Department of Electrical Engineering & Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA.
IBM Research-Almaden, 650 Harry Road, San Jose, CA, USA.
Nat Commun. 2022 Aug 27;13(1):5064. doi: 10.1038/s41467-022-32749-4.
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.
二维材料（如石墨烯）在生物传感器方面显示出巨大的应用前景，但由于材料合成和器件制造技术的不均匀性，它们存在着较大的器件间差异。在这里，我们开发了一个由 200 多个集成传感器单元、定制的高速读出电子设备和机器学习推理组成的强大的生物电子传感平台，克服了这些挑战，实现了快速、便携和可靠的测量。该平台展示了可重构的多离子电解质传感能力，并提供了高度敏感、可逆和实时的响应，用于复杂溶液中的钾、钠和钙离子，尽管存在器件性能的变化。还提出了一种利用传感器冗余和器件间差异的校准方法，同时，使用通过复用传感器阵列收集的多维信息训练的机器学习模型来增强传感系统在离子分类方面的功能和准确性。
