Electrolyte-Gated Graphene Field Effect Transistor-Based Ca2+ Detection Aided by Machine Learning

Flexible electrolyte-gated graphene field effect transistors (Eg-GFETs) are widely developed as sensors because of fast response, versatility and low-cost. However, their sensitivities and responding ranges are often altered by different gate voltages. These bias-voltage-induced uncertainties are an obstacle in the development of Eg-GFETs. To shield from this risk, a machine-learning-algorithm-based LgGFETs' data analyzing method is studied in this work by using Ca2+ detection as a proof-of-concept. For the as-prepared Eg-GFET-Ca2+ sensors, their transfer and output features are first measured. Then, eight regression models are trained with the use of different machine learning algorithms, including linear regression, support vector machine, decision tree and random forest, etc. Then, the optimized model is obtained with the random-forest-method-treated transfer curves. Finally, the proposed method is applied to determine Ca2+ concentration in a calibration-free way, and it is found that the relation between the estimated and real Ca2+ concentrations is close-to y = x. Accordingly, we think the proposed method may not only provide an accurate result but also simplify the traditional calibration step in using Eg-GFET sensors.

Automated summary

In this work, a machine-learning-algorithm-based data analyzing method is developed for flexible electrolyte-gated graphene field effect transistors (Eg-GFETs) to address the bias-voltage-induced uncertainties. Ca2+ detection is used as a proof-of-concept. Eight regression models are trained with different machine learning algorithms, and the optimized model is obtained. The proposed method accurately determines Ca2+ concentration in a calibration-free way and simplifies the traditional calibration step in using Eg-GFET sensors.