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Resonance energy transfer (RET) from plasmonic metal nanoparticles (NPs) to two-dimensional (2D) materials enhances the performance of 2D optoelectronic devices and sensors. Herein, single-NP scattering spectroscopy is employed to investigate plasmon-trion and plasmon-exciton RET from single Au nanotriangles (AuNTs) to monolayer MoS, at room temperature. The large quantum confinement and reduced dielectric screening in monolayer MoS facilitates efficient RET between single plasmonic metal NPs and the monolayer. Because of the large exciton binding energy of monolayer MoS, charged excitons (i.e., trions) are observed at room temperature, which enable us to study the plasmon-trion interactions under ambient conditions. Tuning of plasmon-trion and plasmon-exciton RET is further achieved by controlling the dielectric constant of the medium surrounding the AuNT-MoS hybrids. Our observation of switchable plasmon-trion and plasmon-exciton RET inspires new applications of the hybrids of 2D materials and metal nanoparticles.

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Gesture is a natural form of human communication, and it is of great significance in human–computer interaction. In the dynamic gesture recognition method based on deep learning, the key is to obtain comprehensive gesture feature information. Aiming at the problem of inadequate extraction of spatiotemporal features or loss of feature information in current dynamic gesture recognition, a new gesture recognition architecture is proposed, which combines feature fusion network with variant convolutional long short‐term memory (ConvLSTM). The architecture extracts spatiotemporal feature information from local, global and deep aspects, and combines feature fusion to alleviate the loss of feature information. Firstly, local spatiotemporal feature information is extracted from video sequence by 3D residual network based on channel feature fusion. Then the authors use the variant ConvLSTM to learn the global spatiotemporal information of dynamic gesture, and introduce the attention mechanism to change the gate structure of ConvLSTM. Finally, a multi‐feature fusion depthwise separable network is used to learn higher‐level features including depth feature information. The proposed approach obtains very competitive performance on the Jester dataset with the classification accuracies of 95.59%, achieving state‐of‐the‐art performance with 99.65% accuracy on the SKIG (Sheffifield Kinect Gesture) dataset.

Based on a two-step linear optical transformation method, a homogeneous-materials-constructed electromagnetic field concentrator whose energy concentrating ratio varies with the incident angle of the electromagnetic wave is proposed. The design is achieved by compressing the space in different directions with different ratios. Numerical simulations are performed to demonstrate the properties, especially the wave-incident angle-dependent concentrating effect of the concentrator. The relationship between the wave-incident angle and the concentrating ratio is investigated and the corresponding analytical expression is given and verified. This wave-incident angle-dependent concentrator constructed with homogeneous materials shows an advantage of facilitating the implementation in practice and provides a convenient route to continuously adjust the concentrated energy density in a pre-defined strategy.

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