Hydrogen is receiving a lot of attention from researchers as a clean energy source and one of the most promising sources of energy for the future. Detection of hydrogen before it reaches explosive conditions is a central issue in the safe use of hydrogen. Hydrogen sensors are devices that detect the hydrogen concentration in the environment and are capable of outputting an electrical signal proportional to the magnitude of the hydrogen concentration. Palladium (Pd) has become one of the preferred materials for the preparation of hydrogen sensors due to its strong hydrogen absorbing ability. In this paper, the intrinsic mechanism of hydrogen absorption by Pd metal is revealed, and the performance of various types of Pd-based hydrogen sensors is reviewed.

In this paper, a new sensor structure is designed, which consists of a metal–insulator–metal (MIM) waveguide and a circular protrusion and a rectangular triangular cavity (CPRTC). The characterization of nanoscale sensors is considered using an approximate numerical method (finite element method). The simulation results show that the sharp asymmetric resonance generated by the interaction between the discrete narrow-band mode and the continuous wideband mode is called Fano resonance. The performance of the sensor is considerably influenced by CPRTC. The sensor structure has attained a sensitivity of 3060 nm/RIU and a figure of merit (FOM) of 53.68. In addition, the application of this structure to temperature sensors is also investigated; its sensitivity is 1.493 nm/°C. The structure also has potential for other nanosensors.

In this paper, the corona discharge process of the bar-plate gap at −1 kV DC voltage is simulated using a two-dimensional axisymmetric plasma module. We analyze the variation of air negative corona discharge current, and the distribution morphology of microparticles in different discharge stages in detail. The significance of plasma chemical reactions at some typical time and the distribution characteristics of heavy particles are investigated according to reaction rates. Results show that, in the current rising stage, the collision ionization reactions (e.g., R₁ and R₂) and electron adsorption reaction (e.g., R₃) play a major role, which lead to the increase in charged particles and the formation of an electron avalanche. In the current drop stage, all reaction rates decreased, except for collision ionization and electron attachment, partial charge transfer reactions (e.g., R₈, R₁₀, R₁₁, and R₁₄), and composite reactions (e.g., R₁₆, R₁₇, and R₁₈), which come into play and gradually reduce the number of charged ions in the gap. In the current stabilizing stage, the main chemical reactions are composite reactions (e.g., R₁₆ and R₁₇), then the corona discharge ends. For the heavy particle distribution, O₂⁺ and O₄⁺ are the main positive ions, O₂⁻ is the most abundant negative ions, and the neutral particles are mainly O.

Aiming at the problem that the vibration signals of rolling bearings working in a harsh environment are mixed with many harmonic components and noise signals, while the traditional sparse representation algorithm takes a long time to calculate and has a limited accuracy, a bearing fault feature extraction method based on the ensemble empirical mode decomposition (EEMD) algorithm and improved sparse representation is proposed. Firstly, an improved orthogonal matching pursuit (adapOMP) algorithm is used to separate the harmonic components in the signal to obtain the filtered signal. The processed signal is decomposed by EEMD, and the signal with a kurtosis greater than three is reconstructed. Then, Hankel matrix transformation is carried out to construct the learning dictionary. The K-singular value decomposition (K-SVD) algorithm using the improved termination criterion makes the algorithm have a certain adaptability, and the reconstructed signal is constructed by processing the EEMD results. Through the comparative analysis of the three methods under strong noise, although the K-SVD algorithm can produce good results after being processed by the adapOMP algorithm, the effect of the algorithm is not obvious in the low-frequency range. The method proposed in this paper can effectively extract the impact component from the signal. This will have a positive effect on the extraction of rotating machinery impact features in complex noise environments.

Within this manuscript, we provide a novel Fano resonance-driven micro-nanosensor. Its primary structural components are a metal-insulator-metal (MIM) waveguide, a shield with three disks, and a T-shaped cavity (STDTC). The finite element approach was used to study the gadget in theory. It is found that the adjustment of the structure and the change of the dimensions are closely related to the sensitivity (S) and the quality factor (FOM). Different model structural parameters affect the Fano resonance, which in turn changes the transmission characteristics of the resonator. Through in-depth experimental analysis and selection of appropriate parameters, the sensor sensitivity finally reaches 3020 nm/RIU and the quality factor reaches 51.89. Furthermore, the installation of this microrefractive index sensor allows for the quick and sensitive measurement of glucose levels. It is a positive contribution to the field of optical devices and micro-nano sensors and meets the demand for efficient detection when applied in medical and environmental scenarios.