Raman Spectroscopy of Reduced Graphene Oxide: A Review Highlighting Defect-Induced Bands and a Glimpse into Machine Learning Approaches

Raman spectroscopy is a powerful tool for probing the structural and electronic properties of graphene-based materials, however, a focused review on reduced graphene oxide (RGO) is limited This review critically examines the Raman signatures of RGO, with a particular emphasis on defect-related bands arising from structural disorder and residual functionalities. The reliability and limitations of widely used intensity ratios and quantitative models are discussed, highlighting unconventional ratios for better analysis. By comparing recent advances, defect evolution can be decoded to better connect Raman features with physical and chemical properties. Emerging machine learning approaches for automated Raman analysis including preprocessing pipelines and algorithm strategies are also discussed. This work provides both a consolidated reference and a forward-looking perspective on Raman spectroscopy as a noninvasive tool for functional graphene materials.