Biological recognition element anchored 2D graphene materials for the electrochemical detection of hazardous pollutants

•Herein, we reviewed biorecognition elements anchored on graphene nanomaterials for hazardous pollutant detection.•Electrochemical techniques for the detection and quantification of pollutants are summarized.•Working principles, sensing mechanisms, and immobilization strategies of biorecognition elements are reviewed.•Challenges and prospects in graphene-based biological recognition platforms are outlined. The detection of hazardous pollutants in environmental and industrial settings is of critical importance for safeguarding human health and the ecosystem. Recent years witnessed significant progress and development in novel sensing platforms. The combination of biological recognition elements with two-dimensional (2D) materials enhanced the selectivity and sensitivity of pollutant detection. This review focuses on the integration of biological recognition elements, such as antibodies, nucleic acids, enzymes, aptamers, and proteins with 2D graphene for electrochemical detection of hazardous pollutants. The unique properties of graphene include high electrocatalytic activity, high surface area, and excellent conductivity, combined with the specificity and affinity of biological recognition elements, enabling selective and sensitive detection of hazardous pollutants. The synergistic coupling between 2D graphene and biological recognition elements offers several advantages, including improved sensor performance, rapid response, and potential for multi-analyte detection. Furthermore, the immobilization strategies, surface modifications, and fabrication techniques employed to anchor biological recognition elements onto graphene are discussed. The application of these bio-functionalized 2D material-based sensors for the detection of various hazardous pollutants, such as heavy metals, organic compounds, and environmental toxins, is highlighted. Moreover, the challenges and future perspectives in this field are addressed, including sensor stability, scalability, and the integration of advanced technologies, such as nanotechnology and artificial intelligence, for real-time monitoring and data analysis. [Display omitted]