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.     

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.     

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.     

Manufacturing catalyst-coated membranes by ultrasonic spray deposition for PEMFC: Identification of key parameters and their impact on PEMFC performance

This study deals with the manufacturing of catalyst-coated membranes (CCMs) for newcomers in the field of coating. Although there are many studies on electrode ink composition for improving the performance of proton-exchange membrane fuel cells (PEMFCs), there are few papers dealing with electrode coating itself. Usually, it is a know-how that often remains secret and constitutes the added value of scientific teams or the business of industrialists. In this paper, we identify and clarify the role of key parameters to improve coating quality and also to correlate coating quality with fuel cell performance via polarization curves and electrochemical active surface area measurements. We found that the coating configurations can affect the performance of lab-made CCMs in PEMFCs. After the repeatability of the performance obtained by our coating method has been proved, we show that: (i) edge effects, due to mask shadowing - cannot be neglected when the active surface area is low, (ii) a heterogeneous thickness electrode produces performance lower than a homogeneous thickness electrode, and (iii) the origin and storage of platinum on carbon powders are a very important source of variability in the obtained results.     

Electrochemical performance of spherical Li-rich LMNCO cathode materials prepared using a two-step spray-drying method

In this study we synthesized Li-rich Li_1.2Ni_0.13Mn_0.54Co_0.13O₂ (LMNCO) as a composite cathode material through a two-step spray-drying method, using transition metal (TM) acetates and citric acid (CA, as a chelating agent) at various molar ratios and then calcining at various temperatures for various periods of time. This two-step spray-drying method created hierarchical nano/micro-sphere structures of the LMNCO cathode material. The LMNCO cathode exhibited the best electrochemical performance when synthesized with a TM:CA ratio of 3:2, a calcination temperature of 900 °C, and a calcination time of 5 h. This as-prepared LMNCO composite was then modified with polyimide (PI) at various weight ratios (PI/LMNCO = 0.5, 1.0, and 1.5 wt%) to improve its electrochemical properties. Among the various structures, the LMNCO cathode material coated with 1.0 wt% of PI at a layer thickness of approximately 1.88 nm achieved the best initial discharge capacities. This modified electrode also displayed enhanced cycle stability, with over 93.3 and 87.9% of the capacity retained after 30 cycles at 0.1C and 100 cycles at 1C, respectively. In comparison, the capacity retention of the unmodified LMNCO electrode measured under the same conditions was no more than 91.3% at 0.1C and 70.1% at 1C. Thus, surface modification with PI was an effective method for improving the coulombic efficiency, discharge capacity, and long-term cycling performance of the LMNCO cathode. Such PI-coated LMNCO composite cathode materials appear to be potential candidates for use in next-generation high-performance lithium-ion batteries.     

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.     

Application of artificial neural network method to predict the breakage properties of PGE bearing chromite ore

In the present investigation, systematic grinding experiments were conducted in a laboratory ball mill to determine the breakage properties of low-grade PGE bearing chromite ore. The population balance modeling technique was used to study the breakage parameters such as primary breakage distribution (B_{i, j}) and the specific rates of breakage (S_i). The breakage and selection function values were determined for six feed sizes. The results stated that the breakage follows the first-order grinding kinetics for all the feed sizes. It was observed that the coarser feed sizes exhibit higher selection function values than the finer feed size. Further, an artificial neural network was used to predict breakage characteristics of low-grade PGE bearing chromite ore. The predicted results obtained from the neural network modeling were close to the experimental results with a correlation of determination R² = 0.99 for both product size and selection function.     

A Ti3C2Tx-carbon black-acrylic epoxy coating for 304SS bipolar plates with enhanced corrosion resistant and conductivity

A conducting and anticorrosive coating is crucial for the application of metal bipolar plates (BP) in proton exchange membrane fuel cell (PEMFC). In this work, a Ti₃C₂T_x (T)-carbon black (C)-acrylic epoxy (AE) coating is prepared on 304 stainless steel (SS) with enhanced corrosion resistance and conductivity. The corrosion resistance of the T-C-AE coating is investigated in a 0.5 M H₂SO₄ solution as compared to the AE, T, and T-AE coatings. The T-C-AE coated 304SS exhibits the strongest corrosion resistance with the most positive corrosion potential and the lowest corrosion current density of 0.00673 μA cm^?2 in all the samples, while retaining intact and compact surface morphology with the lowest metal ion dissolution even after immersed for 720 h. The addition of Ti₃C₂T_x and carbon black into the AE matrix greatly decreases interfacial contact resistance (ICR), and the T-C-AE coating achieves a low ICR of 15.5 mΩ cm^?2 under 140 N cm^?2 compaction force. The excellent anticorrosion performance is mainly attributed to the physical barrier and the cathodic protection provided by the stacked Ti₃C₂T_x (MXene) nanosheets in the T-C-AE coating. This eco-friendly, conducting, and anticorrosive T-C-AE coating has a good application prospect on SS BP of PEMFC.