Artificial Intelligence in Recycling Wastes to Carbon Nanomaterials

Artificial intelligence (AI) and machine learning (ML) are transforming the process of converting waste materials into high-value carbon nanomaterials (CNMs) at a rapid pace. With the rising concerns about waste generation and resource depletion across the globe, AI-based waste valorization has emerged as an intelligent approach for converting diverse waste materials into valuable nanostructures like carbon nanotubes (CNTs), graphene, carbon dots, and biochar. Recent breakthroughs have shown that deep learning-based computer vision and intelligent classification models can greatly enhance the efficiency of waste segregation and the purity of the feedstock, which are essential factors in determining the quality and reproducibility of nanomaterials. AI-based waste-sorting models based on convolutional neural networks (CNNs) have reached a classification accuracy of over 95%, ensuring the constant quality of the precursor and improving the performance of downstream synthesis. These intelligent feedstock management systems improve the reliability of the process and make it possible to implement large-scale waste-to-nanomaterial conversion. Machine learning models such as artificial neural networks, random forests, and Gaussian process regression models can predict and optimize synthesis conditions such as temperature, catalyst composition, heating rate, and reaction time. Together, the integration of AI and ML promotes innovation, improves process robustness, and facilitates scalable waste conversion to valuable carbon nanomaterials. These intelligent systems facilitate predictive optimization, real-time process control, and sustainable manufacturing, thereby making AI-assisted waste-to-CNM conversion a revolutionary strategy for environmentally sustainable and economically feasible nanomaterial manufacturing.