Nanostructured Fe 2

In the present work, a method combining arc plasma evaporation of a metal followed by oxidation in air was developed to produce nanosized metal oxide based composites in large scale. As an example, Fe2O3 based nanocomposites were prepared through such a method. With increasing the oxidation temperature, α-Fe2O3 content in the composites increases, while γ-Fe2O3 and residual α-Fe contents decrease. As anode materials for lithium batteries, the electrochemical properties of nanosized Fe2O3 composites were tested. It was found that the anode materials changed to tiny crystallites and then followed by grain growth during the galvanostatic charge/discharge cycles. A capacity rising was observed for the composites obtained at 400°C and 450°C, which was more prominent with increasing the oxidation temperature. Among these composites, the one obtained at 450°C showed the best performance: a specific capacity of 507.6 mAh/g remained after 150 cycles at a current density of 200 mA/g, much higher than that of the commercial nano-Fe2O3 powder (~180 mAh/g after 30 cycles)

Topological fractal networks introduced by mixed degree distribution

Several fundamental properties of real complex networks, such as the small-world effect, the scale-free degree distribution, and recently discovered topological fractal structure, have presented the possibility of a unique growth mechanism and allow for uncovering universal origins of collective behaviors. However, highly clustered scale-free network, with power-law degree distribution, or small-world network models, with exponential degree distribution, are not self-similarity. We investigate networks growth mechanism of the branching-deactivated geographical attachment preference that learned from certain empirical evidence of social behaviors. It yields high clustering and spectrums of degree distribution ranging from algebraic to exponential, average shortest path length ranging from linear to logarithmic. We observe that the present networks fit well with small-world graphs and scale-free networks in both limit cases (exponential and algebraic degree distribution respectively), obviously lacking self-similar property under a length-scale transformation. Interestingly, we find perfect topological fractal structure emerges by a mixture of both algebraic and exponential degree distributions in a wide range of parameter values. The results present a reliable connection among small-world graphs, scale-free networks and topological fractal networks, and promise a natural way to investigate universal origins of collective behaviors.Comment: 14 pages, 6 figure

Surface Characteristics and High Cycle Fatigue Performance of Shot Peened Magnesium Alloy ZK60

The current work investigated the effect of shot peening (SP) on high cycle fatigue (HCF) behavior of the hot-extruded ZK60 magnesium alloy. SP can significantly improve the fatigue life of the ZK60 alloy. After SP at the optimum Almen intensities, the fatigue strength at 107 cycles in the as-extruded (referred to as ZK60) and the T5 aging-treated (referred to as ZK60-T5) alloys increased from 140 and 150 MPa to 180 and 195 MPa, respectively. SP led to a subsurface fatigue crack nucleation in both ZK60 and ZK60-T5 alloys. The mechanism by which the compressive residual stress induced by shot peening results in the improvement of fatigue performance for ZK60 and ZK60-T5 alloys was discussed

Forecasting of Short-Term Load Using the MFF-SAM-GCN Model

Short-term load forecasting plays a significant role in the operation of power systems. Recently, deep learning has been generally employed in short-term load forecasting, primarily in the extraction of the characteristics of digital information in a single dimension without taking into account of the impact of external variables, particularly non-digital elements on load characteristics. In this paper, we propose a joint MFF-SAM-GCN to realize short-term load forecasting. First, we utilize a Bi-directional Long Short-Term Memory (Bi-LSTM) network and One-Dimensional Convolutional Neural Network (1D-CNN) in parallel connection to form a multi-feature fusion (MFF) framework, which can extract spatiotemporal correlation features of the load data. In addition, we introduce a Self-Attention Mechanism (SAM) to further enhance the feature extraction capability of the 1D-CNN network. Then with the deployment of a Graph Convolutional Network (GCN), the external non-digital features such as weather, strength, and direction of wind, etc., are extracted. Moreover, the generated weight matrices are incorporated into the load features to enhance feature recognition ability. Finally, we exploit Bayesian Optimization (BO) to find the optimal hyperparameters of the model to further improve the prediction accuracy. The simulation is taken from our proposed model and six benchmark schemes by using the bus load dataset of the Shandong Open Data Network, China. The results show that the RMSE of our proposed MFF-SAM-GCN model is 0.0284, while the SMAPE is 9.453%,the MBE is 0.025, and R-squared is 0.989, which is better than the selected three traditional machine learning methods and the three deep learning models

Forecasting of Short-Term Load Using the MFF-SAM-GCN Model

Short-term load forecasting plays a significant role in the operation of power systems. Recently, deep learning has been generally employed in short-term load forecasting, primarily in the extraction of the characteristics of digital information in a single dimension without taking into account of the impact of external variables, particularly non-digital elements on load characteristics. In this paper, we propose a joint MFF-SAM-GCN to realize short-term load forecasting. First, we utilize a Bi-directional Long Short-Term Memory (Bi-LSTM) network and One-Dimensional Convolutional Neural Network (1D-CNN) in parallel connection to form a multi-feature fusion (MFF) framework, which can extract spatiotemporal correlation features of the load data. In addition, we introduce a Self-Attention Mechanism (SAM) to further enhance the feature extraction capability of the 1D-CNN network. Then with the deployment of a Graph Convolutional Network (GCN), the external non-digital features such as weather, strength, and direction of wind, etc., are extracted. Moreover, the generated weight matrices are incorporated into the load features to enhance feature recognition ability. Finally, we exploit Bayesian Optimization (BO) to find the optimal hyperparameters of the model to further improve the prediction accuracy. The simulation is taken from our proposed model and six benchmark schemes by using the bus load dataset of the Shandong Open Data Network, China. The results show that the RMSE of our proposed MFF-SAM-GCN model is 0.0284, while the SMAPE is 9.453%,the MBE is 0.025, and R-squared is 0.989, which is better than the selected three traditional machine learning methods and the three deep learning models

Preparation and Hydrogen Storage Properties of Mg-Rich Mg-Ni Ultrafine Particles

In the present work, Mg-rich Mg-Ni ultrafine powders were prepared through an arc plasma method. The phase components, microstructure, and hydrogen storage properties of the powders were carefully investigated. It is found that Mg2Ni and MgNi2 could be obtained directly from the vapor state reactions between Mg and Ni, depending on the local vapor content in the reaction chamber. A nanostructured MgH2 + Mg2NiH4 hydrogen storage composite could be generated after hydrogenation of the Mg-Ni ultrafine powders. After dehydrogenation, MgH2 and Mg2NiH4 decomposed into nanograined Mg and Mg2Ni, respectively. Thermogravimetry/differential scanning calorimetry (TG/DSC) analyses showed that Mg2NiH4 phase may play a catalytic role in the dehydriding process of the hydrogenated Mg ultrafine particles

Turning Trash into Treasure: MXene with Intrinsic LiF Solid Electrolyte Interfaces Performs Better and Better during Battery Cycling

Commercialization of lithium ion batteries has accelerated dramatically over the last few decades. Single‐layered Ti3C2 (s‐Ti3C2) is effectively prepared by etching Ti3AlC2 via simple treatment with HCl and LiF, producing inevitably sediments always discarded after etching. This study explores the effect of LiF doping of multilayered Ti3C2 to form m‐Ti3C2/LiF consisting essentially of the sediments. Simple half‐cells assembled with m‐Ti3C2/LiF sediments suggest that LiF suppresses electrode volume expansion and surface cracking during cycling promoting Li+ intercalation/deintercalation. The data also suggest that LiF promotes formation of stable artificial solid electrolyte interfaces to prevent electrolyte and electrode degradation. The capacity of m‐Ti3C2/LiF sediments derived cells maintains 136 mAh g−1 after 1500 cycles at 300 mA g−1 while s‐Ti3C2 from supernatants physically mixed with 20 wt% LiF shows a capacity of 335 mAh g−1 (100th cycle) at 100 mA g−1 with an initial coulombic efficiency of 83%. Half‐cell anodes made of Ti3C2 etched by HF, commercial TiO2, and Sn powder mixed physically with 20 wt% LiF exhibit improved performance with cycling. These results indicate that the always discarded sediments can be directly used in LIBs and simple doping with LiF obviously improves the electrochemical performance of materials.Etching Ti3AlC2 using HCl and LiF results in the supernatants containing single‐layered Ti3C2 and multilayered Ti3C2/LiF sediments [m‐Ti3C2/LiF(S1)] always discarded as “debris”, further to be investigated in lithium ion batteries. The m‐Ti3C2/LiF(S1) shows negative capacity fading with capacity increasing to 198 mAh g−1 after 600 cycles at 30 mA g−1. LiF takes a significant role in electrochemical performance enhancing.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/167076/1/admt202000882_am.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/167076/2/admt202000882-sup-0001-SuppMat.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/167076/3/admt202000882.pd

Effect of FLiBe thermal neutron scattering on reactivity of molten salt reactor

Thermal neutron scattering data has an important influence on the calculation and design of reactor with a thermal spectrum. However, as the only liquid fuel in the Gen-IV reactor candidates, the research on the thermal neutron scattering effect of coolant and somewhat moderator FLiBe has not been carried out sufficiently either experimentally or theoretically. The effect of FLiBe thermal neutron scattering on reactivity of TMSR-LF (thorium molten salt reactor - liquid fuel), TMSR-SF (thorium molten salt reactor - solid fuel) and MSRE (molten salt reactor experiment) were investigated and compared. Results show that the effect of FLiBe thermal neutron scattering on reactivity depends to some extent on the fuel-graphite volume ratio of core. Calculations indicate that FLiBe thermal neutron scattering of MSRE (with the hardest spectrum) has the minimum effect of 41 pcm on reactivity, and FLiBe thermal neutron scattering of TMSR-SF (with the softest spectrum) has the maximum effect of -94 pcm on reactivity, and FLiBe thermal neutron scattering of TMSR-LF has an effect of -61 pcm on reactivity at 900 K