Author(s) :

Bharath Kumar.

Volume/Issue :

Abstract :

Bioremediation, utilizing microorganisms or plants to degrade pollutants, faces challenges in dealing with difficult contaminants like heavy metals and organic compounds. Nanotechnology has emerged as a promising tool to enhance bioremediation, owing to the unique properties of nanomaterials such as high surface area and chemical reactivity. However, the design of effective nanomaterials for environmental cleanup remains complex due to the variability in material properties and pollutant interactions. Traditional trial-and-error methods for nanomaterial design are resource-intensive and time-consuming. Artificial intelligence (AI) has become a transformative solution to optimize nanomaterial design and performance. AI-powered predictive models, leveraging large datasets and advanced algorithms, can simulate nanomaterial behavior, enabling the identification of optimal properties for pollutant removal. Machine learning (ML) techniques, such as supervised learning, unsupervised learning, and reinforcement learning, play critical roles in predicting nanomaterial performance and optimizing synthesis parameters. Additionally, AI enables the use of multiscale modeling to predict nanomaterial behavior across atomic, molecular, and larger scales, improving their interaction with pollutants and microbial communities. By integrating AI with high-throughput screening systems, researchers can rapidly evaluate and optimize nanomaterials for effective bioremediation, accelerating the development of sustainable environmental solutions.

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