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Endowed with flexible surface coordination and synergetic electronic status, bimetallic particles serve as promising heterogeneous catalysts as their microstructures evolved sensitively to the treatment atmospheres, whereas knowledge of dynamic manners is less. Herein, utilizing environmental transmission electron microscopy (ETEM), the reconstructions of Cu3Pd particles in oxidization/reduction atmospheres were explored at atomic scale. Specifically, bare Cu3Pd particles went through a phase separation of CuOx and PdOx during in situ oxidation, and subsequent agglomeration after H2 reduction. While protected in a silica shell, the confined Cu3Pd particles were oxidized into Cu1.5Pd0.5O2 phase after air calcination and subsequently restructured into versatile configurations during H2 reduction. Specifically, hollow Cu3Pd alloy architecture with Pd enriched layer near surface as reduced at 200 °C. Further rising 400 to 600 °C, it yielded disordered Cu3Pd alloys with slightly Pd atoms enrichment at outmost surface. The dynamical behaviors of single Cu1.5Pd0.5O2 particle during in situ reduction have been visualized in ETEM, wherein a series of deformation, elongation and rotation is involved during the hollow architecture firstly formation, and then vanished into a Cu3Pd solid solution nanoparticle. The tunable microstructures of Cu3Pd@SiO2 driven by redox atmospheres demonstrate efficient approach for precisely regulating the chemical environments of constrained bimetallic nanocatalysts. [ABSTRACT FROM AUTHOR]

The lubricating oil system is a significant component of aviation engine lubrication and cooling, and the scavenge pipe is an essential component of the lubricating oil system. Accurately identifying and understanding the flow state of the scavenge pipe is very important. This article establishes a visualization test bench for a 45-degree inclined scavenge pipe, with upward and downward flow directions, respectively. The test temperature is 370 K, and a high-speed camera captures the changes in the two-phase flow inside the pipeline. Based on high-speed photography photos, we develop software for analyzing the flow characteristics of bubbles inside the tube and explore the influence of gas phase conversion velocity and liquid phase conversion velocity on the apparent velocity of bubbles inside the tube. Multiple algorithms were used to develop the model by combining machine learning with speed and accuracy to establish a data regression prediction model for the apparent velocity of bubbles inside the tube. Through calculation and analysis, it was found that the root mean square error of the prediction model using the BP neural network algorithm was the lowest, and the decision coefficient of the prediction model using the support vector machine algorithm was the highest. [ABSTRACT FROM AUTHOR]

Located at an average elevation of approximately 2000 m, the Minjiang River Basin (MJB), a key tributary of the Upper Yangtze River, straddles the Western Sichuan Plateau and the Sichuan Basin. Vegetation here is crucial for human life, providing oxygen and energy. However, the influence of climatic variables, human activities, and rugged terrain on vegetation vitality is still debated. This study mainly leverages data from the Normalized Difference Vegetation Index (NDVI), meteorological stations data, and land use data. Analytical techniques include trend analysis, partial correlation coefficient analysis (PCC), principal component analysis (PCA), and partial least squares structural equation modeling (PLS-SEM). Results indicate a stable upward trend in vegetation growth with minimal fluctuations, with a growth rate of 0.95 × 10−3/a (p < 0.01). PCC analysis shows a positive correlation between NDVI and key climatic elements in over 60% of the area. The areas with significant vegetation growth had the highest average PCC. PCA and PLS-SEM identify temperature and precipitation as primary growth drivers, while elevation and land use intensity hinder growth. The MJB landscape reveals thresholds and tipping points, with specific temperature and precipitation benchmarks varying by elevation, delineating the boundary between flourishing vegetation and growth inhibition. [ABSTRACT FROM AUTHOR]

In the modern aerospace industry, the importance of a lubrication system is self-evident for aero-engines, and the aero-engine bearing chamber return line is an even more challenging environment, as it involves a complex two-phase flow. The designer of the scavenge pipe needs to have an accurate understanding of the flow conditions in the scavenge pipe. This paper establishes a visual scavenge pipe test system. The flow direction was vertical flow, the test temperature was 370 k, and a high-speed camera was used to take high-definition flow photographs, which can observe the three main flow types: bubble flow, slug flow, and annular flow. Code program was created to analyze many pictures taken to obtain the apparent flow rate and perimeter of bubbles in the pipe under different flow conditions and to explore the gas–liquid two-phase flow in the scavenge pipe. A support vector machine (SVM) was used for data regression prediction, and the converted velocities of the gas–oil phases were inputted as eigenvalues to obtain the predicted values of bubble-flow velocity. The bubble-flow analysis prediction model established in this paper has a good prediction effect with root mean square error RMSE = 0.0172, which can more objectively and accurately describe the bubble-flow characteristics in the scavenge pipe. [ABSTRACT FROM AUTHOR]