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A VO2(B) ultrathin vertical nanosheet array was prepared by the hydrothermal method. The influence of the concentration of oxalic acid on the crystal structure and room-temperature NO2 sensing performance was studied. The morphology and crystal structure of the nanosheets were characterized by scanning electron microscopy, transmission electron microscopy, and X-ray diffraction. Room-temperature gas sensing measurements of this structure to NO2 with a concentration span from 0.5 to 5 ppm were carried out. The experimental results showed that the thickness of the vertical VO2(B) nanosheet was lower than 20 nm and close to the 2 times Debye length of VO2(B). The response of the sensor based on this structure to 5 ppm NO2 was up to 2.03, and the detection limit was 20 ppb. Its high response performance was due to the fact that the target gas could completely control the entire conductive path by forming depletion layers on the surface of VO2(B) nanosheets. Density functional theory was used to analyze the adsorption of NO2 on the VO2(B) surface. It is found that the band gap of VO2(B) becomes narrower and the Fermi level moves to the valence band after NO2 adsorption, and the density of states near the Fermi level increases significantly. This ultrathin vertical nanosheet array structure can make VO2(B) detect NO2 with high sensitivity at room temperature and therefore has potential applications in the field of low-power-consumption gas sensors.

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Artificial synapses with the capability of optical sensing and synaptic functions are fundamental components to construct neuromorphic visual systems. However, most reported artificial optical synapses require a combination of optical and electrical stimuli to achieve bidirectional synaptic conductance modulation, leading to an increase in the processing time and system complexity. Here, an all-optically controlled artificial synapse based on the graphene/titanium dioxide (TiO 2 ) quantum dot heterostructure is reported, whose conductance could be reversibly tuned by the effects of light-induced oxygen adsorption and desorption. Synaptic behaviors, such as excitatory and inhibitory, short-term and long-term plasticity, and learning−forgetting processes, are implemented using the device. An artificial neural network simulator based on the artificial synapse was used to train and recognize handwritten digits with a recognition rate of 92.2%. Furthermore, a 5 × 5 optical synaptic array that could simultaneously sense and memorize light stimuli was fabricated, mimicking the sensing and memory functionality of the retina. Such an all-optically controlled artificial synapse shows a promising prospect in the application of perception, learning, and memory tasks for future neuromorphic visual systems.

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