Nanoparticles (NPs), which possess unique physicochemical qualities such as large surface area and reactivity, have brought about a revolution in a variety of sectors, including medicine and electronics. The growing ubiquity of these substances, on the other hand, has given rise to worries over the toxicological effects they have on human health and ecosystems. The condition known as oxidative stress, which is caused by an imbalance between the formation of reactive oxygen species (ROS) and antioxidant defenses, is one of the key processes that contribute to the toxicity of NPs. An excessive amount of ROS may cause damage to cellular components such as lipids, proteins, and DNA, which can result in detrimental consequences such as inflammation, apoptosis, and the development of cancer. NP-induced oxidative stress is investigated in this work, which focuses on the molecular mechanisms that are responsible for it. These processes include mitochondrial dysfunction, catalytic redox cycling, and the release of metal ions from particle disintegration. On top of that, we investigate how the features of NPs, such as their size, shape, surface charge, and composition, affect their capacity to produce ROS. Additionally, the consequences of oxidative stress for both acute and chronic health effects are examined, in addition to the function that it plays in the toxicity of the environment. The use of antioxidants and alterations to the surface of NPs are two examples of mitigation measures that are discussed in this article. The findings of this study highlight the significance of gaining knowledge of the processes behind oxidative stress to ensure the safe design and deployment of NPs.

The widespread application of nanoparticles (NPs) in fields ranging from consumer products to industrial processes has led to increased concerns about their potential toxic effects on human health and the environment. While traditional toxicological studies often evaluate the effects of individual NPs, real-world exposure scenarios typically involve mixtures of NPs, where interactions between particles can significantly alter their toxicological profiles. This study provides an overview of overcoming this gap by possible utilization of high-throughput screening (HTS) for evaluation of the combined effects of NP mixtures under various exposure conditions. This review discusses HTS of metal oxide NPs, which have cytotoxic, genotoxic, and oxidative stress-inducing effects. Using HTS, this review describes multiple studies with multiple mixture ratios and exposure durations using human lung epithelial cells and zebrafish embryo systems. The review also describes a range of interactions, from synergistic effects, where the combined toxicity might be the sum of individual toxicities. Oxidative stress and metal ion release were key drivers of toxicity, particularly in metal oxide-dominant NP mixtures. This theoretical study highlights the importance of integrating HTS into nanotoxicology research to provide a more comprehensive understanding of the toxic behavior of NPs.

The shift from fossil fuels to renewable energy is a key component in achieving global sustainability and dealing with climate change. Natural resources, such as sunlight, air, water, and biomass, have tremendous potential to create clean energy; however, exploiting these resources in an efficient, stable, and large-scale integration manner is difficult due to their variable and distributed nature. Artificial intelligence (AI) approaches that mimic human learning and decision-making have recently become viable approaches to solving renewable energy problems. This study mainly examines the current landscape of AI applications across solar, wind, hydro, geothermal, ocean, hydrogen, bioenergy, and hybrid energy systems. AI enhances renewable energy forecasting, improves power system frequency analysis and stability assessments, and optimizes dispatch and distribution networks. Beyond technical optimization, AI also contributes to broader sustainability goals, including energy efficiency improvements, intelligent smart grid management, and enabling mechanisms such as carbon trading and circular economy practices to reduce exposure to climate extremes. Drawing on evidence from a range of renewable energy areas, this review highlights the importance of AI in bridging the link between technological innovation and sustainable energy management. This paper discusses potential future research avenues, such as building sophisticated AI designs, energy-efficient chips, and data communication networks. Ultimately, the synergy between AI and renewable energy systems represents not only a technological advancement but also a transformative pathway toward a resilient, low-carbon future.