JOURNAL ARTICLE

Functional‐Nanochannel‐Based Artificial Postsynaptic Membrane for Neural Signal Transduction.

  • Published In: Advanced Functional Materials, 2024, v. 34, n. 52. P. 1 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Wang, Senyao; Zhang, Wenyuan; Wu, Minghui; Wu, Yitian; Xu, Guoheng; Liu, Wenchao; Mei, Tingting; CHEN, Lu; Xiao, Kai 3 of 3

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. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:Advanced Functional Materials. 2024/12, Vol. 34, Issue 52, p1
  • Document Type:Article
  • Subject Area:Health and Medicine
  • Publication Date:2024
  • ISSN:1616-301X
  • DOI:10.1002/adfm.202410597
  • Accession Number:181847874
  • Copyright Statement:Copyright of Advanced Functional Materials is the property of Wiley-Blackwell 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.)

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