Towards circularity of plastics: A materials informatics perspective

Abstract

Plastic pollution and the associated adversities have been intensively researched recently, providing ample solutions with diverse possibilities and yielding a considerable corpus of literature in plastic waste management (PWM). Regardless of the vast range of techniques formulated, such as mechanical recycling and chemical depolymerization, many of these approaches have limitations including significant costs, ecological threats, and inefficiencies in handling diverse plastic types. Manual analysis of these challenges and the reported solutions from the vast collection of interdisciplinary research papers is extremely laborious. Herein, using tools of data science to create a network of ∼350,000 papers and subsequent clustering to identify various protocols for PWM and determining the main paths of their knowledge evolution, we review their progress. The broad objective of this analysis is to provide a comprehensive understanding of different PWM techniques, with a focus on the importance of integrated, technologically advanced, and environmentally conscious approaches to solve plastic pollution. We identify four major categories of PWM (physical, chemical, physio-chemical, and biological) and analyze their mechanistic details. Our study highlights the critical need for the establishment of more sustainable PWM methodologies, supporting the integration of artificial intelligence to refine process optimization and cultivate interdisciplinary collaboration focused on advancing a circular economy and reducing plastic waste. Together with a deep discussion on the gaps between the set goals and the current achievements identified, these analyses could be a useful tool to confront the PW crisis.