RESUMO
Rechargeable lithium-ion batteries, as the most advanced energy storage devices currently available, urgently require the development of cathode materials with high capacity, large specific energy, and fast charge/discharge performance to satisfy the continuous technological innovation. Here, a Li-rich Mn-based ternary cathode material Li7/6Nil/6Co1/6Mn1/2O2 is designed, and the geometrical structure, electronic properties, and thermodynamic properties of this material are investigated employing the first-principles method. Six layered structure models are established by adjusting the ratio of Ni, Co, and Mn elements, and the effects of various elements on the material properties are evaluated. Based on the performance of Ni, Co, and Mn in the structure, Li1.2Ni0.15Co0.1Mn0.55O2 features favorable electrical conductivity, thermal conductivity, and excellent stability. This material obtained through co-precipitation using a high temperature solid phase synthesis presents a high actual capacity (245 mA h g-1) and superior cycling performance (the capacity retention rate of the material is 84% after 60 cycles at 0.2C). This effort discusses the Li-rich Mn-based cathode materials in terms of the structural basis, reaction mechanism, and application exploration, which are valuable for guiding their theoretical design, optimization modification, and industrial application.
RESUMO
In this paper, the directional gradient histogram technology is employed to extract the features of concrete image samples captured under different vibration states. The support vector machine (SVM) is utilized to learn the relationship between image features and vibration state. The machine learning results are subsequently used to assess the feasibility of the concrete vibration state. Simultaneously, the influence mechanism of the calculation parameters of the directional gradient histogram on the recognition accuracy is analyzed. The results demonstrate the feasibility of using the directional gradient histogram-SVM technology to identify the vibration state of concrete. The recognition accuracy initially increases and then decreases as the block size of the directional gradient, or the number of statistical intervals increases. The recognition accuracy also decreases linearly with the increase of the binarization threshold. By using sample images with a resolution of 1024 pixels x 1024 pixels and optimizing the feature extraction parameters, a recognition accuracy of 100% can be attained.
Assuntos
Máquina de Vetores de Suporte , Vibração , Aprendizado de Máquina , Reconhecimento Psicológico , TecnologiaRESUMO
Rubber concrete has been applied to a certain extent in fatigue-resistant structures due to its good durability. Based on a cohesive model of rubber composed of a five-phase material containing mortar, aggregate, rubber, aggregate-mortar interfacial transition zone (ITZ), and rubber-mortar ITZ, this paper studies the influence of the cohesive parameters in the rubber-mortar ITZ on the fatigue problem of rubber concrete on the mesoscopic scale. As the weak part of cement-based composite materials, the ITZ has a great influence on the mechanical properties and durability of concrete, but the performance of the ITZ is difficult to test in macro experiments, resulting in difficulties in determining its simulation parameters. Based on the cohesive model with a rubber content of 5%, this study uses Monofactor analysis and the Plackett-Burman test to quickly and effectively determine the primary and secondary influences of the cohesive model parameters in the rubber-mortar ITZ; further, the response surface method is used to optimize the cohesive parameters in the rubber-mortar ITZ, and the numerical simulation results after optimizing the cohesive parameters are compared and analyzed with the simulation results before optimization. The results show that, under the setting of the optimized parameters, the simulation results of each item of the optimal cohesive model parameters in the rubber-mortar ITZ are in line with the reality and closer to the experimental data, and they are also applicable to rubber concrete models with different rubber dosing.