Polymer–Graphene Composites for Electrochemical Sensing: A Comprehensive Review of Functionalization Pathways and Sustainable Design Strategies

Environmental pollution constitutes an increasingly complex global challenge, largely driven by industrial expansion and the consequent release of toxic species such as Cd2+, Pb2+, Cu2+, Hg2+, Fe3+, As3+, and Rh3+ into natural ecosystems. These contaminants pose significant risks to environmental integrity and public health, motivating the development of analytical technologies capable of sensitive, selective, and reliable detection. In this context, graphene-based electrochemical sensors have emerged as versatile platforms for monitoring a broad range of analytes, particularly in environmental applications involving heavy-metal detection. The intrinsic physicochemical properties of graphene derivatives have enabled low detection limits, rapid response times, and tunable selectivity. Despite analytical advances, critical challenges persist regarding operational stability in complex matrices, inter-batch reproducibility, and robustness to interfering species, which continue to hinder large-scale deployment and real-world applicability. However, challenges remain regarding stability and performance in complex arrays, reproducibility, and resistance to interference, necessitating innovative strategies for functionalization and molecular recognition. This review article establishes a comparative framework based on functionalization strategies (covalent, non-covalent, and hybrid), the chemical nature of graphene (GO, rGO, and doping), and various types of polymers (conductors and insulators), using statistical metrics such as the limit of detection (LOD), linear range, working potential, stability, and interferences, employing a bibliometric analysis using the PRISMA 2020 methodology. This comparative framework enables analysis and explanation of performance trends, and the generation of design and functionalization recommendations for versatile applications, including criteria for reproducibility and sustainability.