Large-area (∼cm2) films of vertical heterostructures formed by alternating graphene and transition-metal dichalcogenide (TMD) alloys are obtained by wet chemical routes followed by a thermal treatment at low temperature. In particular, we synthesized stacked graphene and WxMo1-xS2 alloy phases that were used as hydrogen evolution catalysts. We observed a Tafel slope of 38.7 mV dec-1 and 96 mV onset potential (at current density of 10 mA cm-2) when the heterostructure alloy was annealed at 300 °C. These results indicate that heterostructures formed by graphene and W0.4Mo0.6S2 alloys are far more efficient than WS2 and MoS2 by at least a factor of 2, and they are superior compared to other reported TMD systems. This strategy offers a cheap and low temperature synthesis alternative able to replace Pt in the hydrogen evolution reaction (HER). Furthermore, the catalytic activity of the alloy is stable over time, i.e., the catalytic activity does not experience a significant change even after 1000 cycles. Using density functional theory calculations, we found that this enhanced hydrogen evolution in the WxMo1-xS2 alloys is mainly due to the lower energy barrier created by a favorable overlap of the d-orbitals from the transition metals and the s-orbitals of H2; with the lowest energy barrier occurring for the W0.4Mo0.6S2 alloy. Thus, it is now possible to further improve the performance of the "inert" TMD basal plane via metal alloying, in addition to the previously reported strategies such as creation of point defects, vacancies and edges. The synthesis of graphene/W0.4Mo0.6S2 produced at relatively low temperatures is scalable and could be used as an effective low cost Pt-free catalyst.
Abstract We conducted a mesocosm experiment to examine how ocean acidification (OA) affects communities of prokaryotes and eukaryotes growing on single‐use drinking bottles in subtropical eutrophic waters of the East China Sea. Based on 16S rDNA gene sequencing, simulated high CO 2 significantly altered the prokaryotic community, with the relative abundance of the phylum Planctomycetota increasing by 49%. Under high CO 2 , prokaryotes in the plastisphere had enhanced nitrogen dissimilation and ureolysis, raising the possibility that OA may modify nutrient cycling in subtropical eutrophic waters. The relative abundance of pathogenic and animal parasite bacteria also increased under simulated high CO 2 . Our results show that elevated CO 2 levels significantly affected several animal taxa based on 18S rDNA gene sequencing. For example, Mayorella amoebae were highly resistant, whereas Labyrinthula were sensitive to OA. Thus, OA may alter plastisphere food chains in subtropical eutrophic waters.
The effect of cooling rate on the composition, morphology, size, and volume fraction of the secondary phase in as‐cast Mg–Gd–Y–Zr alloy is investigated. In the study, a casting containing five steps with thickness of 10–50 mm is produced, in which cooling rate ranging from 2.6 to 11.0 K s −1 is created. The secondary phase is characterized using optical microscope (OM), scanning electron microscope (SEM), and electron probe micro‐analyzer (EPMA). The volume fraction of the secondary phase is determined using OM and quantitative metallographic analysis, and Vickers hardness test is conducted to verify the analysis results. The effect of the cooling rate on the volume fraction of the secondary phase is discussed in detail. The result shows that with the increase of the cooling rate, the size of the secondary phase decreases. The effect of the cooling rate on the volume fraction of the secondary phase is complicated somewhat. A comprehensive analysis on the experimental data shows that a critical cooling rate may exist, over which the volume fraction of the secondary phase decreases with the increase of the cooling rate, however under which the volume fraction increases with the increase of the cooling rate.
The heat transfer at the casting-mould interface in resin-bonded sand mould casting of Mg-Gd-Y-Zr alloy was investigated, in which plate-shaped castings with different thicknesses were produced and the temperature variation in the casting and sand mould was recorded. The heat flux and the interfacial heat transfer coefficient (IHTC) were determined by a verified inverse heat conduction model. The results showed that the peak value of the heat flux was about 34~68 kW/m2 and it approximately increased with the decrease of the casting thickness. The averaged heat flux in the solidification process increased from 25.4 kW/m2 to 42.4 kW/m2 when the casting thickness decreased from 35 mm to 10 mm. The IHTC increases rapidly after the liquid metal was poured into the mould, and then decreases for a while, followed by a gentle increase. The averaged IHTC in the solidification process is about 105~183 W/m2K.
In shallow water, reverberation complicates the detection of low-intensity, variable-echo moving targets, such as divers. Traditional methods often fail to distinguish these targets from reverberation, and data-driven methods are constrained by the limited data on intruding targets. This paper introduces the online robust principal component analysis and multimodal anomaly detection (ORMAD) method to address these challenges. ORMAD efficiently performs online low-rank and sparse decomposition while utilizing unsupervised multimodal anomaly detection to enhance detection performance. The multimodal anomaly detection process involves two phases: modality extraction and anomaly detection. During modality extraction, echo data are separated into echo structure and spatial trajectory modalities, providing complementary information that improves the network representation of both reverberation and moving targets. The subsequent anomaly detection phase unsupervisedly learns the modalities of fluctuating reverberation, thereby achieving stable reconstruction while maintaining sensitivity to moving targets. This sensitivity allows effective identification of moving targets by detecting reconstruction loss. Experimental results demonstrate that ORMAD effectively improves detection performance in complex reverberation scenarios. In a real-world sonar dataset, ORMAD increased the average precision for detecting diver targets from 60% to 75% compared to the state-of-the-art method.
Groundwater-based irrigation is an effective buffer against water disconnects during droughts in areas of intensive agriculture. However, it is difficult to implement effective measures to sustainably utilize aquifers due to the unclear understanding of irrigation intensity in the agro-pastoral ecotone. To explore the influence of regional irrigation intensity on groundwater level ( GL ), we investigated the dynamics of Kernel density for irrigation well from 2000 and the changed GL (Δ GL in three groups) in a typical center-pivot irrigation (CPI) area (about 1,000 km 2 ). The results showed that the implementation of CPI systems caused a rapid land-use change from natural grassland (NG) to cultivated pasture (CP). The observed Δ GL in deeper group (0.63 m yr −1 , GL > 20 m) was significantly ( p < 0.05) higher than that in shallower group (0.38 m yr −1 , GL < 10 m) and medium group (0.43 m yr −1 , 10 m < GL < 20 m). The predicted Δ GL and GL were significantly and positively correlated with the CPI well density ( R 2 = 0.447 and 0.429, p < 0.001), respectively, and showed a fitted plane function based on the variables ( R 2 = 0.655, p < 0.001). It indicted that the intensive cropping in the agro-pastoral ecotone profoundly changed regional irrigation intensity, resulting in a rapid response of the GL . To reduce the risk of increased irrigation costs and ensure sustainable availability of groundwater, it’s necessary to control the density of CPI systems in hotspot areas, and implement water-saving measures to balance water usage and recharge rates for sustainable groundwater management.
Forward-looking sonar (FLS) often suffers from complex underwater environments. It is hard to detect small objects from the FLS imagery characterized by low signal-to- noise ratio and low resolution. To highlight the object from severe noise background, we propose an object enhancement method for objects in the regions of interest (ROIs) based on multi-frame image fusion. This method includes two crucial steps: 1) A Fourier-based multi-stage registration algorithm is proposed to solve the problem of a drastic change of object position between frames due to long target distance and rapid change of azimuth angle. 2) A multi-frame fusion algorithm based on self-supervised deep learning is adopted to enhance the ROIs. Experimental results demonstrate that our proposed enhancement method can significantly highlight the objects in the ROIs and has excellent noise suppression in terms of quantitative metrics and visual quality.
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