Functional‐Nanochannel‐Based Artificial Postsynaptic Membrane for Neural Signal Transduction
Abstract Biological‐machine interface (BMI) devices represent a significant step toward adaptive and cognitive technologies. However, current BMI devices emphasize the analysis of electrophysiology and often overlook the chemical information of neurotransmitters in the process of signaling between neurons. To bridge this gap, a light‐gated artificial postsynaptic membrane (APM) is introduced, capable of reading dopamine (DA) released from rat pheochromocytoma cells and regulate neural signal transmission. Like the biological postsynaptic membrane, the APM is a porous membrane functionalized by DA‐specific aptamers and azobenzene (Azo) molecules in different regions. Azo molecules act as a light‐responsive trigger that controls DA release, while DA‐specific aptamers capture DA, which converts its concentration information into an ionic current signal. By light‐enhanced responses to DA exocytosis from rat pheochromocytoma (PC12) cells, the APM confirms its ability to communicate with biological systems, which lays the foundation for developing biological‐machine interaction systems with more advanced functionalities.
Authors
- Kai Xiao (ORCID: https://orcid.org/0000-0001-9829-2707)
- Minghui Wu (ORCID: https://orcid.org/0000-0003-2784-891X)
- Senyao Wang (ORCID: https://orcid.org/0000-0002-8139-558X)
- Tingting Mei (ORCID: https://orcid.org/0009-0006-7543-8527)
- Yitian Wu (ORCID: https://orcid.org/0000-0002-7335-3929)
- Wenyuan Zhang (ORCID: https://orcid.org/0000-0001-8337-1023)
- Wenchao Liu (ORCID: https://orcid.org/0009-0001-5124-2902)
- Chen Lü (ORCID: https://orcid.org/0000-0003-1927-2391)
- Guoheng Xu (ORCID: https://orcid.org/0000-0001-6548-4737)
Institutions
- Shenzhen Polytechnic University (CN)
- Southern University of Science and Technology (CN)
- Technical University of Munich (DE)
Publication Details
- Journal
- Advanced Functional Materials
- Published
- 2024-08-09
- DOI
- https://doi.org/10.1002/adfm.202410597
- Citations
- 8
- Primary Topic
- Advanced Memory and Neural Computing
- Type
- article
- Field-Weighted Citation Impact
- 1.67
Funders
- National Natural Science Foundation of China