Search

High-performance p-B-n infrared photodetectors based on In 0.53 Ga 0.47 As / Ga 0.51 As 0.49 Sb type-II superlattices with an Al 0.85 Ga 0.15 AsSb barrier on an InP substrate have been demonstrated. These photodetectors exhibit 50% and 100% cutoff wavelengths of ∼ 2.1 μm and ∼ 2.6 μm, respectively. At a bias voltage of −100 mV bias voltage, the device exhibits a peak responsivity of 0.618 A/W at 2.1 μm, corresponding to a quantum efficiency of 36.5%. The device exhibits a saturated dark current shot noise limited specific detectivity (D*) of 4.12 × 10 10 cm · Hz 1 / 2 / W (at a peak responsivity of 2.1 μm) under −100 mV applied bias at 300 K. [ABSTRACT FROM AUTHOR]

Although Ru‐based materials are among the outstanding catalysts for the oxygen evolution reaction (OER), the instability issue still haunts them and impedes the widespread application. The instability of Ru‐based OER catalysts is generally ascribed to the formation of soluble species through the over‐oxidation of Ru and structural decomposition caused by involvement of lattice oxygen. Herein, an effective strategy of selectively activating the lattice oxygen around Ru site is proposed to improve the OER activity and stability. Our synthesized spinel‐type electrocatalyst of Ru and Zn co‐doped Co3O4 showed an ultralow overpotential of 172 mV at 10 mA cm−2 and a long‐term stability reaching to 100 hours at 10 mA cm−2 for alkaline OER. The experimental results and theoretical simulations demonstrated that the lattice oxygen site jointly connected with the octahedral Ru and tetrahedral Zn atoms became more active than other oxygen sites near Ru atom, which further lowered the reaction energy barriers and avoided generating excessive oxygen vacancies to enhance the structural stability of Ru sites. The findings hope to provide a new perspective to improve the catalytic activity of Ru‐incorporated OER catalysts and the stability of lattice‐oxygen‐mediated mechanism. [ABSTRACT FROM AUTHOR]

"The article investigates the macro-cell corrosion behavior and corrosion resistance when the alloyed steel and the carbon steel are used together because the traditional carbon steel is difficult to meet the corrosion resistance and durability of the steel structure of the transmission line in the marine environment." In this paper, a new type of Cr-alloyed corrosion-resistant steel (00Cr10MoV) is used to partially replace carbon structural steel in order to meet the actual needs of corrosion resistance and service life improvement of steel structures for offshore transmission lines. It is important to systematically study the macro-cell corrosion behavior of combinations of the same type of steel and dissimilar steel, induced by the chloride concentration difference in simulated concrete solutions, and employ electrochemical testing methods to scientifically evaluate the corrosion resistance of steel after macro-cell corrosion. The aim is to study and evaluate the macro-cell corrosion behavior of alloyed corrosion-resistant steel and to lay a foundation for its combined use with carbon steel in a chloride corrosion environment to improve the overall corrosion resistance and service life. Under the same concentration difference, the macro-cell corrosion of the alloyed steel combination is milder compared with the carbon steel combination. The corrosion current of the alloyed steel combination at 29 times the concentration difference is only 1/10 of the carbon steel combination. Moreover, at 29 times the concentration difference, the macro-cell corrosion potential of dissimilar steel is only 1/6 of the combined potential of carbon steel combination under the same concentration difference, and the corrosion current is only 1/10 of that of the carbon steel combination. [ABSTRACT FROM AUTHOR]

With the global population growth and increasing food demand, the development of precision agriculture has become particularly critical. In precision agriculture, accurately identifying areas of nitrogen stress in crops and planning precise fertilization paths are crucial. However, traditional coverage path-planning (CPP) typically considers only single-area tasks and overlooks the multi-area tasks CPP. To address this problem, this study proposed a Regional Framework for Coverage Path-Planning for Precision Fertilization (RFCPPF) for crop protection UAVs in multi-area tasks. This framework includes three modules: nitrogen stress spatial distribution extraction, multi-area tasks environmental map construction, and coverage path-planning. Firstly, Sentinel-2 remote-sensing images are processed using the Google Earth Engine (GEE) platform, and the Green Normalized Difference Vegetation Index (GNDVI) is calculated to extract the spatial distribution of nitrogen stress. A multi-area tasks environmental map is constructed to guide multiple UAV agents. Subsequently, improvements based on the Double Deep Q Network (DDQN) are introduced, incorporating Long Short-Term Memory (LSTM) and dueling network structures. Additionally, a multi-objective reward function and a state and action selection strategy suitable for stress area plant protection operations are designed. Simulation experiments verify the superiority of the proposed method in reducing redundant paths and improving coverage efficiency. The proposed improved DDQN achieved an overall step count that is 60.71% of MLP-DDQN and 90.55% of Breadth-First Search–Boustrophedon Algorithm (BFS-BA). Additionally, the total repeated coverage rate was reduced by 7.06% compared to MLP-DDQN and by 8.82% compared to BFS-BA. [ABSTRACT FROM AUTHOR]

Incorporating strengthening phase with a higher thermal stability than traditional L12-ordered Ni3Al phase offers a promising avenue for developing the next-generation Ni-based superalloys. In this study, a powder metallurgy superalloy featuring addition of high-melting-point δ-Nb3Al was fabricated to investigate the high-temperature oxidation behavior at 900 °C. Heterogeneous and accelerated oxidation resulting from the various phase transition products in the alloy were observed. Notably, the preferential oxidation of δ-Nb3Al to Nb2O5 causes obvious surface bulges and cracks. The matrix and Laves phase form oxide layers containing a discontinuous Al2O3. The oxidation mechanism was elucidated through the formation oxidation diagrams based on Gibbs formation energy and total valence of Al2O3. The low Al content in matrix and the high Nb content in Laves phase were identified to cause the discontinuity of surface Al2O3 layer. Additionally, Nb could impede oxygen diffusion within Al2O3 by reducing oxygen vacancy concentration. The findings will provide optimization guidance for the composition design and oxidation protection of novel δ-Nb3Al-strengthened Ni-based superalloys. [ABSTRACT FROM AUTHOR]

Remote sensing scene images contain numerous feature targets with unrelated semantic information, so how to extract to the local key information and semantic features of the image becomes the key to achieving accurate classification. Existing Convolutional Neural Networks (CNNs) mostly concentrate on the global representation of an image and lose the shallow features. To overcome these issues, this paper proposes LPNet for remote sensing scene image classification. First, LPNet employs LKConv to extract the semantic features in the image, while using standard convolution to extract local key information in the image. Additionally, the LPNet applies a shortcut residual concatenation branch to reuse features. Then, parameter fusion combines parameters from previous branches, improving the capacity of the model to obtain a more comprehensive and rich feature representation of the image. Finally, considering the relationship between the classification ability of the model and the depth of feature extraction, the Feature Mixture (FM) Block is used to deepen the model for feature extraction. Comparative experiments on four publicly available datasets show that LPNet provides comparable results to other state‐of‐the‐art methods. The effectiveness of LPNet is further demonstrated by visualizing the effective receptive fields (ERFs). [ABSTRACT FROM AUTHOR]

Air pollution, particularly fine particulate matter (PM2.5), poses a significant health risk, especially in high-density urban areas. Urban green space (UGS) can effectively mitigate this pollution. Despite their potential, strategies for effectively leveraging Land Use/Land Cover (LULC) optimization to combat PM2.5 remain largely unexplored. Ordinary least squares (OLS), geographically weighted regression (GWR) and multiscale geographically weighted regression (MGWR) were employed to investigate the spatial heterogeneity relationship between UGS conversion and PM2.5 fluctuations across various scales and evolutionary stages, developing a multiscale practical framework for LULC synergy in combating air pollution. The areas of UGSs to/from other LULCs, PM2.5 concentrations and corresponding variation zones exhibited significant spatial clustering. These UGS conversions explained more than 65% of the PM2.5 changes in the study area, peaking at 76.4% explanatory power in the fourth stage. Compared to global spatial analysis (OLS: 0–0.48), local spatial regression analysis significantly improved the R2 value (GWR: 0.32–0.75, MGWR: 0.48–0.90), but the fitting quality of local spatial regression analysis decreased with increasing scale, highlighting the importance of scale diagnosis. A 2 km scale was identified as optimal for assessing the spatial heterogeneity impact of UGS and other LULC conversions on PM2.5 changes. Conversion areas from water bodies and bare land to UGSs maintain stable local spatial properties at this scale (bandwidths: 44–99). Our research provides new insights into LULC management and planning, offering a coordinated approach to mitigating urban air pollution. Additionally, a practical framework was established for addressing spatially continuous variables such as PM2.5, revealing effective approaches for addressing urban environmental issues. [ABSTRACT FROM AUTHOR]