Highly Selective and Excitable Artificial Ion Channel for Spike Generation
Cation channels of the neuron can be excited by neurotransmitters and have high ionic selectivity, which are crucial for the generation of spikes. Intuitively, if a channel has both a high ionic selectivity and excitability, it may be used to generate spikes. However, integrating the high selectivity and excitability in a single channel remains highly challenging. In this work, we demonstrate an artificial ion channel based on a wetting film between an oil droplet and a glass substrate. The channel height is dominated by the repulsive electrostatic interaction between the glass/water and water/oil interfaces and is therefore highly adaptable to ambient ions and charged surfactant, setting the basis for flexible control of the open/close state and ion selectivity. The channel stays in a closed state unless excited by an anionic surfactant. Once excited, the channel opens wide for monovalent ions but narrows for divalent ions, exhibiting a selectivity up to >6800. By exploiting such excitability and selectivity, we constructed a prototype artificial neuron for spike generation. Using the anionic surfactant to simulate the neurotransmitter, our artificial neuron can automatically and step-wisely open and close the channel, generating a current spike with adjustable magnitude. We expect our work to inspire the development of biomimetic devices for potential neuromorphic computing applications.
Authors
- Shangfa Pan
- Jun Gao (ORCID: https://orcid.org/0000-0003-2106-3134)
- Qianqian Fu (ORCID: https://orcid.org/0000-0002-9996-7769)
- Lei Jiang (ORCID: https://orcid.org/0000-0003-4579-728X)
- Lin Wang (ORCID: https://orcid.org/0000-0002-1539-9865)
- Chang Chen (ORCID: https://orcid.org/0000-0002-9988-930X)
- Xiaolei Chen (ORCID: https://orcid.org/0000-0003-4231-2574)
Institutions
- Energy Institute (GB)
- Chinese Academy of Sciences (CN)
- Shandong Lianxing Energy Group (China) (CN)
- Technical Institute of Physics and Chemistry (CN)
- Qingdao Institute of Bioenergy and Bioprocess Technology (CN)
Publication Details
- Journal
- ACS Applied Materials & Interfaces
- Published
- 2024-12-13
- DOI
- https://doi.org/10.1021/acsami.4c15366
- Citations
- 2
- Primary Topic
- Advanced Memory and Neural Computing
- Type
- article
- Field-Weighted Citation Impact
- 0.42
Funders
- National Natural Science Foundation of China
- Natural Science Foundation of Shandong Province
- Key Technology Research and Development Program of Shandong
- National Key Research and Development Program of China
- Qingdao Institute of Bioenergy and Bioprocess Technology, Chinese Academy of Sciences