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.

Electron accelerators are essential equipment in science and industry. However, their large footprint, ranging in length up to kilometers, limits their availability. Hence, compact solution to this problem is one of the major trends in advance accelerators. In this paper, a design of micro-scale electron accelerator based on surface plasmons is proposed. Due to the near-field enhancement effect of plasmons, this structure establishes extremely strong field for acceleration within less than $1\mathrm{\mu }\mathrm{m}$. Simulation results shows that driven by infrared laser, the designed accelerator with 5 GV/m peak field deliver more than 0.39 keV energy to electrons. Cascaded in chain, this structure can realize fully electron acceleration, and has the potential of becoming the critical element of future integrated free electron light source lab on chip.

This paper introduces Gaborface-based 2DPCA and (2D)²PCA classification method based on 2D Gaborface matrices rather than transformed ID feature vectors. Two kinds of strategies to use the bank of Gaborfaces are proposed: ensemble Gaborface representation (EGFR) and multichannel Gaborface representation (MGFR). The feasibility of our method is proved with the experimental results on the ORL and Yale databases. In particular, the MGFR-based (2D)² PCA method achieves 100% recognition accuracy for ORL database, and 98.89% accuracy for Yale database with five training samples per class