Synthetic fuels produced from CO₂ show promise in combating climate change. The reverse water gas shift (RWGS) reaction is the key to opening the CO₂ molecule, and CO serves as a versatile intermediate for creating various hydrocarbons. Mo-based catalysts are of great interest for RWGS reactions featured for their stability and strong metal-oxygen interactions. Our study identified Mo defects as the intrinsic origin of the high activity of cluster Mo₂C for CO₂-selective hydrogenation. Specifically, we found that defected Mo₂C clusters supported on nitrogen-doped graphene exhibited exceptional catalytic performance, attaining a reaction rate of 6.3 g_CO/g_cat/h at 400 °C with over 99% CO selectivity and good stability. Such a catalyst outperformed other Mo-based catalysts and noble metal-based catalysts in terms of facile dissociation of CO₂, highly selective hydrogenation, and nonbarrier liberation of CO. Our study revealed that as a potential descriptor, the atomic magnetism linearly correlates to the liberation capacity of CO, and Mo defects facilitated product desorption by reducing the magnetization of the adsorption site. On the other hand, the defects were effective in neutralizing the negative charges of surface hydrogen, which is crucial for selective hydrogenation. Finally, we have successfully demonstrated that the combination of a carbon support and the carbonization process synergistically serves as a feasible strategy for creating rich Mo defects, and biochar can be a low-cost alternative option for large-scale applications.

A frequency-dispersive magnetic material substrate is proposed to achieve the broad bandwidth of metamaterial screens for microwave absorption. Resonance-like ferrites with frequency-dispersive properties at resonant frequencies are applied as substrate. By mixing the multiple types of resonance-like ferrites, the desired bandwidth of frequency-dispersive magnetic material is further broadened. Simulated and measured results show that the proposed metamaterial screen can operate from 7.9 to 14.6 GHz with the reflectivity of less than -10 dB with thickness of only 1.1 mm (0.029 λr,). A figure of merit (FoM) proposed to evaluate the absorption performance of the screen is 20.6 for the proposed screen.

As a special component, inner-wall-shaped parts with a narrow aperture and large cavity play an important role in the field of industrial manufacturing. It is of great significance to accurately measure the full profile of the inner surface of such parts. Line-structured light scanning is a widely used method for inner wall 3D measurement, which is usually applied to linear scanning measurements of the inner wall of pipe-shaped parts. In view of the structural characteristics of narrow-aperture and large-cavity parts, this article establishes a multi-sensor scanning measurement system based on the principle of line-structured light, which adopts rotary scanning instead of the traditional linear scanning measurement method in the system. Additionally, a calibration method is introduced to resolve the challenges associated with the calibration of rotation axis parameters. Considering the structural constraints in the measurement of narrow-aperture and large-cavity parts, a structural optimization algorithm is designed to enable the sensor to achieve a high theoretical measurement resolution while satisfying the geometric constraints of the measured parts. In order to minimize the size of the sensor, the adjacent sub-sensors in the system are arranged in the form of low overlapping fields of view (FOV). To solve the problem of multi-sensor registration under low overlapping FOV, a calibration method based on the structural characteristics of the measurement system itself is proposed, which realizes low-cost and high-precision calibration of the multi-sensor system. Through the repeatability measurement experiment of the spherical cavity parts, the average measurement deviation of the spherical cavity radius was measured to be 6 μm, and the standard deviation was 11.4 μm, which verified the feasibility of the measurement system proposed in this article. By comparing the system calibration method proposed in this article with existing methods, the measurement accuracy of the system is improved by approximately 80%, demonstrating the effectiveness of the proposed method.

Different optical reconstruction methods have stringent requirements on the material or shape of the objects, and have poor generalization ability. To address this issue, this article proposes a stable and reliable target contour recognition algorithm based on omnidirectional illumination using shape from silhouette (SFS). It solves the problem of low signal-to-noise ratio of light signals reflected by materials with high reflectivity or transparency, and achieves performance that is comparable to the state-of-the-art deep learning networks. Moreover, this article solves the blind spot problem of the SFS algorithm in single-image fast reconstruction, and proposes a targeted growth visual hull (VH) method, which further enhances the reconstruction speed of the traditional SFS method while ensuring reconstruction accuracy. This article takes the reconstruction measurement of supermarket products as an example to demonstrate the value of the proposed method in practical applications.

As an important high -energy -consuming industrial load, electrolytic magnesium can continuously adjust the power in a large range. It is of great significance to thoroughly tap the power regulation potential of electrolytic magnesium industrial load for improving the flexibility of power system and promoting the consumption of renewable energy. The assessment of power regulation potential depends on the construction of accurate load models. Thus, it is urgent to construct a delicate model for the active power characteristic of electrolytic magnesium industry. Based on analyzing the industrial process of electrolytic magnesium, this paper firstly combs the main electric equipment of electrolytic magnesium industry. Then based on the electrical characteristics of the main electric equipment, this paper constructed their external characteristic models of representing the relationship between active power and voltage, and constructed the active power characteristic model of electrolytic magnesium load by superimposing the power of above models. Finally, this paper used the measured PMU data of a magnesium factory to identify the key parameters of the model and verified the effectiveness of the model by comparing the measured active power curve with the model’s output active power curve and calculating the simulation error.