Efficient and optimized blood cancer detection using engineered graphene-based silicon-TiN-silicon multilayered...

Area of Science:

• Biomedical Engineering
• Materials Science
• Oncology

Background:

• Early detection of blood cancer is critical for improving patient survival rates and treatment efficacy.
• Current diagnostic methods may lack the sensitivity or speed required for timely intervention.
• Innovative biosensing technologies offer potential for enhanced early diagnosis.

Purpose of the Study:

• To develop and optimize a graphene-based biosensor for highly sensitive and rapid detection of blood cancer biomarkers.
• To leverage machine learning for optimizing sensor performance and diagnostic accuracy.
• To evaluate the sensor's effectiveness for early-stage blood cancer diagnosis and clinical applications.

Main Methods:

• Fabrication of a graphene-based biosensor utilizing advanced materials.
• Application of machine learning and parametric optimization techniques to enhance sensor sensitivity and performance.
• Characterization of sensor performance, including sensitivity, detection limit, quality factor, and figure of merit.

Main Results:

• The optimized graphene sensor achieved a maximum sensitivity of 1430 nm/RIU.
• An impressive detection limit of 0.044 was demonstrated, indicating high precision.
• The sensor design exhibited a high-quality factor of 125 and a figure of merit of 121.

Conclusions:

• The developed graphene biosensor, optimized via machine learning, offers high sensitivity and precision for early blood cancer detection.
• The sensor's performance metrics suggest its suitability for rapid and accurate clinical diagnosis.
• This technology has the potential to significantly improve patient outcomes through earlier intervention and targeted treatment.