with different converting mechanism have been reported, such as electromagnetic generators, [4,5] piezoelectric generators, [6][7][8] electrostatic generators, [9][10][11] and triboelectric nanogenerators. [12][13][14] Triboelectric nanogenerators have shown advantages of a low fabrication cost, high output voltage, and high energy conversion efficiency. However, heat energy is produced and wasted during the triboelectric energy generation process, which limits the output of triboelectric nanogenerators (TENG). [15] On the one hand, noncontact approaches have been used to minimize the energy loss of TENG. On the other hand, it is possible to scavenge the lost energy to improve the output performance of TENG. [16,17] Although pyroelectric nanogenerators harvesting thermal energy from the friction-induced temperature fluctuation have been reported, different thermal harvesting efforts are still needed to enhance the total efficiency of the generators that integrates the energy harvesters and the energy storage devices. [18][19][20] Here, we present triboelectric-thermoelectric hybrid nanogenerator (TTENG), which can harvest mechanical and thermal energy. The TTENG is composed of a 2D rotary TENG Recently developed triboelectric nanogenerators (TENG) with advantages of a low fabrication cost, high output voltage, and high energy conversionefficiency have shown potential applications in harvesting ambient environment energy. However, the heat energy produced and wasted during the triboelectric energy generation process limits the output of TENG. One approach is to design TENG based on a noncontact mode to minimize the energy loss. The other approach is to scavenge the lost energy with a supplementary nanogenerator. In this work, triboelectric-thermoelectric hybrid nanogenerator (TTENG) is fabricated to harvest the energy from ambient environment and the thermal energy from the temperature difference induced by r-TENG friction. At a rotation rate of 500 rpm, r-TENG can produce a constant open-circuit voltage (V oc ) of 200 V and a short-circuit current (I sc ) of 0.06 mA. The thermoelectric nanogenerator (TMENG) with a size of 16 cm 2 can produce a V oc of 0.2 V and an I sc of 20 mA. The experimental results show that the TTENG is a promising method to harvest the ambient mechanical energy. Hybrid NanogeneratorThe ORCID identification number(s) for the author(s) of this article can be found under https://doi.

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High sensitivity detection of terahertz waves can be achieved with a graphene nanomesh as grating to improve the coupling efficiency of the incident terahertz waves and using a graphene nanostructure energy gap to enhance the excitation of plasmon. Herein, the fabrication process of the FET THz detector based on the rectangular GNM (r-GNM) is designed, and the THz detector is developed, including the CVD growth and the wet-process transfer of high quality monolayer graphene films, preparation of r-GNM by electron-beam lithography and oxygen plasma etching, and the fabrication of the gate electrodes on the Si3N4 dielectric layer. The problem that the conductive metal is easy to peel off during the fabrication process of the GNM THz device is mainly discussed. The photoelectric performance of the detector was tested at room temperature. The experimental results show that the sensitivity of the detector is 2.5 A/W (@ 3 THz) at room temperature.

The success of convolutional neural networks (CNNs) benefits from the stacking of convolutional layers, which improves the model's receptive field for image data but also causes a decrease in inference speed. To improve the inference speed of large convolutional network models without sacrificing performance indicators too much, a data-aware adaptive pruning algorithm is proposed. The algorithm consists of two parts, namely, a channel pruning method based on the attention mechanism and a data-aware pruning policy based on reinforcement learning. Experimental results on the CIFAR-100 dataset show that the performance of the proposed pruning algorithm is reduced by only 2.05%, 1.93% and 5.66% after pruning the VGG19, ResNet56 and EfficientNet networks, respectively, but the speedup ratios are 3.63, 3.35, and 1.14, respectively, and the comprehensive pruning performance is the best. In addition, the generalization ability of the reconstruction model is evaluated on the ImageNet dataset and FGVC Aircraft dataset, and the performance of the proposed algorithm is the best, which shows that the proposed algorithm learns data-related information in the pruning process, that is, it is a data-aware algorithm.

The migration model of the (n, 0) zigzag SWNT is established on the basis of Mathiessen’s law to calculate carrier mobility, which are the foundation for performance analysis of electroluminescence light emission. The split-gate technique is used to create p-and n-doped regions in the single-walled carbon nanotube (SWNT) arrays that are separated by a gap with a width of several microns. The LED devices based on SWNT arrays using split-gate technique are fabricated and tested by using an optical measurement system. Compared to the LED with the central gate, the split-gate SWNT LED has enhanced the light generation efficiency of the intrinsic SWNT array segment by decreasing the potential barrier across the junction of the intrinsic SWNT array segment. The results demonstrate the luminescent principle of LED based on SWNT array in theoretical simulation and device measurement.