The high price of state-of-the-art Pt electrocatalysts has plagued the acidic water electrolysis technique for decades. As a cheaper alternative to Pt, ruthenium is considered an inferior hydrogen evolution reaction (...
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The high price of state-of-the-art Pt electrocatalysts has plagued the acidic water electrolysis technique for decades. As a cheaper alternative to Pt, ruthenium is considered an inferior hydrogen evolution reaction (HER) catalyst than Pt due to its high susceptibility to oxidation and loss of activity. Herein, we reveal that the HER activity on Ru based catalysts could surpass Pt via tuning Ru oxidation state. Specifically, RuP clusters encapsulated in few layers of N, P-doped carbon (RuP@NPC) display a minimum over potential of 15.6 mV to deliver 10 mA·cm^(−2). Moreover, we for the first time show that a Ru based catalyst could afford current density up to 4 A·cm^(−2) in a practical water electrolysis cell, with voltage even lower than the Pt/C-based cell, as well as high robustness during 200 h operation. Using a combination of experiment probing and calculation, we postulate that the suitably charged Ru (∼ +2.4) catalytic center is the origin for its superior catalytic behavior. While the moderately charged Ru is empowered with optimized H adsorption behavior, the carbon encapsulation layers protect RuP clusters from over oxidation, thereby conferring the catalyst with high robustness.
Limited by diffraction limit, low spatial resolution is one of the shortcomings of terahertz imaging. Low spatial resolution is also one of the reasons limiting the development of stress measurement using terahertz im...
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Limited by diffraction limit, low spatial resolution is one of the shortcomings of terahertz imaging. Low spatial resolution is also one of the reasons limiting the development of stress measurement using terahertz imaging. In this paper, the full-field stress measurement using Terahertz Time Domain Spectroscopy (THz-TDS) is combined with Super-Resolution Convolutional Neural Network (SRCNN) algorithm to obtain stress fields with high spatial resolution. A modulation model from a plane stress state to a THz-TDS signal is constructed. A large number of simulated sets are obtained to train the SRCNN model. By applying the trained SRCNN model to imaging the numerical and physical stress fields, the improved spatial resolution of stress field calculated from the captured THz-TDS signal is obtained.
Entropy-stabilized multi-component alloys have been considered to be prospective structural materials attributing to their impressive mechanical and functional *** local chemical complexions,microstates and configurat...
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Entropy-stabilized multi-component alloys have been considered to be prospective structural materials attributing to their impressive mechanical and functional *** local chemical complexions,microstates and configurational transformations are essential to reveal the structure–property relationship,thus,to promote the development of advanced multicomponent *** the present work,effects of local lattice distortion(LLD)and microstates of various configurations on the equilibrium volume(V0),total energy,Fermi energy,magnetic moment(μMag)and electron work function(Φ)and bonding structures of the Fe–Mn–Al medium entropy alloy(MEA)have been investigated comprehensively by first-principles *** is found that theΦandμMag of those MEA are proportional to the V 0,which is dominated by lattice *** terms of bonding charge density,both the strengthened clusters or the so-called short-range order structures and the weakly bonded spots or weak spots are *** the presence of weakly bonded Al atoms implies a large LLD/mismatch,the Fe–Mn bonding pairs result in the formation of strengthened clusters,which dominate the local microstates and the configurational *** variations ofμMag are associated with the enhancement of the nearest neighbor magnetic Fe and Mn atoms,attributing to the LLD caused by Al atoms,the local changes in the electronic *** work provides an atomic and electronic insight into the microstate-dominated solid-solution strengthening mechanism of Fe–Mn–Al MEA.
Predicting the thermodynamically stable arrangements of molecular building blocks is crucial for understanding the properties and behaviors of crystalline molecular compounds. Several molecular structure prediction me...
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Predicting the thermodynamically stable arrangements of molecular building blocks is crucial for understanding the properties and behaviors of crystalline molecular compounds. Several molecular structure prediction methods have been proposed, leading to successful applications. However, some evolutionary approaches that involve direct sampling within the infinite atomic coordinate space, without symmetry-preserving constraints, tend to suffer from biased sampling, where structures with P1 symmetry become disproportionately dominant. To overcome this limitation, we propose a method that utilizes a finite set of symmetrically inequivalent Wyckoff encodes to sparsely represent the search space of crystalline molecular compounds, thereby improving the coverage of the search space. This method can be used to predict the molecular crystal structures with varying numbers of molecules in the asymmetric unit, from fractional to multiple integers. The applications of this method to three-dimensional molecular crystals of ammonia dihydrate, the NH3−H2O−CH4 mixture, and two-dimensional layered ices revealed a series of energetically quasidegenerate structures with diverse crystal packing motifs. This method expands the scope and utility of molecular building blocks for designing stable molecular crystals, providing a robust framework for achieving extensive tunability in molecular materials design.
This paper investigates the problem of adaptive detection of distributed targets in power heterogeneous clutter. In the considered scenario, all the data share the identical structure of clutter covariance matrix, but...
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In the complex electromagnetic environment of modern electronic warfare, active deception jamming poses a serious threat to radar. This paper proposes the identification of active deception jamming based on residual n...
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The topological connectivity information derived from the brain functional network can bring new insights for diagnosing and analyzing dementia *** brain functional network is suitable to bridge the correlation betwee...
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The topological connectivity information derived from the brain functional network can bring new insights for diagnosing and analyzing dementia *** brain functional network is suitable to bridge the correlation between abnormal connectivities and dementia ***,it is challenging to access considerable amounts of brain functional network data,which hinders the widespread application of data-driven models in dementia *** this study,a novel distribution-regularized adversarial graph auto-Encoder(DAGAE)with transformer is proposed to generate new fake brain functional networks to augment the brain functional network dataset,improving the dementia diagnosis accuracy of data-driven ***,the label distribution is estimated to regularize the latent space learned by the graph encoder,which canmake the learning process stable and the learned representation ***,the transformer generator is devised to map the node representations into node-to-node connections by exploring the long-term dependence of highly-correlated distant brain *** typical topological properties and discriminative features can be preserved ***,the generated brain functional networks improve the prediction performance using different classifiers,which can be applied to analyze other cognitive *** on the Alzheimer’s Disease Neuroimaging Initiative(ADNI)dataset demonstrate that the proposed model can generate good brain functional *** classification results show adding generated data can achieve the best accuracy value of 85.33%,sensitivity value of 84.00%,specificity value of 86.67%.The proposed model also achieves superior performance compared with other related ***,the proposedmodel effectively improves cognitive disease diagnosis by generating diverse brain functional networks.
In practical applications, the SAR image dataset is always insufficient and lacks sample diversity. Generative adversarial nets(GANs) have been widely used to address the problem of insufficient data. However, owing t...
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Since the discovery of superconductivity, the realization of room-temperature superconductivity has been the long dream of mankind. Recently, it has been reported that nitrogen-doped lutetium hydride has room-temperat...
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Since the discovery of superconductivity, the realization of room-temperature superconductivity has been the long dream of mankind. Recently, it has been reported that nitrogen-doped lutetium hydride has room-temperature superconductivity at near-ambient conditions. However, there is no solid evidence of such tantalizing superconductivity in nitrogen-doped lutetium hydride synthesized by follow-up experiments. The compositions and crystal structures of the nitrogen-doped lutetium hydride are still unclear. Therefore, we here systematically study the structural stability and electronic properties of Fm3¯m LuH3 and N-doped Fm3¯m LuH3, such as 1, 2, 3, 4, and 8% N, by first-principles calculations. Our further electronic properties calculations show that all the simulated Lu-N-H ternary compounds are metallic, except for a semiconducting phase of Lu4H11N (LuH2.75N0.25). Remarkably, the contribution of the H atoms to the electronic density of states at the Fermi level could be tuned by the increasing N concentration. Electron-phonon coupling calculations also show that both Fm3¯m LuH3 and Pm3¯m Lu4H11N are superconducting with critical temperature Tc's of 23 and 32 K at 24 and 60 GPa, respectively. Our current results suggest that LuH3 and N-doped LuH3 could be potential superconductors only at high pressure, while the estimated Tc values are much lower than room temperature.
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