From the past few decades, attention towards the biological evaluation of nanoparticles (NPs) has increased due to the persistent and extensive application of NPs in various fields, including biomedical science, modern industry, magnetic resonance imaging, and the construction of sensors. Therefore, in the current study, magnetic nickel ferrite (NiFe₂O₄) nanoparticles (NFNPs) were synthesized and evaluated for their possible adverse effects in rabbits. The crystallinity of the synthesized NFNPs was confirmed using X‐ray diffraction (XRD) technique. The saturation magnetization (46.7 emug^-1) was measured using vibrating sample magnetometer (VSM) and 0.35‐tesla magnetron by magnetic resonance imaging (MRI). The adverse effects of NFNPs on blood biochemistry and histoarchitecture of the liver, kidneys, spleen, brain, and heart of the rabbits were determined. A total of sixteen adult rabbits, healthy and free from any apparent infection, were blindly placed in two groups. The rabbits in group A served as control, while the rabbits in group B received a single dose (via ear vein) of NFNPs for ten days. The blood and visceral tissues were collected from each rabbit at days 5 and 10 of posttreatment. The results on blood and serum biochemistry profile indicated significant variation in hematological and serum biomarkers in NFNP‐treated rabbits. The results showed an increased quantity of oxidative stress and depletion of antioxidant enzymes in treated rabbits. Various serum biochemical tests exhibited significantly higher concentrations of different liver function tests, kidney function tests, and cardiac biomarkers. Histopathologically, the liver showed congestion, edema, atrophy, and degeneration of hepatocytes. The kidneys exhibited hemorrhages, atrophy of renal tubule, degeneration, and necrosis of renal tubules, whereas coagulative necrosis, neutrophilic infiltration, and severe myocarditis were seen in different sections of the heart. The brain of the treated rabbits revealed necrosis of neurons, neuron atrophy, and microgliosis. In conclusion, the current study results indicated that the highest concentration of NPs induced adverse effects on multiple tissues of the rabbits.

The market for nanoparticles has grown significantly over the past few decades due to a number of unique qualities, including antibacterial capabilities. It is still unclear how nanoparticle toxicity works. In order to ascertain the toxicity of synthetic cobalt iron oxide (CoFe₂O₄) nanoparticles (CIONPs) in rabbits, this study was carried out. Sixteen rabbits in total were purchased from the neighborhood market and divided into two groups (A and B), each of which contained eight rabbits. The CIONPs were synthesized by the co-precipitation method. Crystallinity and phase identification were confirmed by X-ray diffraction (XRD). The average size of the nanoparticles (13.2 nm) was calculated by Scherrer formula (Dhkl = 0.9 λ/β cos θ) and confirmed by TEM images. The saturation magnetization, 50.1 emug⁻¹, was measured by vibrating sample magnetometer (VSM). CIONPs were investigated as contrast agents (CA) for magnetic resonance images (MRI). The relaxivity (r = 1/T) of the MRI was also investigated at a field strength of 0.35 T (Tesla), and the ratio r₂/r₁ for the CIONPs contrast agent was 6.63. The CIONPs were administrated intravenously into the rabbits through the ear vein. Blood was collected at days 5 and 10 post-exposure for hematological and serum biochemistry analyses. The intensities of the signal experienced by CA with CIONPs were 1427 for the liver and 1702 for the spleen. The treated group showed significantly lower hematological parameters, but significantly higher total white blood cell counts and neutrophils. The results of the serum biochemistry analyses showed significantly higher and lower quantities of different serum biochemical parameters in the treated rabbits at day 10 of the trial. At the microscopic level, different histological ailments were observed in the visceral organs of treated rabbits, including the liver, kidneys, spleen, heart, and brain. In conclusion, the results revealed that cobalt iron oxide (CoFe₂O₄) nanoparticles induced toxicity via alterations in multiple tissues of rabbits.

In this research work, polymer coated magnetic nanoparticles were prepared by co-precipitation method. The samples were characterized by XRD, SEM, EDS, VSM and two probe DC conductivity measurements. XRD pattern indicated the existence of a sole cubic phase of Fe₃O₄ with Miller indices (2 2 0), (3 1 1), (5 1 1), (4 4 0). An average size of magnetic nanoparticles was about 22.9 nm and it was reduced to 21.3 nm and 19.4 nm after 1 wt. %. and 2 wt. % coating of PEG-6000, respectively. The morphology and size of the samples were investigated by scanning electron microscope (SEM). EDX spectra confirmed the coating of PEG on magnetic nanoparticles. Magnetic properties were examined by vibrating sample magnetometer (VSM). Saturation magnetization (M _s ) decreased as the concentration of PEG increased in the magnetic material. Electrical properties of uncoated and polymer coated Fe₃O₄ nanoparticles were studied by two-probe conductivity meter. This study concluded that the thermal flow of charge in polymer coated magnetic nanoparticles can be evaluated at micro and nano level.

The lithium-ion battery/supercapacitor hybrid energy storage system has become the most widely used hybrid energy storage system due to its excellent performance, low cost, and strong versatility. Energy management technology is one of the core technologies in hybrid energy storage systems and remains a significant research focus. To systematically review the energy management methods for hybrid energy storage systems, this article first introduces the topology, energy management architecture, and power allocation control of lithium-ion battery/supercapacitor hybrid systems. Next, the article categorizes existing energy management methods for hybrid energy storage systems into five main types: experience-based, optimization-based, operating condition pattern recognition-based, and machine learning-based. A detailed comparative analysis is provided, with a focus on evaluating the effectiveness of various energy management methods under both regular and random operating conditions. The robustness and computational complexity of each method are also discussed. Finally, the article summarizes current energy management methods and outlines future research directions and development trends in this field. The comprehensive analysis suggests that improving the prediction accuracy of future operating conditions for random loads, establishing more precise hybrid energy storage system models, and enhancing the real-time performance of energy management methods through cloud collaboration will be key areas of focus for future research in hybrid energy storage systems.