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Selective Release of Different Neurotransmitters Emulated by a p–i–n Junction Synaptic Transistor for Environment‐Responsive Action Control
Author(s) -
Zhang Shuo,
Guo Kexin,
Sun Lin,
Ni Yao,
Liu Lu,
Xu Wenlong,
Yang Lu,
Xu Wentao
Publication year - 2021
Publication title -
advanced materials
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 10.707
H-Index - 527
eISSN - 1521-4095
pISSN - 0935-9648
DOI - 10.1002/adma.202007350
Subject(s) - materials science , neurotransmission , long term potentiation , transistor , neurotransmitter , neuroscience , optoelectronics , biophysics , nanotechnology , electrical engineering , chemistry , receptor , biology , voltage , biochemistry , engineering , central nervous system
The design of the first p–i–n junction synaptic transistor (JST) based on n‐type TiO 2 film covered with poly(methyl methacrylate) (PMMA) and with a p‐type P3HT/PEO nanowire (NW) on top. Except for basic synaptic functions that can be realized by a single neurotransmitter, the electronic device emulates the multiplexed neurotransmission of different neurotransmissions, i.e., glutamate and acetylcholine, for fast switching between short‐ and long‐term plasticity (STP and LTP). This is realized by the special p–i–n junction with hole transport in the p‐type P3HT NW to form STP, and electron transport in the n‐type TiO 2 layer and trapped under the PMMA inversion layer to form LTP. Altering the external input induces changes of the polarity of the charge carriers in the conductive channel, promoting fast switching between STP and LTP modes. When stimulated using two parallel inputs, the response of PMMA/TiO 2 emulates the synergistic effect of taste and aroma on the control of food‐intake in the brain. Because of the bipolarity, the p–i–n JST has excellent reconfigurability, which importantly is attributed to simulate the plasticity of synapses and to mimic how distinct types of gustatory receptor neurons respond to different concentrations of salt. The electronic device lays the technical foundation for the realization of the future complex artificial neural networks.

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