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Assembly and Characterization of Biomolecular Memristors Consisting of Ion Channel-doped Lipid Membranes

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Published on: March 9, 2019

Self-Powered Neuromorphic Systems Based on Tribotronics Synaptic Devices.

Kumar Shrestha^1,2, Mohammad Karbalaei Akbari^1,2, Alireza Pourvahabi Anbari^1,3

¹Center for Green Chemistry & Environmental Biotechnology, Ghent University Global Campus, Incheon, Republic of Korea.

Small (Weinheim an Der Bergstrasse, Germany)

|June 15, 2026

Summary

This summary is machine-generated.

Triboelectric nanogenerators (TENGs) power artificial synaptic devices for self-powered neuromorphic computing. These TENG-driven systems offer energy-efficient operation and enable advanced applications by harvesting mechanical energy.

Keywords:

artificial synapse field‐effect transistor neuromorphic device triboelectric nanogenerator tribotronics

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Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

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Published on: March 2, 2015

Area of Science:

Materials Science
Electrical Engineering
Computer Science

Background:

Artificial synaptic devices based on field-effect transistors (FETs) are crucial for neuromorphic computing.
Existing FET-based synaptic devices often require external power, limiting their use in self-powered systems.
Triboelectric nanogenerators (TENGs) offer a solution by harvesting mechanical energy and modulating synaptic signals.

Purpose of the Study:

To review recent advancements in TENG-driven three-terminal artificial synaptic devices.
To explore device architectures, operating principles, and synaptic functionalities.
To discuss challenges and future perspectives in self-powered neuromorphic computing.

Main Methods:

Review of existing literature on TENG-driven artificial synaptic devices.
Analysis of device architectures and operating principles.
Discussion of TENG output characteristics and their impact on synaptic behaviors.

Main Results:

TENGs enable self-powered neuromorphic systems by harvesting ambient mechanical energy.
TENG outputs exhibit unique electrical characteristics (high impedance, pulsed voltage) influencing synaptic functions.
Applications in tactile sensing, human-machine interaction, and auditory perception are emerging.

Conclusions:

TENG-driven artificial synaptic devices represent a significant step towards energy-efficient neuromorphic computing.
Addressing challenges like signal stability and impedance matching is key for future development.
Further research can advance next-generation self-powered neuromorphic systems.