Author(s) :

Shaistha H.

Volume/Issue :

Abstract :

Antimicrobial resistance (AMR) is a major global health threat, as it undermines the effectiveness of traditional antibiotics against multidrug-resistant (MDR) pathogens. To address this issue, the use of nanomaterials, particularly nanoparticles, has gained significant attention due to their unique antimicrobial properties. Nanoparticles exhibit potent antimicrobial effects by disrupting bacterial cell membranes, generating reactive oxygen species, and interfering with bacterial metabolism. However, the synthesis of antimicrobial nanoparticles requires precise control over their properties such as size, shape, surface charge, and composition. Traditional approaches to nanoparticle synthesis are often resource-intensive and time-consuming. To streamline this process, artificial intelligence (AI) has emerged as a powerful tool for designing, optimizing, and automating the synthesis of antimicrobial nanomaterials. AI-driven models, including machine learning (ML), can predict nanoparticle properties and optimize synthesis conditions, reducing the time and cost required for nanoparticle development. Furthermore, AI can integrate with high-throughput synthesis techniques to rapidly generate and test large numbers of nanoparticle formulations. Real-time monitoring and control of the synthesis process, enabled by AI, allows for dynamic adjustments to maintain optimal conditions, ensuring reproducibility and scalability. While challenges such as the need for large, high-quality datasets and model generalization across nanoparticle systems persist, AI holds immense potential to revolutionize the design and production of antimicrobial nanoparticles, offering novel solutions to combat AMR in the future.

No. of Downloads :

0