With the increase of interest in the application of piezoelectric polyvinylidene fluoride (PVDF) in nanogenerators (NGs), sensors, and microdevices, the most efficient and suitable methods of their synthesis are being pursued. Electrospinning is an effective method to prepare higher content β-phase PVDF nanofiber films without additional high voltage poling or mechanical stretching, and thus, it is considered an economically viable and relatively simple method. This work discusses the parameters affecting the preparation of the desired phase of the PVDF film with a higher electrical output. The design and selection of optimum preparation conditions such as solution concentration, solvents, the molecular weight of PVDF, and others lead to electrical properties and performance enhancement in the NG, sensor, and other applications. Additionally, the effect of the nanoparticle additives that showed efficient improvements in the PVDF films was discussed as well. For instance, additives of BaTiO₃, carbon nanotubes, graphene, nanoclays, and others are summarized to show their contributions to the higher piezo response in the electrospun PVDF. The recently reported applications of electrospun PVDF films are also analyzed in this review paper.

Contemporary lithium-ion batteries (LIBs) are one of the main components of energy storage systems that need effective management to extend service life and increase reliability and safety. Their characteristics depend highly on internal and external conditions (ageing, temperature, and chemistry). Currently, the state of batteries is determined using two parameters: the state of charge (SOC) and the state of health (SOH). Applying these two parameters makes it possible to calculate the expected battery life and a battery’s performance. There are many methods for estimating the SOH of batteries, including experimental, model-based, and machine learning methods. By comparing model-based estimations with experimental techniques, it can be concluded that the use of experimental methods is not applicable for commercial cases. The electrochemical model-based SOH estimation method clearly explains processes in the battery with the help of multidifferential equations. The machine learning method is based on creating a program trained to predict the battery’s state of health with the help of past ageing data. In this review paper, we analyze the research available in the literature in this direction. It is found that all methods used to assess the SOH of an LIB play an essential role, and each method has its pros and cons.

With the expanding requirements of recent energy regulations and economic interest in high-performance batteries, the need to improve battery energy density and safety has gained prominence. High-energy-density lithium batteries, employed in next-generation energy storage devices, rely on nickel-rich cathode materials. Since they have extremely high charge/discharge capacity, high operating voltage, prolonged cycle life, and lower cost, nickel-rich cathode materials such as Ni-rich NCM (LiNi_x > 0.8Co_yMn_zO₂) and Ni-rich NCA (LiNi_x > 0.8Co_yAl_zO₂) are of particular interest to researchers. Several in situ characterization methodologies are currently used to understand lithium-ion battery electrode response and deterioration better. Nevertheless, in many contexts, these measurement methodologies must be combined with specially designed cells and electrode materials with distinct forms, which is sometimes inconvenient. As an alternative, thermo-voltammetric dynamic characterization may be utilized to describe the thermal internal characteristics of various electrode materials, such as the structural changes and electrode reactions that occur during charging and discharging. In this paper, a nondestructive entropy measurement method demonstrates that phase change occurs for NCM (LiNi_0.83Co_0.12Mn_0.05O₂) and NCA (LiNi_0.88Co_0.09Al_0.03O₂) at 40-30% of state of charge (SOC) and 90-80% of SOC, respectively. This is confirmed by ex situ X-ray diffraction (XRD) measurements for these highly popular cathodes.

Carbon-encapsulated cobalt (Co@C) nanoparticles, with a tunable structure, were synthesized by chemical vapor deposition using Co nanoparticles as the catalyst and supported on a water-soluble substrate (sodium chloride), which was easily removed by washing and centrifugation. The influences of growth temperature and time on the structure and magnetic properties of the Co@C nanoparticles were systematically investigated. For different growth temperatures, the magnetic Co nanoparticles were encapsulated by different types of carbon layers, including amorphous carbon layers, graphitic layers, and carbon nanofibers. This inferred a close relationship between the structure of the carbon-encapsulated metal nanoparticles and the growth temperature. At a fixed growth temperature of 400 °C, prolonged growth time caused an increase in thickness of the carbon layers. The magnetic characterization indicated that the magnetic properties of the obtained Co@C nanoparticles depend not only on the graphitization but also on the thickness of the encapsulated carbon layer, which were easily controlled by the growth temperatures and times. Optimization of the synthesis process allowed achieving relatively high coercivity of the synthesized Co@C nanoparticles and enhancement of its ferromagnetic properties, which make this system promising as a magnetic material, particularly for high-density magnetic recording applications.

The governing equations for a pre-twisted rotating cantilever beam are derived and used for free vibration analysis of a pre-twisted rotating beam whose flexural displacements are coupled in two planes. First differential equations of motion of a rotating twisted beam, including terms due to centrifugal stiffening, are derived for an Euler–Bernoulli beam undergoing free natural vibrations. The general solutions of these equations are obtained on applying the Adomian modified decomposition method (AMDM). The AMDM allows the governing differential equations to become recursive algebraic equations and the boundary conditions to become simple algebraic frequency equations suitable for symbolic computation. With additional simple mathematical operations on the model, the natural frequencies and corresponding closed-form series solution of the mode shape can be obtained simultaneously. Two main advantages of the application of the AMDM are, for the cases considered here, its fast convergence rate to the solution with the high degree of accuracy. As the AMDM technique is systematic, it is found straight-forward to modify boundary conditions from one case to the next. Comparison of results with published data showed the present calculations to be in reasonable agreement.