Carbon materials derived from waste polyethylene terephthalate for sodium-ion batteries: Progress, challenges, and future perspectives

The rapid commercialization of sodium-ion batteries (SIBs) has intensified the demand for scalable, high-performance anode materials with improved sustainability. Hard carbon remains the dominant commercial anode material for SIBs owing to its structural stability, low operating potential, and favorable adsorption-intercalation-pore-filling sodium storage behavior. Polyethylene terephthalate (PET) is one of the most abundant plastic wastes worldwide, creating both environmental burdens and resource inefficiencies. Therefore, transforming waste PET into hard carbon offers a dual opportunity for sustainable energy storage and circular material utilization. This review critically examines recent advances in PET-derived hard carbon for SIB applications, with an emphasis on pyrolysis chemistry, defect evolution, closed-pore formation, and their coupling with sodium storage mechanisms. The multiphase adsorption-intercalation-filling model is discussed in the context of PET-specific microstructural evolution, highlighting the roles of pore entrance size, oxygen functionality, stacking disorder, and defect density in governing the plateau capacity and initial Coulombic efficiency. Reported PET-derived carbons demonstrate reversible capacities exceeding 400 mAh g −1 and ICE values above 80%, approaching the commercial hard‑carbon benchmarks. Quantitative comparisons with biomass- and pitch-derived carbons are provided in terms of gravimetric and volumetric capacities, rate capability, cycling stability, and scalability constraints. Sustainability metrics, including feedstock availability, processing energy demand, carbon footprint, and circular integration, are evaluated to assess industrial feasibility. Finally, key challenges, such as oxygen-induced sodium entrapment, pore stability, electrode densification, and manufacturing reproducibility, are analyzed, and future directions, including molecular precursor engineering and AI-assisted optimization, are proposed.