Machine Learning-Empowered Plastic-Derived Porous Carbons for High-Performance CO2 Capture

Abstract

Plastic pollution and climate change are interconnected global environmental challenges. Conventional methods (incineration and landfills) exacerbate these issues by emitting greenhouse gases and releasing micro/nanoplastics. To simultaneously address these two critical environmental issues, we upcycle plastic waste into porous carbon materials, enabling high-performance postcombustion CO₂ capture in a transformative and practical manner. This strategy tackles environmental pollution, aligns with circular economy principles, and supports several of UN Sustainable Development Goals (SDGs). We conduct systematic studies, including experimental validations, numerical simulations, and machine learning (ML)-empowered optimizations, to provide detailed guidelines for upcycling plastic waste into porous carbons with high-performance CO₂ capture.