X-ray photoelectron spectroscopy is the most important and widely used surface analysis technique. This article provides a thorough overview of X-ray spectroscopy, covering its principles, analytical methods, applications, and recent progress. The principle of the technique is explained based on undergraduate-level knowledge. The crucial steps and key aspects of the analysis process are then discussed. The application section illustrates the utilization of X-ray photoelectron spectroscopy for different material categories, along with an introduction of commonly employed techniques and their application characteristics in these domains. This article is intended to be helpful for beginners in this field, including researchers, postgraduate students, and X-ray photoelectron spectroscopy practitioners who still need to become fully acquainted with the technique, enabling them to develop a comprehensive understanding of X-ray photoelectron spectroscopy.

Aeration on a porous plate surface with micropores is an important method for preparing microbubbles. The characteristics of microbubbles generated on the surface of a microporous plate have been studied using a non-invasive digital image recognition method in a transparent squared flow channel. The effects of liquid flow rate, superficial gas velocity, liquid viscosity, liquid surface tension and average micropore size on the plate on the average diameter of bubbles (d_av) were analyzed. The results show that d_av decreases with increasing liquid velocity, increasing superficial gas velocity and decreasing surface tension. With increasing viscosity d_av initially decreases first and then increases, and the minimum value of d_av is obtained with a liquid viscosity of 1.32 mPa·s. Based on the experimental results, empirical correlations are proposed describing the relationship between the average bubble diameter d_av and the liquid Reynolds number, capillary number and gas-liquid ratio.

In an effort to minimize the accidental concentration of nitrogen oxides (NO_x) in the exhaust emission process of nuclear facilities, an implementation scheme of arranging the exhaust pipe outlet of NO_x in the exhaust chimney is proposed. The flow and diffusion process of NO_x in the exhaust chimney during the loading process is studied by numerical simulation methods, and the influence of the emission height and layout orientation of the exhaust pipe on the flow and diffusion of NO_x is discussed. The results show that there is a NO_x concentration area with a clockwise spiral rise in the chimney, and NO_x is discharged from the chimney with a similar mass concentration distribution cloud pattern when the height of the chimney is above 70 m. When the exhaust pipe is located in the center of the chimney, the local maximum mass concentration of NO_x and the mass concentration variation coefficient at the chimney outlet increase with the increase of the exhaust outlet height (10-60 m). However, the local maximum mass concentration of NO_x and the average mass concentration of the cross section at the personnel working area decrease significantly with the increase of the exhaust outlet height (10-30 m). When NO_x is discharged at a low level, the optimal arrangement is an exhaust emission height of 20 m and +90° orientation. When NO_x is emitted at a higher position, the exhaust gas outlet should be arranged in the center of the chimney as far as possible.

Using the cluster FePS₃ as the local model, structure optimization and imaginary frequency verification were carried out using the Gaussian 09 program at the B3LYP/def2-tzvp level, and 12 steady-state configurations were obtained. The atomic charges, electron spin densities and reactivities of these 12 configurations were analyzed. The results show that the S atom in the FePS₃ cluster is an electron acceptor, and the Fe and P atoms are electron donors. Fe atoms are more likely to lose electrons than P atoms. The overall direction of electron flow in the FePS₃ cluster is from Fe and P to S atoms. The excess electrons between Fe and P atoms and between Fe and S atoms are mainly spin-down β-single electrons, and the excess electrons between P and S atoms and between S and S atoms are mainly spin-up α-single electrons. The electron spin densities of configurations 4⁽⁴⁾, 2⁽²⁾, 1⁽²⁾, 2⁽⁴⁾, 1⁽⁴⁾ and 4⁽²⁾ have the best symmetry and the highest stability. Hence these six configurations may be the dominant configurations of the FePS₃ cluster. The frontier orbital (HOMO-LUMO) energy gap of configuration 6⁽²⁾ is the smallest, which is, therefore, most prone to electron transitions and has the highest reactivity. The HOMO-LUMO orbital energy gap of configuration 2⁽⁴⁾ is the largest and this configuration has the lowest reactivity.

The surface of flake zinc powder was modified by a silane coupling agent KH570, and the surface of flake aluminum powder was modified by the single and composite coatings of tetraethyl orthosilicate (TEOS), vinyltriisopropoxysilane (VIPOS) and acrylic acid (AA). Chromium-free Dacromet coatings were prepared with the modified zinc powder and aluminum powder as the basic components. The potentiodynamic polarization curves of the modified zinc-aluminum coatings in 3.5% NaCl solution were measured. The surface of the TEOS/VIPOS modified aluminum powder coating was smooth, with no obvious particles. The dispersion and stability of the coating were good. Compared with the unmodified zinc-aluminum coating, the corrosion rate of the TEOS/VIPOS and TEOS/VIPOS/AA modified aluminum powder coatings decreased significantly. The TEOS/VIPOS modified aluminum powder coating had a smaller corrosion current density (1.01μA/cm²) and corrosion rate (0.011mm/a). After soaking in 20% ammonium nitrate solution for 2h, the surface of TEOS/VIPOS modified aluminum powder coating was smooth and the corrosion resistance was good.

Silver nanosheets with a two-dimensional sheet structure exhibit excellent electrical, optical and chemical properties and are widely used in electronics, catalysis, biology and other fields. The diameter of silver nanosheets prepared by chemical reduction methods is usually less than 1 μm, and the yield is low. In order to solve the above problems, large-sized silver nanosheets were prepared by a chemical reduction method with guanine as the structural inducer and silver nitrate as the silver source. The optimized preparation conditions were as follows: at 0 ℃, the dropping speed was 1.0-1.3 mL/min, the stirring speed was 300 r/min, and the concentrations of guanine and AgNO₃ in solution B were 16.56 mmol/L and 0.50 mol/L, respectively. Under the optimal conditions, large-sized silver nanosheets with a diameter of 10-20 μm were prepared, and the yield of silver nanosheets per unit volume of reaction solution was as high as 15 g/L. The results of X-ray diffraction (XRD) and Raman spectroscopy show that the growth mechanism of silver nanosheets is that the carbonyl oxygen atoms of guanine molecules are selectively adsorbed on the (111) crystal plane of silver crystals to form a coating, and the reduced silver atoms are deposited on the (110) and (100) crystal planes to form silver nanosheets by lateral growth. A conductive adhesive was prepared using the large-sized silver nanosheets prepared under the optimal conditions as fillers with diethylene glycol monobutyl ether and ethanol as solvents. The percolation threshold of the resulting materials was as low as 30%-40% (mass fraction).

High-nickel ternary LiNi_0.85Co_0.11Mn_0.04O₂ (NCM85) is a promising cathode material for high-performance lithium-ion batteries. However, Li loss and cationic mixing are very likely to occur during calcination at high temperature, resulting in the formation of a disordered rock salt phase on the surface of the positive electrode particles. A method of preparation of in situ-modified NCM85 by treatment with H₃PO₄ is proposed. A uniform Li₃PO₄ coating layer is formed on the surface of NCM85, which effectively improves the electrochemical performance of NCM85 and stabilizes the cathode-electrolyte interface. Compared with the pristine NCM85 electrode, the cathode electrode (Li₃PO₄@NCM85) with the appropriate amount of Li₃PO₄ coating exhibited excellent electrochemical performance. The specific discharge capacity was 169.2 mA·h/g after 200 cycles at 0.5 C (200 mA/g) in the voltage range 2.75-4.3 V. The capacity retention rate is as high as 84%, because the residual lithium on the surface of the material is consumed during the formation of the Li₃PO₄ coating layer, which reduces the mixing of Li⁺/Ni²⁺ and enhances the stability of the structure. In addition, the Li₃PO₄ coating can increase the ionic conductivity, accelerate the Li⁺ diffusion rate, and inhibit phase transformation, cation mixing and volume shrinkage. Our study of the influence of surface modification on the interface mechanism of materials should promote the development of cathode electrode materials for the next generation of high-energy lithium-ion batteries.

Stainless steel rods with different thread heights on the surface were subjected to different subcooling degrees under forced coolant flow conditions in order to investigate the effect of thread height on quench boiling. The effects of thread height and subcooling degree on film evolution and boiling heat transfer characteristics during quench boiling were analyzed by combining the measured temperature data with the visualization results. The results show that an increase in thread form height leads to a rise in the minimum film boiling temperature and the critical heat flux, a decrease in the film collapse time, and an improved heat transfer performance in transition and nucleate boiling stages. An increase in subcooling leads to a rise in the surface cooling rate and the minimum film boiling temperature, and a decrease in the quenching boiling time. However, the effect of thread surface morphology on film boiling time decreases with increasing subcooling. Finally, the reliability of the experiment was verified by comparing the experimental minimum film boiling temperature with the fitting empirical formulae in the literature.

The implementation of the double prevention mechanism is an effective approach to addressing prominent issues such as "failure to recognize" and "failure to anticipate" in production safety. However, the construction and operation of the double prevention mechanism are severely impacted by issues such as lagging data informatization, data gaps, and difficulties in verifying data authenticity during the implementation of risk-based control and hazard identification processes within organizations. To tackle these challenges, we propose a solution based on AR (augmented reality) technology, integrating Web application development, Android application development, and WebRTC technology. Our primary focus is on the double prevention mechanism of risk-based control and hazard identification. An information-driven platform, comprising both Web and AR glasses applications, has been developed to facilitate real-time data management. The platform has undergone preliminary validation within enterprises, demonstrating its capability to activate the operation of the double prevention mechanism in a timely and effective manner. Our approach improves the analytical and decision-making efficiency of organizations in dealing with safety risks and should help to ensure the implementation of safety responsibilities.

Phase change temperature is a key parameter that determines the material properties of phase change films and has a huge impact on the data retention force, thermal stability and power consumption of phase change memory. Accurate measurements of phase change temperature are therefore very important. Currently, the main methods for measuring the phase transition temperature of thin film materials in China and elsewhere are variable temperature X-ray diffraction methods and differential scanning calorimetry. The former has limited measurement accuracy, while the latter is a destructive measurement method. Based on the fact that the optical properties of thin film materials will change greatly before and after a phase change, a new phase change temperature measuring instrument for thin film materials has been designed. The hardware part includes a high-temperature heating furnace, a sample stage, and an optical path module. The temperature field uniformity of the high-temperature heating furnace cavity and the ability to control the temperature module were shown to meet the experimental requirements. Finally, films of typical chalcogenide materials, GeTe and Ge₂Sb₂Te₅, were studied using the device. The average phase change temperatures of 10 measurements were 212.7 ℃ and 145.4 ℃, and the standard deviation was 1.70 and 2.32, respectively. The measurement results are stable and meet the expected requirements.

With the rapid development of distributed power and new energy technology, the number of electric vehicles and new energy generation users participating in power trading is increasing. Due to the randomness and uncertainty of electric vehicles and new energy, the existing trading model cannot support the trading demand. Blockchain has the characteristics of decentralization and non-tampering, which can effectively solve the challenges encountered in vehicle-to-grid (V2G) electricity trading. Based on blockchain technology, a transaction rule based on combinatorial auction and rolling aggregation transactions is first proposed and a rolling aggregation transaction model is subsequently designed. Then, based on the rules of the combinatorial auction and rolling aggregation trading mechanism, a smart contract for V2G electricity trading is designed. Finally, in order to verify the feasibility of the scheme, the smart contract is published on the hyperchain blockchain platform allowing simulation experiments. The results show that the scheme proposed in this paper can improve the success rate of the transactions.

The trend toward large-scale development of domestic power generation equipment is evident, and unplanned downtime caused by the failure of its supporting rotating mechanical equipment will cause serious economic losses and major safety issues. Rotor imbalance runs through the entire life cycle of rotating mechanical equipment, and diagnosing the condition of an in-service rotor is particularly important. A rotor imbalance fault diagnosis model based on multi-source domain data extraction and machine learning algorithms is proposed to address the problems of large rotating machinery with multiple vibration measurement points and non-stationary vibration signals. Based on multi-source vibration monitoring data, a vibration signal with rich fault information is first extracted based on cross-correlation coefficients, and a high-dimensional mixed feature space is constructed by fusing multi-domain features such as time domain, frequency domain and time-frequency domain. Secondly, a random neighbourhood embedding method based on t-distribution is used to reveal the feature information of the high-dimensional space, which is reflected as a visualised three-dimensional space. Finally, the nearest node algorithm is used for fault classification to determine the unbalanced mass and phase of the rotor. This proposed model uses the cross-correlation coefficients to characterize the richness of fault information in multi-source data, and the combination of machine learning methods to determine the type of rotor unbalance. The effectiveness of the model was verified by designing unbalance state experiments on rotors with different additional masses at multiple speeds, solving the problems of online diagnosis and on-site dynamic balancing of rotors.

Recognition models based on deep learning are mainly used to solve the problem of coal maceral recognition. However, the parameters of these models are constantly stacked during the calculation process, which leads to increased computational power demand and affects the training efficiency of the model. In light of the above problems, an improved Swin-Transformer model, i.e. DA-ViT, based on the dilated convolutional self-attention (DCSA) mechanism is constructed. The DCSA mechanism enhances the local feature information of the coal maceral group image while retaining its two-dimensional spatial information. By multi-scale decomposition of the large-scale convolution kernel of the coal microscopic image, the relationship between the pixels in different regions of the coal microscopic image is strengthened. The number of image attention parameters is significantly reduced by 81.18%. To strengthen the correlation of morphological features between coal maceral images, a DA-ViT recognition model is proposed by combining DCSA with the improved Swin-Transformer framework. The experimental results show that compared with other existing recognition models, the DA-ViT model can improve the accuracy of the prediction results while significantly reducing the computational requirements of the model. The maximum values of the pixel accuracy (PA) and the mean intersection over union (mIoU) are 92.14% and 63.18%, respectively. The minimum values of the total model parameters (Params) and floating point operations (FLOPs) are 4.95×10⁶ and 8.99×10⁹, respectively.

A population model is investigated using bifurcation analysis with a bifurcation parameter fear factor incorporated in prey. Theoretical analysis confirmed that the fear effect can cause various types of local bifurcation. Numerical simulations demonstrate the complex nature of the dynamics, including the coexistence of bistability, periodic oscillations and the extinction of the population. The results demonstrate that the fear effect plays an important role in the stability of population systems.