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Updated: Oct 24, 2026

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Published on: March 4, 2021
Advancements in bottom-up synthesis routes for monolayer graphene: a review
Muhammad A Awan1,2, Szymon Abrahamczyk2, Maria Hasan2,3
1Centre for Nanotechnology, Faculty of Materials Sciences and Technology, Vysoka skola banska-Technicka univerzita Ostrava, Ostrava, Moravian-Silesian Region, Czech Republic.
Abstract:
The synthesis of monolayer graphene has progressed from exfoliation to bottom-up strategies, enabling increasing atomic-level control. This review compares key routes, chemical vapour deposition (CVD), SiC epitaxy, arc discharge, molecular assembly and single-atom catalysts (SACs) and evaluates their mechanisms, scalability and quality bottlenecks. CVD remains the most mature and scalable approach, delivering centimetre-scale-single-crystal films through optimized substrates, roll-to-roll annealing and dry transfer, yet transfer-induced defects and wafer-scale non-uniformity persist. SiC epitaxy enables transfer-free, wafer-scale graphene with well-aligned domains, but requires high temperatures and costly wafers. Arc discharge offers catalyst-free production of crystalline graphene flakes, although monolayer yield and purity remain limited. Molecular assembly provides unmatched structural programmability and atomic precision in nanoribbons and nanographene islands, typically restricted to micrometre scales and stringent conditions. SAC-mediated growth represents a precision limit, where isolated metal atoms catalyse single-carbon insertion or removal at graphene edges, though current demonstrations remain largely single-event observations. Complementing these routes, artificial intelligence/machine learning is increasingly used for parameter optimization, predictive modelling of domain size/uniformity/defects and automated quality control (convolutional neural network/ generative modelling and YOLO-based defect detection). Overall, industrially viable, defect-free monolayer graphene will require combining scalable growth, transfer-free integration, atomic-precision and AI-enabled closed-loop optimization.
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