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Efficient hydrogen storage and transportation are crucial for the sustainable development of human society. Ammonia, with a hydrogen storage density of up to 17.6 wt%, is considered an ideal energy carrier for large-scale hydrogen storage and has great potential for development and application in the "hydrogen economy". However, achieving ammonia decomposition to hydrogen under mild conditions is challenging, and therefore, the development of suitable catalysts is essential. Metal oxide-based catalysts are commonly used in the industry. This paper presents a comprehensive review of single and composite metal oxide catalysts for ammonia decomposition catalysis. The focus is on analyzing the conformational relationships and interactions between metal oxide carriers and active metal sites. The aim is to develop new and efficient metal oxide-based catalysts for large-scale green ammonia decomposition. [ABSTRACT FROM AUTHOR]

Over the last few decades, there has been a growing discourse surrounding environmental and health issues stemming from drinking water and the discharge of effluents into the environment. The rapid advancement of various sewage treatment methodologies has prompted a thorough exploration of promising materials to capitalize on their benefits. Metal–organic frameworks (MOFs), as porous materials, have garnered considerable attention from researchers in recent years. These materials boast exceptional properties: unparalleled porosity, expansive specific surface areas, unique electronic characteristics including semi-conductivity, and a versatile affinity for organic molecules. These attributes have fueled a spike in research activity. This paper reviews the current MOF-based wastewater removal technologies, including separation, catalysis, and related pollutant monitoring methods, and briefly introduces the basic mechanism of some methods. The scale production problems faced by MOF in water treatment applications are evaluated, and two pioneering methods for MOF mass production are highlighted. In closing, we propose targeted recommendations and future perspectives to navigate the challenges of MOF implementation in water purification, enhancing the efficiency of material synthesis for environmental stewardship. [ABSTRACT FROM AUTHOR]

In order to clarify the temporal and spatial changes in the internal photothermal environment in an open-field agrivoltaic system (OAVS), this paper took the OAVS in eastern China as the research object and divided the internal area into the southern area, middle area and northern area, according to the spatial structure. Further, a photothermal environment test was conducted in the above three areas in the summer and winter. The results showed that the summer average daylight rate (Rm-avg) in the middle area was 66.6%, while the Rm-avg in the other two areas was about 20%, with no significant difference. In the winter, the light environment in the southern area was slightly better, and the Rm-avg in the above three areas was 26.4%, 24.7% and 19.7%, respectively. On the whole, the relationship between the thermal environmental factors and the solar radiation intensity was consistent. Further, a 3D model of an OAVS was established using Autodesk Ecotect Analysis 2011, and the internal light environment was simulated. Compared with the measured values, the relative error was less than 10%, which verified the reliability of the OAVS model. Then, the model was used to reveal the temporal and spatial changes in the light environment of the OAVS. The simulation results showed that the daylighting rate in the summer from the ground to the height of the fig canopy inside the system was 20.7% to 61.5%. In the winter, the daylighting rate from the ground to the height of the fig canopy inside the system was 17.7% to 36.4%. The effectiveness of the OAVS in reducing the level of solar radiation intensity depended on the time of day and the angle of the sun. At the spatial scale, due to the strong consumption of light by photovoltaic panels, there was a strong horizontal and vertical light environment gradient inside the system. In conclusion, the photothermal environment research of an OAVS based on Autodesk Ecotect Analysis 2011 can not only provide a basis for agricultural production and structural design such as span, height and the laying density of PV panels, but also expand its application to regions with different latitudes and longitudes and specific climates. [ABSTRACT FROM AUTHOR]

To understand the pollution characteristics of volatile organic compounds (VOCs) in the glass deep-processing industry, samples were collected using polyvinyl fluoride bags and quickly transferred to summa tanks for GC/MS/FID analysis. The emission characteristics of VOCs, the ozone formation potential and the secondary aerosol formation potential were studied. The results showed that the VOCs emitted by the six enterprises were mainly aromatics and OVOCs, accounting for 35% to 97% of the emissions, with high emission loads of alkanes and halocarbons from individual enterprises. The stack emissions from Enterprise 2 were as high as 38 mg/m3, while the emissions from the remaining five enterprises were all in the range of 1.7~4.1 mg/m3, probably because the terminal treatment facilities were not updated in a timely manner, resulting in excessive stack emissions from Enterprise 2. The characteristic pollutants, including OVOCs, aromatics and alkanes, which are mainly derived from spray painting and gluing, were screened in the six enterprises. Aromatics and OVOCs contributed the most to the ozone formation potential in the six enterprises, with some enterprises having a high contribution from alkanes and alkenes. On the basis of the secondary aerosol formation potential, toluene, benzene, ethylbenzene, o-xylene and m/p-xylene account for 98% of the six enterprises' emissions. Glass enterprises should prioritise the control of benzene and OVOCs emissions. The glass processing industry mainly emits aromatics, OVOCs and alkanes. Through a preliminary study on the emission characteristics of VOCs in the glass deep-processing industry, we provided basic data for the reduction and control of VOCs in the glass deep-processing industry in China. [ABSTRACT FROM AUTHOR]

Excessive carbon emissions will cause irreversible damage to the human living environment. Therefore, carbon neutrality has become an inevitable choice for sustainable development. Marine fishery is an essential pathway for biological carbon sequestration. However, it is also a source of carbon emissions. From this perspective, an in-depth assessment of the performance of carbon emissions and sinks from marine fisheries is required to achieve the goal of carbon neutrality. This paper measured the carbon emissions, carbon sinks, and net carbon emissions of marine fisheries in nine coastal provinces of China from 2005 to 2020 for the first time. Based on the calculation results, the log-mean decomposition index method was used to analyze the driving factors of net carbon emissions. The results suggested that, from 2005 to 2020, both the carbon emissions and carbon sinks of China's marine fisheries increased, and the net carbon emissions showed a downward trend. There were variations in the performance of carbon emissions, carbon sinks, and net carbon emissions in different provinces, and only Shandong could consistently achieve carbon neutrality. Fujian and Liaoning achieved carbon neutrality in 2020. In terms of the contribution of each factor, the industrial structure was the main positive driver, and carbon intensity was the main negative driver. Based on the empirical results, this paper suggested increasing the implementation of the carbon tax policy, establishing a farming compensation mechanism and promoting carbon emissions trading and international blue carbon trading. The results could give a reference for the energy conservation and emission reduction of marine fisheries while enhancing the ecological benefits of their carbon sinks and helping to achieve the carbon neutrality target. [ABSTRACT FROM AUTHOR]

Three-dimensional (3D) point cloud classification methods based on deep learning have good classification performance; however, they adapt poorly to diverse datasets and their classification accuracy must be improved. Therefore, FSDCNet, a neural network model based on the fusion of static and dynamic convolution, is proposed and applied for multiview 3D point cloud classification in this paper. FSDCNet devises a view selection method with fixed and random viewpoints, which effectively avoids the overfitting caused by the traditional fixed viewpoint. A local feature extraction operator of dynamic and static convolution adaptive weight fusion was designed to improve the model's adaptability to different types of datasets. To address the problems of large parameters and high computational complexity associated with the current methods of dynamic convolution, a lightweight and adaptive dynamic convolution operator was developed. In addition, FSDCNet builds a global attention pooling, integrating the most crucial information on different view features to the greatest extent. Due to these characteristics, FSDCNet is more adaptable, can extract more fine-grained detailed information, and can improve the classification accuracy of point cloud data. The proposed method was applied to the ModelNet40 and Sydney Urban Objects datasets. In these experiments, FSDCNet outperformed its counterparts, achieving state-of-the-art point cloud classification accuracy. For the ModelNet40 dataset, the overall accuracy (OA) and average accuracy (AA) of FSDCNet in a single view reached 93.8% and 91.2%, respectively, which were superior to those values for many other methods using 6 and 12 views. FSDCNet obtained the best results for 6 and 12 views, achieving 94.6%, 93.3%, 95.3%, and 93.6% in OA and AA metrics, respectively. For the Sydney Urban Objects dataset, FSDCNet achieved an OA and F1 score of 81.2% and 80.1% in a single view, respectively, which were higher than most of the compared methods. In 6 and 12 views, FSDCNet reached an OA of 85.3% and 83.6% and an F1 score of 85.5% and 83.7%, respectively. [ABSTRACT FROM AUTHOR]

Current research on the reconstruction of hyperspectral images from RGB images using deep learning mainly focuses on learning complex mappings through deeper and wider convolutional neural networks (CNNs). However, the reconstruction accuracy of the hyperspectral image is not high and among other issues the model for generating these images takes up too much storage space. In this study, we propose the double ghost convolution attention mechanism network (DGCAMN) framework for the reconstruction of a single RGB image to improve the accuracy of spectral reconstruction and reduce the storage occupied by the model. The proposed DGCAMN consists of a double ghost residual attention block (DGRAB) module and optimal nonlocal block (ONB). DGRAB module uses GhostNet and PRELU activation functions to reduce the calculation parameters of the data and reduce the storage size of the generative model. At the same time, the proposed double output feature Convolutional Block Attention Module (DOFCBAM) is used to capture the texture details on the feature map to maximize the content of the reconstructed hyperspectral image. In the proposed ONB, the Argmax activation function is used to obtain the region with the most abundant feature information and maximize the most useful feature parameters. This helps to improve the accuracy of spectral reconstruction. These contributions enable the DGCAMN framework to achieve the highest spectral accuracy with minimal storage consumption. The proposed method has been applied to the NTIRE 2020 dataset. Experimental results show that the proposed DGCAMN method outperforms the spectral accuracy reconstructed by advanced deep learning methods and greatly reduces storage consumption. [ABSTRACT FROM AUTHOR]

The connection structure in the convolutional layers of most deep learning-based algorithms used for the classification of hyperspectral images (HSIs) has typically been in the forward direction. In this study, an end-to-end alternately updated spectral–spatial convolutional network (AUSSC) with a recurrent feedback structure is used to learn refined spectral and spatial features for HSI classification. The proposed AUSSC includes alternating updated blocks in which each layer serves as both an input and an output for the other layers. The AUSSC can refine spectral and spatial features many times under fixed parameters. A center loss function is introduced as an auxiliary objective function to improve the discrimination of features acquired by the model. Additionally, the AUSSC utilizes smaller convolutional kernels than other convolutional neural network (CNN)-based methods to reduce the number of parameters and alleviate overfitting. The proposed method was implemented on four HSI data sets, as follows: Indian Pines, Kennedy Space Center, Salinas Scene, and Houston. Experimental results demonstrated that the proposed AUSSC outperformed the HSI classification accuracy obtained by state-of-the-art deep learning-based methods with a small number of training samples. [ABSTRACT FROM AUTHOR]

In this paper, an AlGaN/GaN Schottky barrier diode (SBD) with the T-anode located deep into the bottom buffer layer in combination with field plates (TAI-BBF FPs SBD) is proposed. The electrical characteristics of the proposed structure and the conventional AlGaN/GaN SBD with gated edge termination (GET SBD) were simulated and compared using a Technology Computer Aided Design (TCAD) tool. The results proved that the breakdown voltage (VBK) in the proposed structure was tremendously improved when compared to the GET SBD. This enhancement is attributed to the suppression of the anode tunneling current by the T-anode and the redistribution of the electric field in the anode–cathode region induced by the field plates (FPs). Moreover, the T-anode had a negligible effect on the two-dimensional electron gas (2DEG) in the channel layer, so there is no deterioration in the forward characteristics. After being optimized, the proposed structure exhibited a low turn-on voltage (VT) of 0.53 V and a specific on-resistance (RON,sp) of 0.32 mΩ·cm2, which was similar to the GET SBD. Meanwhile, the TAI-BBF FP SBD with an anode-cathode spacing of 5 μm achieved a VBK of 1252 V, which was enhanced almost six times compared to the GET SBD with a VBK of 213 V. [ABSTRACT FROM AUTHOR]

In this paper, an edge termination structure, referred to as step-double-zone junction termination extension (Step-DZ-JTE), is proposed. Step-DZ-JTE further improves the distribution of the electric field (EF) by its own step shape. Step-DZ-JTE and other termination structures are investigated for comparison using numerical simulations. Step-DZ-JTE greatly reduces the sensitivity of breakdown voltage (BV) and surface charges (SC). For a 30-μm thick epi-layer, the optimized Step-DZ-JTE shows 90% of the theoretical BV with a wide tolerance of 12.2 × 1012 cm−2 to the JTE dose and 85% of the theoretical BV with an improved tolerance of 3.7 × 1012 cm−2 to the positive SC are obtained. Furthermore, when combined with the field plate technique, the performance of the Step-DZ-JTE is further improved. [ABSTRACT FROM AUTHOR]

The plant-specific WRKY transcriptional regulatory factors have been proven to play vital roles in plant growth, development, and responses to biotic and abiotic stresses. However, there are few studies on the WRKY gene family in sugarcane (Saccharum spp.). In the present study, the characterization of a new subgroup, IIc WRKY protein ScWRKY3, from a Saccharum hybrid cultivar is reported. The ScWRKY3 protein was localized in the nucleus of Nicotiana benthamiana leaves and showed no transcriptional activation activity and no toxic effects on the yeast strain Y2HGold. An interaction between ScWRKY3 and a reported sugarcane protein ScWRKY4, was confirmed in the nucleus. The ScWRKY3 gene had the highest expression level in sugarcane stem pith. The transcript of ScWRKY3 was stable in the smut-resistant Saccharum hybrid cultivar Yacheng05-179, while it was down-regulated in the smut-susceptible Saccharum hybrid cultivar ROC22 during inoculation with the smut pathogen (Sporisorium scitamineum) at 0–72 h. ScWRKY3 was remarkably up-regulated by sodium chloride (NaCl), polyethylene glycol (PEG), and plant hormone abscisic acid (ABA), but it was down-regulated by salicylic acid (SA) and methyl jasmonate (MeJA). Moreover, transient overexpression of the ScWRKY3 gene in N. benthamiana indicated a negative regulation during challenges with the fungal pathogen Fusarium solani var. coeruleum or the bacterial pathogen Ralstonia solanacearum in N. benthamiana. The findings of the present study should accelerate future research on the identification and functional characterization of the WRKY family in sugarcane. [ABSTRACT FROM AUTHOR]

Catalases, which consist of multiple structural isoforms, catalyze the decomposition of hydrogen peroxide in cells to prevent membrane lipid peroxidation. In this study, a group II catalase gene ScCAT2 (GenBank Accession No. KF528830) was isolated from sugarcane genotype Yacheng05-179. ScCAT2 encoded a predicted protein of 493 amino acid residues, including a catalase active site signature (FARERIPERVVHARGAS) and a heme-ligand signature (RVFAYADTQ). Subcellular localization experiments showed that the ScCAT2 protein was distributed in the cytoplasm, plasma membrane, and nucleus of Nicotiana benthamiana epidermal cells. Quantitative real-time polymerase chain reaction (qRT-PCR) analysis indicated that the ScCAT2 gene was ubiquitously expressed in sugarcane tissues, with expression levels from high to low in stem skin, stem pith, roots, buds, and leaves. ScCAT2 mRNA expression was upregulated after treatment with abscisic acid (ABA), sodium chloride (NaCl), polyethylene glycol (PEG), and 4 °C low temperature, but downregulated by salicylic acid (SA), methyl jasmonate (MeJA), and copper chloride (CuCl2). Moreover, tolerance of Escherichia coli Rosetta cells carrying pET-32a-ScCAT2 was enhanced by NaCl stress, but not by CuCl2 stress. Sporisorium scitamineum infection of 10 different sugarcane genotypes showed that except for YZ03-258, FN40, and FN39, ScCAT2 transcript abundance in four smut-resistant cultivars (Yacheng05-179, YZ01-1413, YT96-86, and LC05-136) significantly increased at the early stage (1 day post-inoculation), and was decreased or did not change in the two smut-medium-susceptibility cultivars (ROC22 and GT02-467), and one smut-susceptible cultivar (YZ03-103) from 0 to 3 dpi. Meanwhile, the N. benthamiana leaves that transiently overexpressed ScCAT2 exhibited less severe disease symptoms, more intense 3,3′-diaminobenzidine (DAB) staining, and higher expression levels of tobacco immune-related marker genes than the control after inoculation with tobacco pathogen Ralstonia solanacearum or Fusarium solani var. coeruleum. These results indicate that ScCAT2 plays a positive role in immune responses during plant–pathogen interactions, as well as in salt, drought, and cold stresses. [ABSTRACT FROM AUTHOR]

Recent research shows that deep-learning-derived methods based on a deep convolutional neural network have high accuracy when applied to hyperspectral image (HSI) classification, but long training times. To reduce the training time and improve accuracy, in this paper we propose an end-to-end fast dense spectral–spatial convolution (FDSSC) framework for HSI classification. The FDSSC framework uses different convolutional kernel sizes to extract spectral and spatial features separately, and the "valid" convolution method to reduce the high dimensions. Densely-connected structures—the input of each convolution consisting of the output of all previous convolution layers—was used for deep learning of features, leading to extremely accurate classification. To increase speed and prevent overfitting, the FDSSC framework uses a dynamic learning rate, parametric rectified linear units, batch normalization, and dropout layers. These attributes enable the FDSSC framework to achieve accuracy within as few as 80 epochs. The experimental results show that with the Indian Pines, Kennedy Space Center, and University of Pavia datasets, the proposed FDSSC framework achieved state-of-the-art performance compared with existing deep-learning-based methods while significantly reducing the training time. [ABSTRACT FROM AUTHOR]

Magnetorheological elastomers (MRE) is known as an intelligent material constituted of a rubber matrix as well as soft magnetic particles. Silane coupling agents are used to raise the interplay between the inorganic particles and rubber matrix. Silane coupling agent, bis-[-3-(trimethylsilyl propyl)tetra sulfide] (Si69), was picked for comparison of its reinforcing efficiency in the MRE with various vulcanization systems: a conventional (CV), semi-efficient (semi-EV), and efficient (EV) vulcanization system. The outcome illustrated that not only was there improved Si69 surface hydrophobicity of the magnetic particles, but also enhanced Si69 in the interplay between the rubber matrix and magnetic particles. On one hand, the saturated induced magnetic modulus and zero magnetic field modulus of MRE was increased in the vulcanization system, and the loss factor was reduced after the magnetic particles were modified by Si69. On the other hand, the effect of Si69 on the MRE depended on the vulcanization system. The Si69 provided better enhancements in the EV system due to effects of the sulfur contribution of Si69.