Nanoscale Reports

Environmental Applications of Nanoparticles: Water Purification and Pollution Control

Atique Anwar — 2025-04-30

Environmental contamination, particularly water pollution, poses serious threats to ecosystems and human health worldwide. Nanotechnology offers transformative solutions, with nanoparticles emerging as powerful tools for environmental remediation due to their unique physicochemical properties such as high surface area, tunable reactivity, and catalytic capabilities. This paper explores the application of nanoparticles in water purification, focusing on mechanisms like adsorption, catalytic degradation, and disinfection, which enable the efficient removal of heavy metals, organic pollutants, and pathogens. It also examines the use of nanoparticles in controlling soil and air pollution, highlighting their ability to immobilize contaminants and degrade airborne toxins. Despite their significant advantages, the potential environmental and health risks associated with nanoparticle deployment, including toxicity and bioaccumulation, necessitate careful evaluation. Strategies for safer design, controlled application, and sustainable management of nanoparticles are discussed, emphasizing eco-friendly synthesis and life cycle assessment approaches. Future directions point toward smart, responsive nanomaterials and integrated technologies for more sustainable pollution control. Balancing innovation with environmental protection is critical to realizing the full potential of nanoparticles in addressing pressing ecological challenges. This paper underscores the need for interdisciplinary collaboration and robust regulatory frameworks to ensure that nanotechnology contributes effectively and safely to a cleaner, healthier environment.

Nanoparticles as Antimicrobial Agents: A Promising Solution Against Multidrug Resistance

Pavan TK — 2025-04-30

Multidrug-resistant (MDR) pathogens pose a significant threat to global health, rendering many conventional antibiotics ineffective and leading to prolonged infections, higher mortality rates, and increased healthcare costs. As resistance mechanisms continue to evolve, alternative approaches are urgently needed. Nanoparticles (NPs), due to their unique physicochemical properties, have emerged as a promising solution to combat MDR infections. This paper explores the potential of nanoparticles as antimicrobial agents, focusing on their mechanisms of action, types, and applications in fighting bacterial, fungal, and viral resistance. Metal nanoparticles such as silver, copper, and gold, along with metal oxides, carbon-based materials, and polymeric hybrids, demonstrate potent antimicrobial activity through mechanisms including membrane disruption, generation of reactive oxygen species, and interference with microbial DNA and enzymes. Additionally, nanoparticles can be engineered to enhance drug delivery, overcoming challenges such as biofilm formation and poor antibiotic penetration. Despite their promise, several challenges remain, including toxicity concerns, nanoparticle resistance, and regulatory hurdles. Future innovations in multifunctional and smart nanoparticles, along with green synthesis techniques, hold the potential to enhance therapeutic outcomes and reduce side effects. This paper concludes that nanoparticles represent a vital tool in the fight against MDR pathogens, offering new avenues for antimicrobial therapy, but further research, clinical trials, and regulatory frameworks are necessary for their widespread clinical application.

Generative models for designing smart nanomaterials with controlled drug release

Ruqsana Khanum — 2025-05-15

Smart nanomaterials that are capable of controlled drug release have gained significant attention in recent years due to their potential to revolutionize drug delivery systems. These materials, designed to release therapeutic agents in a controlled and site- specific manner, can significantly enhance the effectiveness of treatments while minimizing adverse side effects. However, the design of such materials remains a complex and resource-intensive process. With the rise of artificial intelligence (AI) and machine learning (ML), generative models have emerged as an innovative approach to overcome these challenges. These models utilize large datasets and computational algorithms to generate novel nanomaterial designs with optimized properties for controlled drug release. This article explores the role of generative models in nanomaterial design, particularly their potential in optimizing parameters such as particle size, surface charge, and composition, all of which are critical for regulating drug release kinetics. The integration of generative design principles with nanofabrication technologies can facilitate the creation of more efficient and personalized drug delivery systems.

AI-guided design of nanocarriers for targeted drug delivery in tumors

Dimple M.D — 2025-05-15

Targeted drug delivery represents a critical advancement in cancer treatment, offering improved efficacy and minimized systemic toxicity. Nanocarriers have emerged as promising vehicles for site-specific delivery of anticancer drugs due to their customizable physicochemical properties. However, designing nanocarriers capable of effective tumor targeting remains a complex challenge, given the diverse variables involved in tumor biology, drug kinetics, and nanoparticle interactions with biological environments. The integration of artificial intelligence (AI) into the nanocarrier design process is transforming the landscape of drug delivery research. This article explores the use of AI, particularly machine learning and deep learning models, in guiding the rational design of nanocarriers for tumor-targeted drug delivery. A framework is proposed for utilizing AI tools to optimize design parameters, predict biological interactions, and improve formulation outcomes, thus accelerating the development of effective cancer therapies.

AI-driven toxicity profiling of engineered nanomaterials on human cells

Kavya — 2025-05-15

Engineered nanomaterials (ENMs) are rapidly emerging as transformative agents in fields such as drug delivery, imaging, and environmental remediation, offering unique properties not seen in bulk materials. Despite their promising applications, concerns have been raised about their potential toxicity to human cells. Traditional methods of evaluating nanomaterial toxicity are often slow, expensive, and fail to fully replicate the complex biological processes that occur in the human body. In recent years, artificial intelligence (AI) has emerged as a powerful tool to accelerate toxicity profiling by enabling high-throughput analysis of data and predictive modeling. This research explores the role of AI in the toxicity assessment of ENMs, with a focus on predicting their effects on human cells. By utilizing machine learning algorithms and integrating multi-omics data, AI can provide a more comprehensive and efficient approach to profiling the toxicological risks of ENMs, facilitating the development of safer nanomaterials.