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Graphene has attracted significant attention as a high-performance material for applications ranging from energy storage to transparent electronics. Yet, existing environmental assessments remain fragmented, with inconsistent inventories, functional units, and waste modeling limiting cross-route comparability. To address this gap, this study presents a cradle-to-gate life cycle analysis comparing four representative synthesis routes: chemical vapor deposition, epitaxial growth, exfoliation, and chemical oxidation–reduction. The results reveal substantial variation in environmental performance across routes. Among bottom-up methods, the continuous elevated pressure variant of chemical vapor deposition exhibited the lowest global warming potential in the base scenario (2.7 kg CO 2 eq per m 2 of graphene). Under ideal conditions, the batch-based design became comparatively advantageous because of its lower copper substrate and methane use. In top-down methods, electrochemical exfoliation offered the most favorable profile, reaching 5.2 kg CO 2 eq per kg of graphene by relying on a recoverable acid. Contribution analysis shows that bottom-up methods are largely driven by electricity and carbon precursors, while top-down routes display a more balanced distribution across electricity, chemicals, and waste. Normalized across functional units, top-down routes show much lower impacts, a difference driven by their distinct material quality and application requirements rather than inherent environmental superiority. Overall, this analysis demonstrates how harmonized system boundaries and functional units clarify trade-offs among routes, while highlighting the importance of industrial-scale data and end-of-life modeling for guiding environmentally responsible graphene production. • Harmonized cradle-to-gate graphene LCA with explicit chemical waste modeling. • CVD4 is more energy-efficient, while CVD2 uses less methane and substrate. • Electrochemical exfoliation is the lowest-impact top-down method. • Bottom-up routes driven by electricity/carbon; top-down split across 3 drivers. • Top-down consistently outperforms bottom-up across functional units.
Mammadli et al. (Thu,) studied this question.