Collaborative Distribution Alignment for 2D image-based 3D shape retrieval

Retrieving 3D shapes with 2D images has become a popular research area nowadays, and a great deal of work has been devoted to reducing the discrepancy between 3D shapes and 2D images to improve retrieval performance. However, most approaches ignore the semantic information and decision boundaries of the two domains, and cannot achieve both domain alignment and category alignment in one module. In this paper, a novel Collaborative Distribution Alignment (CDA) model is developed to address the above existing challenges. Specifically, we first adopt a dual-stream CNN, following a similarity guided constraint module, to generate discriminative embeddings for input 2D images and 3D shapes (described as multiple views). Subsequently, we explicitly introduce a joint domain-class alignment module to dynamically learn a class-discriminative and domain-agnostic feature space, which can narrow the distance between 2D image and 3D shape instances of the same underlying category, while pushing apart the instances from different categories. Furthermore, we apply a decision boundary refinement module to avoid generating class-ambiguity embeddings by dynamically adjusting inconsistencies between two discriminators. Extensive experiments and evaluations on two challenging benchmarks, MI3DOR and MI3DOR-2, demonstrate the superiority of the proposed CDA method for 2D image-based 3D shape retrieval task.     

Swapping catalytic active sites from cationic Ni to anionic F in Fe–F–Ni3S2 enables more efficient alkaline oxygen evolution reaction and urea oxidation reaction

Electrolysis of water for producing hydrogen instead of traditional fossil fuels is one of the most promising methods to alleviate environmental pollution and energy crisis. In this work, Fe and F ion co-doped Ni₃S₂ nanoarrays grown on Ni foam substrate were prepared by typical hydrothermal and sulfuration processes for the first time. Density functional theory (DFT) calculation demonstrate that the adsorption energy of the material to water is greatly enhanced due to the doping of F and Fe, which is conducive to the formation of intermediate species and the improvement of electrochemical performance of the electrode. The adsorption energy of anions (F and S) and cations (Fe and Ni) to water in each material was also calculated, and the results showed that F ion showed the most optimal adsorption energy of water, which proved that the doping of F and Fe was beneficial to improve the electrochemical performance of the electrode. It is worth noting that the surface of Fe–F–Ni₃S₂ material will undergo reconstruction during the process of water oxidation reaction and urea oxidation reaction, and amorphous oxides or hydroxides in situ would be formed on the surface of electrode, which are the real active species.     

Generalized differential quadrature analysis of MHD mixed convective motion of Casson fluids past an isothermal irregular slender geometry via Cattaneo–Christov's approach

Classical Fourier's theory is well-known in continuum physics and thermal sciences. However, the primary drawback of this law is that it contradicts the principle of causality. To explore the thermal relaxation time characteristic, Cattaneo–Christov's theory is adopted thermally. In this regard, the features of magnetohydrodynamic (MHD) mixed convective flows of Casson fluids over an impermeable irregular sheet are revealed numerically. In addition, the resulting system of partial differential equations is altered via practical transformations into nonlinear ordinary differential equations. An advanced numerical algorithm is developed in this respect to get higher approximations for temperature and velocity fields, as well as their corresponding wall gradients. For validating our numerical code, the current outcomes are compared with the available literature results. Moreover, it is revealed that the velocity field is more prominent in the suction flow situation as compared with the injection flow case. It is also found that the Casson fluid is hastened in the case of lower yield stress. Larger values of thermal relaxation parameters create a lessening trend in the temperature distribution and its related boundary layer breadth.     

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。     

CNT-interconnected iron-doped NiP2/Ni2P heterostructural nanoflowers as high-efficiency electrocatalyst for oxygen evolution reaction

Oxygen evolution reaction (OER) plays a decisive role in electrolytic water splitting. However, it is still challengeable to develop low-cost and efficient OER electrocatalysts. Herein, we present a combination strategy via heteroatom doping, hetero-interface engineering and introducing conductive skeleton to synthesize a hybrid OER catalyst of CNT-interconnected iron-doped NiP₂/Ni₂P (Fe-(NiP₂/Ni₂P)@CNT) heterostructural nanoflowers by a simple hydrothermal reaction and subsequent phosphorization process. The optimized Fe-(NiP₂/Ni₂P)@CNT catalyst delivers an ultralow Tafel slope of 46.1 mV dec^?1 and overpotential of 254 mV to obtain 10 mA cm^?2, which are even better than those of commercial OER catalyst RuO₂. The excellent OER performance is mainly attributed to its unique nanoarchitecture and the synergistic effects: the nanoflowers constructed by a 2D-like nanosheets guarantee large specific area and abundant active sites; the highly conductive CNT skeleton and the electronic modulation by the heterostructural NiP₂/Ni₂P interface and the hetero-atom doping can improve the catalytic activity; porous nanostructure benefits electrolyte penetration and gas release; most importantly, the rough surface and rich defects caused by phosphorization process can further enhance the OER performance. This work provides a deep insight to boost catalytic performance by heteroatom doping and interface engineering for water splitting.     

Investigation of liquid water heterogeneities in large area proton exchange membrane fuel cells using a Darcy two-phase flow model in a multiphysics code

Proper management of the liquid water and heat produced in proton exchange membrane (PEM) fuel cells remains crucial to increase both its performance and durability. In this study, a two-phase flow and multicomponent model, called two-fluid model, is developed in the commercial COMSOL Multiphysics® software to investigate the liquid water heterogeneities in large area PEM fuel cells, considering the real flow fields in the bipolar plate. A macroscopic pseudo-3D multi-layers approach has been chosen and generalized Darcy's relation is used both in the membrane-electrode assembly (MEA) and in the channel. The model considers two-phase flow and gas convection and diffusion coupled with electrochemistry and water transport through the membrane. The numerical results are compared to one-fluid model results and liquid water measurements obtained by neutron imaging for several operating conditions. Finally, according to the good agreement between the two-fluid and experimentation results, the numerical water distribution is examined in each component of the cell, exhibiting very heterogeneous water thickness over the cell surface.     

Role of functionalized graphene quantum dots in hydrogen evolution reaction: A density functional theory study

Rapid advances in the field of catalysis require a microscopic understanding of the catalytic mechanisms. However, in recent times, experimental insights in this field have fallen short of expectations. Furthermore, experimental searches of novel catalytic materials are expensive and time-consuming, with no guarantees of success. As a result, density functional theory (DFT) can be quite advantageous in advancing this field because of the microscopic insights it provides and thus can guide experimental searches of novel catalysts. Several recent works have demonstrated that low-dimensional materials can be very efficient catalysts. Graphene quantum dots (GQDs) have gained much attention in past years due to their unique properties like low toxicity, chemical inertness, biocompatibility, crystallinity, etc. These properties of GQDs which are due to quantum confinement and edge effects facilitate their applications in various fields like sensing, photoelectronics, catalysis, and many more. Furthermore, the properties of GQDs can be enhanced by doping and functionalization. In order to understand the effects of functionalization by oxygen and boron based groups on the catalytic properties relevant to the hydrogen-evolution reaction (HER), we perform a systematic study of GQDs functionalized with the oxygen (O), borinic acid (BC₂O), and boronic acid (BCO₂). All calculations that included geometry optimization, electronic and adsorption mechanism, were carried out using the Gaussian16 package, employing the hybrid functional B3LYP, and the basis set 6-31G(d,p). With the variation in functionalization groups in GQDs, we observe significant changes in their electronic properties. The adsorption energy E_ads of hydrogen over O-GQD, BC₂O-GQD, and BCO₂-GQD is ?0.059 eV, ?0.031 eV and ?0.032 eV respectively. Accordingly, Gibbs free energy (ΔG) of hydrogen adsorption is extraordinarily near the ideal value (0 eV) for all the three types of functionalized GQDs. Thus, the present work suggests pathways for experimental realization of low-cost and multifunctional GQDs based catalysts for clean and renewable hydrogen energy production.     

Topological homogenization of metamaterial variability

With the proliferation of additive manufacturing and 3D printing technologies, a broader palette of material properties can be elicited from cellular solids, also known as metamaterials, architected foams, programmable materials, or lattice structures. Metamaterials are designed and optimized under the assumption of perfect geometry and a homogeneous underlying base material. Yet in practice real lattices contain thousands or even millions of complex features, each with imperfections in shape and material constituency. While the role of these defects on the mean properties of metamaterials has been well studied, little attention has been paid to the stochastic properties of metamaterials, a crucial next step for high reliability aerospace or biomedical applications. In this work we show that it is precisely the large quantity of features that serves to homogenize the heterogeneities of the individual features, thereby reducing the variability of the collective structure and achieving effective properties that can be even more consistent than the monolithic base material. In this first statistical study of additive lattice variability, a total of 239 strut-based lattices were mechanically tested for two pedagogical lattice topologies (body centered cubic and face centered cubic) at three different relative densities. The variability in yield strength and modulus was observed to exponentially decrease with feature count (to the power −0.5), a scaling trend that we show can be predicted using an analytic model or a finite element beam model. The latter provides an efficient pathway to extend the current concepts to arbitrary/complex geometries and loading scenarios. These results not only illustrate the homogenizing benefit of lattices, but also provide governing design principles that can be used to mitigate manufacturing inconsistencies via topological design.     

Fabrication of alumina ceramics with functional gradient structures by digital light processing 3D printing technology

Alumina ceramics with different unit numbers and gradient modes were prepared by digital light processing (DLP) 3D printing technology. The side length of each functional gradient structure was 10 mm, the porosity ratio was controlled to 70%, and the number of units were (1 × 1 × 1 unit) and (2 × 2 × 2 unit) respectively. The different gradient modes were named FCC, GFCC-1, GFCC-2 and GFCC-3. SEM, XRD, and other characterization methods proved that these gradient structures of alumina ceramics had only α-Al2O3 phase and good surface morphology. The mechanical properties and energy absorption properties of alumina ceramics with different functional gradient structures were studied by compression test. The results show that the gradient structure with 1 × 1 × 1 unit has better mechanical properties and energy absorption properties when the number of units is different. When the number of units is the same, GFCC-2 and GFCC-3 gradient structures have better compressive performance and energy absorption potential than FCC structures. The GFCC-2 gradient structure with 1 × 1 × 1 unit has a maximum compressive strength of 19.62 MPa and a maximum energy absorption value of 2.72 × 105 J/m3. The good performance of such functional gradient structures can provide new ideas for the design of lightweight and compressive energy absorption structures in the future.