Fe(III) grafted MoO3 nanorods for effective electrocatalytic fixation of atmospheric N2 to NH3

Electrocatalytic nitrogen reduction reaction (ENRR) offers a carbon-neutral process to fix nitrogen into ammonia, but its feasibility depends on the development of highly efficient electrocatalysts. Herein, we report that Fe ion grafted on MoO₃ nanorods synthesized by an impregnation technique can efficiently enhance the electron harvesting ability and the selectivity of H⁺ during the NRR process in neutral electrolyte. In 0.1 M Na₂SO₄ solution, the electrocatalyst exhibited a remarkable NRR activity with an NH₃ yield of 9.66 μg h^?1 mg^?1_cat and a Faradaic efficiency (FE) of 13.1%, far outperforming the ungrafted MnO₃. Density functional theory calculations revealed that the Fe sites are major activation centers along the alternating pathway.     

Density functional theory study of the hydrogen evolution reaction in haeckelite boron nitride quantum dots

To satisfy arising energy needs and to handle the forthcoming worldwide climate transformation, the major research attention has been drawn to environmentally friendly, renewable and abundant energy resources. Hydrogen plays an ideal and significant role is such resources, due to its non-carbon based energy and production through clean energy. In this work, we have explored catalytic activity of a newly predicted haeckelite boron nitride quantum dot (haeck-BNQD), constructed from the infinite BN sheet, for its utilization in hydrogen production. Density functional theory calculations are employed to investigate geometry optimization, electronic and adsorption mechanism of haeck-BNQD using Gaussian16 package, employing the hybrid B3LYP and wB97XD functionals, along with 6–31G(d,p) basis set. A number of physical quantities such as HOMO/LUMO energies, density of states, hydrogen atom adsorption energies, Mulliken populations, Gibbs free energy, work functions, overpotentials, etc., have been computed and analysed in the context of the catalytic performance of haeck-BNQD for the hydrogen-evolution reaction (HER). Based on our calculations, we predict that the best catalytic performance will be obtained for H adsorption on top of the squares or the octagons of haeck-BNQD. We hope that our prediction of most active catalytic sites on haeck-BNQD for HER will be put to test in future experiments.     

疫情下电磁场理论课程的网络教学

本文主要总结了新冠疫情期间作者的电磁场理论课程在线教学经验。对比分析了录播和直播的优缺点后,选择录播教学方式。基于超星网络教学平台,展示了录播网络教学的具体措施,包括网上答疑和学习效果检查以及在线批改作业等。给出了网络教学可以为线下教学继续使用的方法和手段,为疫情结束后的正常教学提供了新的网络教学补充措施。     

Engineering the hydrogen storage properties of the perovskite hydride ZrNiH3 by uniaxial/biaxial strain

In the present work, the bonding length, electronic structure, stability, and dehydrogenation properties of the Perovskite-type ZrNiH₃ hydride, under different uniaxial/biaxial strains are investigated through ab-initio calculations based on the plane-wave pseudo-potential (PW-PP) approach. The findings reveal that the uniaxial/biaxial compressive and tensile strains are responsible for the structural deformation of the ZrNiH₃ crystal structure, and its lattice deformation becomes more significant with decreasing or increasing the strain magnitude. Due to the strain energy contribution, the uniaxial/biaxial strain not only lowers the stability of ZrNiH₃ but also decreases considerably the dehydrogenation enthalpy and decomposition temperature. Precisely, the formation enthalpy and decomposition temperature are reduced from ?67.73 kJ/mol.H₂ and 521 K for non-strained ZrNiH₃ up to ?33.73 kJ/mol.H₂ and 259.5 K under maximal biaxial compression strain of ε = ?6%, and to ?50.99 kJ/mol.H₂ and 392.23 K for the maximal biaxial tensile strain of ε = +6%. The same phenomenon has been also observed for the uniaxial strain, where the formation enthalpy and decomposition temperature are both decreased to ?39.36 kJ/mol.H₂ and 302.78 K for a maximal uniaxial compressive strain of ε = - 12%, and to ?51.86 kJ/mol.H₂ and 399 K under the maximal uniaxial tensile strain of ε = +12%. Moreover, the densities of states analysis suggests that the strain-induced variation in the dehydrogenation and structural properties of ZrNiH₃ are strongly related to the Fermi level value of total densities of states. These ab-initio calculations demonstrate insightful novel approach into the development of Zr-based intermetallic hydrides for hydrogen storage practical applications.     

Learn decision trees with deep visual primitives

In this paper, we strive to propose a self-interpretable framework, termed PrimitiveTree, that incorporates deep visual primitives condensed from deep features with a conventional decision tree, bridging the gap between deep features extracted from deep neural networks (DNNs) and trees’ transparent decision-making processes. Specifically, we utilize a codebook, which embeds the continuous deep features into a finite discrete space (deep visual primitives) to distill the most common semantic information. The decision tree adopts the spatial location information and the mapped primitives to present the decision-making process of the deep features in a tree hierarchy. Moreover, the trained interpretable PrimitiveTree can inversely explain the constituents of the deep features, highlighting the most critical and semantic-rich image patches attributing to the final predictions of the given DNN. Extensive experiments and visualization results validate the effectiveness and interpretability of our method.     

Carbon corrosion mechanism on nitrogen-doped carbon support — A density functional theory study

In this work, density functional theory (DFT) calculations were used to investigate the mechanism of carbon corrosion on nitrogen-doped carbon support. Free energy diagrams were generated based on three proposed reaction pathways to evaluate corrosion mechanisms. The most energetically preferred mechanism on nitrogen-doped carbon was determined. The results show that the step of water dissociation to form ^#OH was the rate-determining step for gra-G-1N (graphene doped with graphitic N) and pyrr-G-1N (graphene doped with pyrrolic N). As for graphene doped with pyridinic N, the step of C^#OC^#O formation was critical. It was found that the control of nitrogen concentration was necessary for precisely designing optimized carbon materials. Abundance of nitrogen moieties aggravated the carbon corrosion. When the high potential was applied, specific types of graphitic N and pyridinic N were found to be favorable carbon modifications to improve carbon corrosion resistance. Moreover, the solvent effect was also investigated. The results provide theoretical insights and design guidelines to improve corrosion resistance in carbon support through material modification by inhibiting the adsorption of surface oxides (OH, O, and OOH).     

Lithiation MXene Derivative Skeletons for Wide-Temperature Lithium Metal Anodes

Lithium (Li) metal, as an appealing candidate for the next-generation of high-energy-density batteries, is plagued by its safety issue mainly caused by uncontrolled dendrite growth and infinite volume expansion. Developing new materials that can improve the performance of Li-metal anode is one of the urgent tasks. Herein, a new MXene derivative containing pure rutile TiO₂ and N-doped carbon prepared by heat-treating MXene under a mixing gas, exhibiting high chemical activity in molten Li, is reported. The lithiation MXene derivative with a hybrid of LiTiO₂-Li₃N-C and Li offers outstanding electrochemical properties. The symmetrical cell assembling lithiation MXene derivative hybrid anode exhibits an ultra-long cycle lifespan of 2000 h with an overpotential of ≈30 mV at 1 mA cm⁻², which overwhelms Li-based anodes reported so far. Additionally, long-term operations of 34, 350, and 500 h at 10 mA cm⁻² can be achieved in symmetrical cells at temperatures of −10, 25, and 50 °C, respectively. Both experimental tests and density functional theory calculations confirm that the LiTiO₂-Li₃N-C skeleton serves as a promising host for Li infusion by alleviating volume variation. Simultaneously, the superlithiophilic interphase of Li₃N guides Li deposition along the LiTiO₂-Li₃N-C skeleton to avoid dendrite growth.     

Thermal desorption and pyrolysis direct analysis in real time mass spectrometry of Nafion membrane

Perfluorosulfonic acid ionomer membranes have been widely used as proton conducting membranes in various electrochemical processes such as polymer electrolyte fuel cells and water electrolysis. While their thermal stability has been studied by thermogravimetry and analysis of low molecular weight products, their decomposition mechanism is little understood. In this study a newly developed methodology of thermal desorption and pyrolysis in combination with direct analysis in real time mass spectrometry is applied for Nafion membrane. An ambient ionization source and a high-resolution time-of-flight mass spectrometer enabled unambiguous assignment of gaseous products. Thermal decomposition is initiated by side chain detachment above 350°C, which leaves carbonyls on the main chain at the locations of the side chains. Perfluoroalkanes are released above 400°C by main chain scission and their further decomposition products dominate above 500 °C. DFT calculation of reaction energies and barrier heights of model compounds support proposed decomposition reactions.     

Off‐grid DoA estimation method of wideband signals

In this paper, an off‐grid direction of arrival (DoA) estimation method is proposed for wideband signals. This method is based on the sparse representation (SR) of the array covariance matrix. Similar to the time domain DoA estimation methods, the correlation function of the sources was assumed to be the same and known. A new measurement vector is obtained using the lower‐left triangular elements of the covariance matrix. The DoAs are estimated by quantizing the entire range of continuous angle space into discrete grid points. However, the exact DoAs may be located between two grid points; therefore, this estimation has errors. The accuracy of DoA estimation is improved by the minimization of the difference between the new measurement vector and its estimated values. Simulation results revealed that the proposed method can enhance the DoA estimation accuracy of wideband signals.     

Knowledge-Based Virtual Modeling and Simulation of Manufacturing Processes for Industry 4.0

Abstract

Industry 4.0 aims at providing a digital representation of a production landscape, but the challenges in building, maintaining, optimizing, and evolving digital models in inter-organizational production chains have not been identified yet in a systematic manner. In this paper, various Industry 4.0 research and technical challenges are addressed, and their present scenario is discussed. Moreover, in this article, the novel concept of developing experience-based virtual models of engineering entities, process, and the factory is presented. These models of production units, processes, and procedures are accomplished by virtual engineering object (VEO), virtual engineering process (VEP), and virtual engineering factory (VEF), using the knowledge representation technique of Decisional DNA. This blend of the virtual and physical domains permits monitoring of systems and analysis of data to foresee problems before they occur, develop new opportunities, prevent downtime, and even plan for the future by using simulations. Furthermore, the proposed virtual model concept not only has the capability of Query Processing and Data Integration for Industrial Data but also real-time visualization of data stream processing.