Integrated biosensor platform based on graphene transistor arrays for real-time high-accuracy ion sensing

Area of Science:

• Bioelectronics
• Materials Science
• Analytical Chemistry

Background:

• Two-dimensional materials like graphene show potential for biosensing applications.
• Significant device-to-device variation hinders the reliability of current graphene-based biosensors due to synthesis and fabrication inconsistencies.

Purpose of the Study:

• To develop a robust bioelectronic sensing platform that overcomes device variations for reliable ion detection.
• To achieve rapid, portable, and accurate measurements of multiple ions in complex solutions.

Main Methods:

• Integration of over 200 sensing units into a single platform.
• Development of custom high-speed readout electronics.
• Application of machine learning for data analysis, calibration, and ion classification.

Main Results:

• Demonstrated reconfigurable multi-ion electrolyte sensing (potassium, sodium, calcium).
• Achieved highly sensitive, reversible, and real-time ion detection despite device variations.
• Developed a calibration method utilizing sensor redundancy and a machine learning model for enhanced accuracy.

Conclusions:

• The developed platform offers a reliable solution for multi-ion sensing, overcoming traditional limitations of 2D material-based biosensors.
• Machine learning and sensor redundancy are key to enhancing the functionality and accuracy of bioelectronic sensing systems.
• This technology enables portable and precise ion measurements in complex biological or environmental samples.