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

Mantian XueCharles MackinWei-Hung WengJiadi ZhuYiyue LuoShao-Xiong Lennon LuoAng-Yu LuMarek HempelElaine McVayJing KongTomás Palacios
Published in: Nature communications (2022)
Two-dimensional materials such as graphene have shown great promise as biosensors, but suffer from large device-to-device variation due to non-uniform material synthesis and device fabrication technologies. Here, we develop a robust bioelectronic sensing platform  composed of  more than 200 integrated sensing units, custom-built high-speed readout electronics, and machine learning inference that overcomes these challenges to achieve rapid, portable, and reliable measurements. The platform demonstrates reconfigurable multi-ion electrolyte sensing capability and provides highly sensitive, reversible, and real-time response for potassium, sodium, and calcium ions in complex solutions despite variations in device performance. A calibration method leveraging the sensor redundancy and device-to-device variation is also proposed, while a machine learning model trained with multi-dimensional information collected through the multiplexed sensor array is used to enhance the sensing system's functionality and accuracy in ion classification.
Keyphrases
  • machine learning
  • high speed
  • high throughput
  • big data
  • artificial intelligence
  • healthcare
  • gold nanoparticles
  • mass spectrometry
  • health information
  • high density
  • walled carbon nanotubes