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In this communication, a pattern-reconfigurable antenna (PRA) array with wide beam-steering angle and stable-enhanced gain based on Huygens sources is presented. It consists of a cascaded $1 \times $ magnetic dipole (MD) array and an in-phase $2 \times $ electric monopole (EM) array. The MD array is realized as TE $_{(1/2)01}$ -mode substrate-integrated waveguide (SIW) cavity with meander-type phased inverters and a single port. The EM array, composed of six trapezoidal metal patches, is orthogonally located along both sides of the SIW cavity. In addition, the EM array is connected/disconnected to the SIW cavity by six p-i-n diodes. The MD array and EM array form a pair of Huygens sources, which provide the foundation for reconfigurable patterns. By controlling the on/off states of p-i-n diodes, a nine-type Huygens source array with different tilted beams is realized. The measured overlapped impedance bandwidth of the proposed antenna is 280 MHz from 4.62 to 4.9 GHz. Across the whole bandwidth, the measured realized gain is stable with a 9.66-dBi peak value. Besides, when the different reconfigurable patterns are steering, the E-plane pattern can cover 162° beamwidth with a fluctuation of less than 1 dB. Compared with the existing PRA-based beam-steering arrays, the proposed design provides a wide beam-steering angle, stable gain, and simple feeding method.

Array nulling synthesis (ANS) problem is usually solved iteratively via the optimization algorithms, resulting the processing time-consuming. The communication proposes a fast ANS algorithm based on a self-paced learning convolutional neural network (SPL-CNN). The CNN includes the convolution layers and the fully connected (FC) layers. The convolution layers serve as an extractor to learn the features of the covariance matrix of the array factor (CMAF) in the nulling regions, while the FC layers serve as a regressor to predict the array excitation and the radiation pattern. However, the significant differences among the CMAF elements for different wide nulling regions make the convolutional neural network (CNN) difficult to quickly learn the samples. To overcome this shortcoming, SPL is adopted to make the CNN select samples from “easy” to “hard” in the training process by comparing the loss function and the hyper-parameter. Moreover, the quantum particle swarm optimization (QPSO) is utilized to tune the architecture hyper-parameter values of the SPL-CNN. After the SPL-CNN is well-trained, the proposed algorithm can calculate the array excitation without iteration. Numerical simulations for point/wide nullings are carried out to validate the superiority of the proposed algorithm by comparing it with the convex algorithms and the other existing data-driven algorithms.

Heteroatoms in situ-doped hierarchical porous hollow-activated carbons (HPHACs) have been prepared innovatively by pyrolyzation of setaria viridis combined with alkaline activation for the first time. The micro-morphology, pore structure, chemical compositions, and electrochemical properties are researched in detail. The obtained HPHACs are served as outstanding electrode materials in electrochemical energy storage ascribe to the particular hierarchical porous and hollow structure, and the precursor setaria viridis is advantage of eco-friendly as well as cost-effective. Electrochemical measurement results of the HPHACs electrodes exhibit not only high specific capacitance of 350 F g−1at 0.2 A g−1, and impressive surface specific capacitance (Cs) of 49.9 μF cm−2, but also substantial rate capability of 68% retention (238 F g−1at 10 A g−1) and good cycle stability with 99% retention over 5000 cycles at 5 A g−1in 6 M KOH. Besides, the symmetrical supercapacitor device based on the HPHACs electrodes exhibits excellent energy density of 49.5 Wh kg−1at power density of 175 W kg−1, but still maintains favorable energy density of 32.0 Wh kg−1at current density of 1 A g−1in 1-ethy-3-methylimidazolium tetrafluoroborate (EMIMBF4) ionic liquid electrolyte, and the excellent cycle stability behaviour shows the nearly 97% ratio capacitance retention of the initial capacitance after 10,000 cycles at current density of 2 A g−1. Overall, the results indicate that HPHACs derived from setaria viridis have appealing electrochemical performances thus are promising electrode materials for supercapacitor devices and large-scale applications.

The letter adaptively minimizes the gap between the mainlobe region and the sidelobe region for the shaped-beam pattern synthesis (SBPS) via antenna array. The gap setting is empirically in the existing SBPS methods, full of uncertainty and flexibility. Therefore, a gap autominimization algorithm is proposed. First, the original SBPS problem is approximated to be a quasi-convex one by relaxing the nonconvex cost function from $\mathit{l}_{0}$-norm to $\mathit{l}_{1}$-norm with a penalty constraint. Then, the referenced shaped-beam pattern (SBP) is synthesized with existing approaches. The proposed algorithm iteratively shrinks the mainlobe/sidelobe gap by synthesizing an SBP similar to the referenced one. As a result, the proposed algorithm enables obtaining the minimum gap while obtaining the desired sidelobe level with a narrow mainlobe ripple level. The effectiveness of the proposed algorithm is validated via numerical simulations from different perspectives.

Recently, $\beta $ -Ga2O3 solar-blind photodetectors (PDs) have been of great interest, and the attempts to improve their device performance have been extensively explored. However, surface passivation, a critical technique that has been widely explored in other electronic and optoelectronic devices for the enhancement of performance and ambient stability, has been rarely reported in the field of $\beta $ -Ga2O3 solar-blind PDs. Herein, the $\beta $ -Ga2O3 metal-insulator–semiconductor-insulator–metal (MISIM) solar-blind PDs passivated with ultrathin Al2O3 layers were fabricated and comparatively investigated. The physical mechanisms behind the passivation technique are in-depth discussed as well. It was found that the device passivated with a 3-nm Al2O3 layer not only has lower dark current but also exhibits improved photocurrent as well as responsivity due to the change of dominant carrier-transport mechanism at metal/semiconductor (M/S) interface and the effective physical isolation that eliminates the oxygen chemisorption/desorption process at the active area, resulting in the highest competitive responsivity and specific detectivity of 83.3 A/W and $1.35\times 10^{{15}}$ Jones, respectively, among the fabricated devices. This reveals that the key factor for the comprehensive improvement of $\beta $ -Ga2O3 solar-blind PDs lies in precisely controlling the deposition thickness of Al2O3 passivation layer. This work not only provides an alternative pathway for comprehensively enhancing the performance of $\beta $ -Ga2O3 solar-blind PD, but also suggests an effective technique for reducing its energy consumption and noise level without degrading the other performance such as sensitivity and response speed.