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Optical Biosensor Based on Graphene and Its Derivatives for Detecting Biomolecules
Guangmin Ji1, Jingkun Tian1, Fei Xing1
1School of Physics and Optoelectronic Engineering, Shandong University of Technology, Zibo 255000, China.
Graphene and its derivatives offer advanced optical properties for high-sensitivity, real-time biosensing of biomolecules. This review covers their preparation, optical sensor applications, and future challenges for widespread use.
Area of Science:
- Materials Science
- Nanotechnology
- Biomedical Engineering
Background:
- Graphene and its derivatives possess unique optical, electrical, and physical properties ideal for biosensing applications.
- These materials exhibit excellent optical characteristics, including tunable absorption and fluorescence, enabling non-destructive biomolecule detection.
Purpose of the Study:
- To review the preparation methods for graphene and its derivatives.
- To summarize recent advancements in optical biosensors utilizing graphene-based materials.
- To analyze the advantages and disadvantages of graphene derivatives in various sensor types.
Main Methods:
- Review of surface plasmon resonance (SPR), surface-enhanced Raman spectroscopy (SERS), fluorescence resonance energy transfer (FRET), and colorimetric sensing principles.
- Analysis of surface functionalization techniques for graphene and its derivatives.
- Examination of applications in detecting biomolecules like cells, proteins, nucleic acids, and antigen-antibodies.
Main Results:
- Graphene-based optical biosensors offer ultra-fast, highly sensitive, label-free, and real-time detection of biomolecular interactions and surface structure changes.
- These sensors are versatile, applicable to a wide range of biomolecules and complex environments.
- The review analyzes the suitability of graphene and its derivatives for different sensing modalities.
Conclusions:
- Graphene and its derivatives are promising for developing high-performance optical biosensors.
- Current challenges include achieving applicability, reusability, low cost, and scalability for complex environments.
- Future research should focus on overcoming these challenges for practical, widespread adoption.
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