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Terahertz (THz) waves have gained considerable attention in the rising 6G communication due to their large bandwidth. However, the cost and power consumption become the major constraints for the commercialization of 6G THz systems as the frequency increases. Reconfigurable intelligent surface (RIS) comprising active metasurfaces and digital controllers has been proposed for beamforming in the 6G multiple-input-multiple-output systems, showing good potential to suppress the system size, weight, and power consumption (SWaP). Currently, their controlling diodes can hardly work up to THz frequencies. Therefore, several active stimuli have been investigated as alternatives. Among them, chalcogenide phase-change material Ge2Sb2Te5 (GST) addresses large modulation depth, picosecond switching speed, and non-volatile properties. Notably, the non-volatile GST may enable RIS systems with memory and low control power. This work briefly reviews the advances of GST-tuned THz metamaterials (MTMs), discusses the current obstacles to overcome, and gives a perspective of GST applications in the rising 6G communications.

Due to the high cost of ultra‐pure water supply and the mismatch between water sources and renewable energy distribution, the large‐scale production of green hydrogen through seawater electrolysis has generated significant interest. This presents an attractive potential technology within the framework of carbon‐neutral energy production. However, owing to the complex composition of seawater, particularly the competitive oxidation reactions and corrosion issues involving Cl−, seawater electrolysis has suffered from low selectivity and poor stability in oxygen evolution reaction (OER), which severely impact the efficiency of hydrogen production and hinder the practical applications. To further promote in‐depth research and practical applications of seawater electrolysis, this review introduces the principles, key advantages, and challenges of seawater electrolysis. Specifically, the design strategies are categorized for highly active OER electrocatalysts for seawater electrolysis, including catalyst design, design of chemical reaction systems, and other special process design. To ensure long‐term operational stability of seawater electrolysis, various strategies such as employing self‐supporting materials, surface protection strategies, and electrolyzer design, are discussed. Finally, current challenges and future prospects for the industrialization of seawater electrolysis are proposed and discussed. It is expected that this review provides new insights for large‐scale seawater‐based hydrogen production in the future. [ABSTRACT FROM AUTHOR]

Replacing the slow protonation process of oxygen evolution reaction (OER) with the fast protonation of alcohol electro‐oxidation can decrease the driving potentials, thus improving overall efficiency of electrochemical devices. However, the formation of effective catalytic sites for alcohol oxidation remains challenging in accelerating protonation to inhibit metal leaching and improve catalyst stability. Herein, asymmetric Ni‐O‐Cr sites are constructed by alloying Cr into the NiO matrix to optimize coordination environments, showing significantly enhanced stability during alcohol electro‐oxidation. The asymmetric Ni‐O‐Cr can maintain constant valence states of Cr and Ni during alcohol oxidation, efficiently suppressing metal dissolution even at high oxidation potentials. In situ electrochemical characterizations combined with theoretical calculations indicate that asymmetric Ni‐O‐Cr can improve adsorption and activation of OH* and alcohol molecules compared to pure NiO, thus increasing anodic kinetics. The theoretical results also indicate that the smaller gap of Ni 3d‐O 2p in asymmetric Ni‐O‐Cr strengthens charge transfer, leading to fast protonation of catalytic sites with enhanced stability. This work gives insights into boosting anodic protonation using asymmetric sites‐enriched solid‐solution electrocatalysts. [ABSTRACT FROM AUTHOR]

Large discrepancy of sample distribution resulting from speed fluctuation is a great challenge to mechanical equipment health monitoring. Existing fault diagnosis methods are often limited by the acquisition mechanism of single-modal measurement. Considering the above problems, a multidimensional features dynamically adjusted adaptive network (MFDAAN) fused vibro-acoustic modal signals is proposed in this paper. The MFDAAN considers the context information of activation features by Funnel activation (FReLU) function to activate the vibro-acoustic signal features. In order to obtain fusion features, the multidimensional features of vibro-acoustic signals are dynamically adjusted at different stages by channel attention mechanisms, which is capable of considering the global information. Wasserstein distance is employed in the domain-adversarial training strategy to improve the property extracting domain-invariant features. The effectiveness of the MFDAAN is verified by cross-domain fault diagnosis experiments in two different scenarios. The results show that the MFDAAN can achieve good diagnostic effect for the tasks set of cross-domain fault diagnosis. [ABSTRACT FROM AUTHOR]

In recent years, transfer learning has been widely used in mechanical fault diagnosis with some achievements. However, most transfer learning methods do not perform well in diagnosis when the speed and load change simultaneously. Inspired by the adversarial learning mechanism, a transfer learning method named attention mechanism-guided domain adversarial network (AMDAN) is proposed in this paper. AMDAN regards the convolutional neural networks (CNNs) as the generator of the domain adversarial network to learn mutually invariant features and the domain classifier as the discriminator of the domain adversarial network. Attention mechanism is introduced to take into account the interchannel and intraspace feature fusion to improve the training efficiency. Then, multi-kernel maximum mean discrepancy (MK-MMD) is used to measure the distance of different feature spaces to achieve domain alignment. Finally, the superiority of AMDAN is verified by two sets of gear fault diagnosis experiments. The experimental results show that AMDAN has the highest classification accuracy and the strongest generalization ability compared with other methods. [ABSTRACT FROM AUTHOR]