Predicted Optimal Bifunctional Electrocatalysts for the Hydrogen Evolution Reaction and the Oxygen Evolution Reaction Using Chalcogenide Heterostructures Based on Machine Learning Analysis of in Silico Quantum Mechanics Based High Throughput Screening

Two-dimensional van der Waals heterostructure materials, particularly transition metal dichalcogenides (TMDC), have proved to be excellent photoabsorbers for solar radiation, but performance for such electrocatalysis processes as water splitting to form H₂ and O₂ is not adequate. We propose that dramatically improved performance may be achieved by combining two independent TMDC while optimizing such descriptors as rotational angle, bond length, distance between layers, and the ratio of the bandgaps of two component materials. In this paper we apply the least absolute shrinkage and selection operator (LASSO) process of artificial intelligence incorporating these descriptors together with quantum mechanics (density functional theory) to predict novel structures with predicted superior performance. Our predicted best system is MoTe₂/WTe₂ with a rotation of 300°, which is predicted to have an overpotential of 0.03 V for HER and 0.17 V for OER, dramatically improved over current electrocatalysts for water splitting

Design of a Graphene Nitrene Two-Dimensional Catalyst Heterostructure Providing a Well-Defined Site Accommodating 1 to 3 Metals, with Application to CO₂ Reduction Electrocatalysis for the 2 Metal Case

Recently, the reduction of CO₂ to fuels has been the subject of numerous studies, but the selectivity and activity remain inadequate. Progress has been made on single-site two-dimensional catalysts based on graphene coupled to a metal and nitrogen for the CO₂ reduction reaction (CO₂RR); however, the product is usually CO, and the metal–N environment remains ambiguous. We report a novel two-dimensional graphene nitrene heterostructure (grafiN₆) providing well-defined active sites (N₆) that can bind one to three metals for the CO₂RR. We find that homobimetallic FeFe–grafiN₆ could reduce CO₂ to CH₄ at −0.61 V and to CH₃CH₂OH at −0.68 V versus reversible hydrogen electrode, with high product selectivity. Moreover, the heteronuclear FeCu–grafiN₆ system may be significantly less affected by hydrogen evolution reaction, while maintaining a low limiting potential (−0.68 V) for C1 and C2 mechanisms. Binding metals to one N₆ site but not the other could promote efficient electron transport facilitating some reaction steps. This framework for single or multiple metal sites might also provide unique catalytic sites for other catalytic processes

Interface Physical Influence on Mechanic Properties of PP/AS Modified Polymer Caused by Maleic Anhydride

Polypropylene (PP) is a widely used polymer matter, which has many advantages, such as rich sources, simple synthesis process, small density, low cost, easy processing and molding. At present, there has been a lot of progress in the research of polypropylene modified blending, including PP/ABS system, PP/SBS system, etc. However, the research on PP/AS blend is relatively few, and the experimental formula range is large, so it is difficult to determine the optimal ratio. On the basis of previous experiments, this study further narrowed the proportion range. Through AS modifying PP, the interface physical effect of maleic anhydride addition on the mechanical properties of the blend was discussed, and the optimal proportion was found to obtain the modified PP resin with significantly improved mechanical properties.Comment: 8 page

Predicted Optimal Bifunctional Electrocatalysts for the Hydrogen Evolution Reaction and the Oxygen Evolution Reaction Using Chalcogenide Heterostructures Based on Machine Learning Analysis of in Silico Quantum Mechanics Based High Throughput Screening

Two-dimensional van der Waals heterostructure materials, particularly transition metal dichalcogenides (TMDC), have proved to be excellent photoabsorbers for solar radiation, but performance for such electrocatalysis processes as water splitting to form H₂ and O₂ is not adequate. We propose that dramatically improved performance may be achieved by combining two independent TMDC while optimizing such descriptors as rotational angle, bond length, distance between layers, and the ratio of the bandgaps of two component materials. In this paper we apply the least absolute shrinkage and selection operator (LASSO) process of artificial intelligence incorporating these descriptors together with quantum mechanics (density functional theory) to predict novel structures with predicted superior performance. Our predicted best system is MoTe₂/WTe₂ with a rotation of 300°, which is predicted to have an overpotential of 0.03 V for HER and 0.17 V for OER, dramatically improved over current electrocatalysts for water splitting

Crowd3D: Towards Hundreds of People Reconstruction from a Single Image

Image-based multi-person reconstruction in wide-field large scenes is critical for crowd analysis and security alert. However, existing methods cannot deal with large scenes containing hundreds of people, which encounter the challenges of large number of people, large variations in human scale, and complex spatial distribution. In this paper, we propose Crowd3D, the first framework to reconstruct the 3D poses, shapes and locations of hundreds of people with global consistency from a single large-scene image. The core of our approach is to convert the problem of complex crowd localization into pixel localization with the help of our newly defined concept, Human-scene Virtual Interaction Point (HVIP). To reconstruct the crowd with global consistency, we propose a progressive reconstruction network based on HVIP by pre-estimating a scene-level camera and a ground plane. To deal with a large number of persons and various human sizes, we also design an adaptive human-centric cropping scheme. Besides, we contribute a benchmark dataset, LargeCrowd, for crowd reconstruction in a large scene. Experimental results demonstrate the effectiveness of the proposed method. The code and datasets will be made public.Comment: 8 pages (not including reference

InterFormer: Interactive Local and Global Features Fusion for Automatic Speech Recognition

The local and global features are both essential for automatic speech recognition (ASR). Many recent methods have verified that simply combining local and global features can further promote ASR performance. However, these methods pay less attention to the interaction of local and global features, and their series architectures are rigid to reflect local and global relationships. To address these issues, this paper proposes InterFormer for interactive local and global features fusion to learn a better representation for ASR. Specifically, we combine the convolution block with the transformer block in a parallel design. Besides, we propose a bidirectional feature interaction module (BFIM) and a selective fusion module (SFM) to implement the interaction and fusion of local and global features, respectively. Extensive experiments on public ASR datasets demonstrate the effectiveness of our proposed InterFormer and its superior performance over the other Transformer and Conformer models.Comment: Accepted by Interspeech 202

Measuring the Quality of Financial Electronic Payment System: Combined with Fuzzy AHP and Fuzzy TOPSIS

The study aims to apply Fuzzy AHP in TOPSIS to discuss the key factors that foster the success of current third-party online payment platforms. This study organized the quality measurements into four categories and eleven sub-categories. The AHP in TOPSIS is applied to calculate the weighted averages of all categories and sub-categories to measure the quality of third-party online payment platforms. This study finds that “safety quality” is the most emphasized category, “system quality” is the second, “communication quality” is the third, and “service quality” is the least emphasized