmerich, T., Ismail, A., Niguès, A., You, Y., Nam, G.-H., Keerthi, A., Siria, A., Geim, A. K., Radha, B., & Bocquet, L. (2023). Long-term memory and synapse-like dynamics in two-dimensional nanofluidic channels. Science. https://doi.org/10.1126/science.adc9931
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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