Bibliographic Details
| Title: |
Efficient Carbon‐Based Optoelectronic Synapses for Dynamic Visual Recognition |
| Authors: |
Wenhao Liu, Jihong Wang, Jiahao Guo, Lin Wang, Zhen Gu, Huifeng Wang, Haiping Fang |
| Source: |
Advanced Science, Vol 12, Iss 11, Pp n/a-n/a (2025) |
| Publisher Information: |
Wiley, 2025. |
| Publication Year: |
2025 |
| Collection: |
LCC:Science |
| Subject Terms: |
2D heterostructure, C60, dynamic vision, graphene oxide, optoelectronic synapse, Science |
| More Details: |
Abstract The human visual nervous system excels at recognizing and processing external stimuli, essential for various physiological functions. Biomimetic visual systems leverage biological synapse properties to improve memory encoding and perception. Optoelectronic devices mimicking these synapses can enhance wearable electronics, with layered heterojunction materials being ideal materials for optoelectronic synapses due to their tunable properties and biocompatibility. However, conventional synthesis methods are complex and environmentally harmful, leading to issues such as poor stability and low charge transfer efficiency. Therefore, it is imperative to develop a more efficient, convenient, and eco‐friendly method for preparing layered heterojunction materials. Here, a one‐step ultrasonic method is employed to mix fullerene (C60) with graphene oxide (GO), yielding a homogeneous layered heterojunction composite film via self‐assembly. The biomimetic optoelectronic synapse based on this film achieves 97.3% accuracy in dynamic visual recognition tasks and exhibits capabilities such as synaptic plasticity. Experiments utilizing X‐ray photoelectron spectroscopy (XPS), X‐ray diffraction spectroscopy (XRD), Fourier–transform infrared spectroscopy (FTIR), ultraviolet‐visible spectroscopy (UV‐vis), scanning electron microscopy (SEM), and transmission electron microscopy (TEM) confirms stable π‐π interactions between GO and C60, facilitating electron transfer and prolonging carrier recombination times. The novel approach leveraging high‐density π electron materials advances artificial intelligence and neuromorphic systems. |
| Document Type: |
article |
| File Description: |
electronic resource |
| Language: |
English |
| ISSN: |
2198-3844 |
| Relation: |
https://doaj.org/toc/2198-3844 |
| DOI: |
10.1002/advs.202414319 |
| Access URL: |
https://doaj.org/article/7ee5a144ca514f88b61ce94226659444 |
| Accession Number: |
edsdoj.7ee5a144ca514f88b61ce94226659444 |
| Database: |
Directory of Open Access Journals |
