A study combining experimental, computationaland machine learning approaches on the removal of tartrazine dye using carbon nanotubes

Abstract

This study addresses the environmental threat posed by synthetic hazardous dyes, particularly tartrazine, in water bodies. We present an integrated method for the effective removal of tartrazine using Multiwalled Carbon Nanotubes (MWCNTs) synthesized via Chemical Vapor Deposition. Extensive characterization confirms MWCNTs as highly efficient adsorbents, with batch adsorption studies demonstrating their remarkable ability to remove 98.39% of tartrazine from water within 60 min at room temperature and pH 6. Density Functional Theory calculations provide molecular insights into the adsorption process, identifying six interaction possibilities and the most reactive site on tartrazine. Additionally, Machine Learning and Deep Learning models offer cost-effective predictive tools for efficient dye removal, with the deep learning model (ANN) proving most effective. This interdisciplinary approach marks a significant advancement in water treatment and environmental remediation, with future research focusing on scaling MWCNT synthesis and exploring diverse practical applications.