MnO2 has advantages such as the simple and diverse preparation methods, low cost and high theoretical capacity, but its industrial application is affected by its poor conductivity and fast attenuation of cycle performance. In order to improve its conductivity, battery capacity and performance, MnO2/carbon nanofibers (MnO2/CNFs) are obtained by using electrospinning technology, and the electrochemical performance was confirmed by XRD, SEM, TEM. Confirmed by comparison, the 20% MnO2/CNFs exhibit superior and excellent long cycling performance with a reversible capacity of 835 mA h g−1 at 0.1 A g−1 after the 133th cycle and a high initial specific capacity of 1094 mA h g−1 at a current density of 0.1 A g−1. The MnO2/CNFs have notable specific capacities with a coulombic efficiency of 99.5%, which greatly improve the reaction rate. This can also be used as a flexible electrode material because of its good flexibility. Due to the fact that carbon has better electron/ion conductivity, it shows better kinetics.

High-performance anodes are contributed to nanostructure of transition metal oxides for rechargeable Li-ion batteries (LIBs). In this work, we report the fabrication of high-performance anode materials for lithium-ion batteries, MnO2 nanotube directly grown into fabrics of carbon nanofibers, the MnO2/Carbon nanofibers (CNFs) were investigated by X-ray diffraction, scanning and transmission electron microscopies. When tested as the anode material in LIBs, the MnO2/CNFs exhibit superior performance and excellent long cycling performance with a reversible capacity of 835 mA h g-1 at 0.1 A g-1 after the 133 th cycle, a high initial specific capacity of 1094 mA h g-1 at a current density of 0.1 A g-1. The MnO2/CNFs demonstrates notable specific capacities, specifically, with a coulombic efficiency of 99.5 %, both stability and capacity are conspicuously above literature data. These impressive results indicate that MnO2/CNFs has great potential for high-energy and high-power energy storage applications.

To design efficient photocatalytic systems, it is necessary to inhibit the compounding of electron-hole pairs and promote light absorption in photocatalysts. In this paper, semiconductor heterojunction systems of C-modified Zn-doped TiO2 composite nanomaterials with nanofiber structures were synthesized by electrospinning and hydrothermal methods. The composite nanofiber film was thoroughly characterized and the morphology, structure, chemical phases and optical properties were determined. Scanning electron microscopy confirmed that the nanofiber diameter was 150–200 nm and the C particles were uniformly modified on the smooth nanofiber surfaces. X–ray diffraction patterns and Raman show TiO2 as a typical anatase, modified C as graphite and Zn as ZnOcrystals. Moreover, the entry of Zn and C into the TiO2 lattice increases the crystal defects. Meanwhile, TiO2, ZnO and graphite form multiple heterojunctions, providing pathways for photogenerated carrier transfer. These synergistic effects inhibit the recombination of electron-hole pairs and provide more reaction sites, thus improving the photocatalytic efficiency. UV-Vis diffuse reflectance spectroscopy and fluorescence spectroscopyimply that these synergistic effects lead to improved optical properties of the composite. Using organic dyes (methylene blue, methyl orange, rhodamine Bandmalachite green) as simulated pollutants, the composite nanofiber film exhibited good photocatalytic activity for all dyes due to the significantly large specific surface area, small size effect and synergistic effects of multiple heterojunctions and dopant atom. In addition, the nanofiber film has good reusability and stability for the photodegradation of organic dyes, so it has potential for industrial applications.

Lithium-ion batteries are an important part of smartphones, and their performance has a great impact on the life of the phone. The longevity of lithium-ion batteries is key to ensuring their reliability and extending their useful life. This paper built a lithium battery life prediction model and grey model MDGM(1,1) based on data mining. Then, experimental data were selected for testing, and the prediction error reached 10.5% at the minimum. It showed that the prediction model had higher precision and could provide help for the prediction and development of mobile phone battery life.

Lithium-ion batteries, the core components of electric vehicles, have received unprecedented attention and undergone development in the era of huge energy demand. The traditional clustering algorithm cannot meet the requirement of the consistency of lithium battery distribution. In this study, we provide an improved K-means algorithm to meet the battery distribution needs of enterprises and combine it with reality. This model includes an early data processing model and a battery comparison method based on the new K-means algorithm. In the battery data processing model, the preprocessing process approach and actual production standards preclude problematic batteries. In the battery comparison algorithm, the number of batteries in each cluster becomes equal after the battery comparison. The algorithm can ensure the internal characteristics of lithium-ion power batteries, and, at the same time, after the matching is completed, the number of lithium batteries in each cluster is equal.