Paper Title: Deep Learning Applications in Green Chemistry and Environmental Monitoring
Author:
Abstract:
Deep learning, a subset of artificial intelligence, is revolutionizing various fields, including green chemistry and environmental monitoring. In green chemistry, deep learning models are increasingly employed for optimizing sustainable processes such as waste reduction, energy efficiency, and the development of eco-friendly materials. These models can predict molecular interactions, design new catalysts, and automate the synthesis of green compounds, all while minimizing environmental impact. In environmental monitoring, deep learning techniques facilitate real-time analysis of large datasets, enabling more accurate predictions of pollution levels, climate change, and ecosystem health. Automated sensor systems powered by deep learning can identify contaminants in air, water, and soil, contributing to more effective pollution control and management. Deep learning aids in the interpretation of satellite imagery and remote sensing data, enhancing environmental conservation efforts. As these technologies evolve, their synergy with green chemistry and environmental monitoring holds significant promise for fostering a sustainable future by reducing environmental footprints and improving ecosystem health.
Keywords:Deep Learning, Green Chemistry, Environmental Monitoring, Sustainability, Pollution Control.
DOI Link – https://doi.org/10.63431/AIJITR/2.IV.2025.01-07
Review By – Dr. Shivalika Sarkar and Dr. P. Karmakar