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<p>This review provides a comprehensive analysis of graphene nanomaterial-based biosensors for early cancer detection. The paper examines the fundamental properties of graphene, various synthesis methods, and functionalization strategies that enhance its biosensing capabilities. We discuss different types of graphene nanomaterials and their unique characteristics that make them suitable for cancer biomarker detection. The review explores various sensing mechanisms, including electrochemical, optical, and field-effect transistor-based approaches, highlighting their respective sensitivities and detection limits. Current challenges in implementing graphene-based biosensors for clinical applications are addressed, along with emerging solutions and future perspectives. The integration of graphene biosensors with advanced detection techniques shows promising potential for developing highly sensitive, specific, and cost-effective platforms for early cancer diagnosis, particularly beneficial for resource-limited settings<b>.</b></p>

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Roadmap to 2D Graphene Nanomaterials-Based Biosensors for Early Cancer Detection

  • 期刊论文

  • 发表日期：

    2025年3月31日

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  • Jacob Wekalao,
  • Lu Hao,
  • Ihtisham Ul Haq,
  • Muhammad Abbas Khan

摘要

This review provides a comprehensive analysis of graphene nanomaterial-based biosensors for early cancer detection. The paper examines the fundamental properties of graphene, various synthesis methods, and functionalization strategies that enhance its biosensing capabilities. We discuss different types of graphene nanomaterials and their unique characteristics that make them suitable for cancer biomarker detection. The review explores various sensing mechanisms, including electrochemical, optical, and field-effect transistor-based approaches, highlighting their respective sensitivities and detection limits. Current challenges in implementing graphene-based biosensors for clinical applications are addressed, along with emerging solutions and future perspectives. The integration of graphene biosensors with advanced detection techniques shows promising potential for developing highly sensitive, specific, and cost-effective platforms for early cancer diagnosis, particularly beneficial for resource-limited settings.