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A novel mediator-free, non-enzymatic electrochemical sensor, based on a graphene-Schottky junction, was fabricated for glucose detection. The sensor offers a promising alternative to the conventional enzyme-catalyzed electrochemical continuous glucose monitoring systems (CGM), as it overcomes many of the drawbacks attributed to the enzymatic nature; namely, irreversibility, drift, and interference with body fluids, which affect their accuracy, reliability and longevity. Enhanced performance of the sensors is demonstrated through the band interaction at the graphene-Schottky junction, which yields stronger forward/reverse currents in response to 50 {mu}L glucose drop. Under optimized conditions, the linear response of the sensor to glucose concentration was valid in the range from 0 to 15 mmol/L with a detection limit of 0.5 mmol/L. The results indicated that the proposed sensor provided a highly sensitive, more facile method with good reproducibility for continuous glucose detection.
This study proposes a novel design of glucose sensor with enhanced selectivity and sensitivity by using graphene Schottky diodes, which is composed of Graphene (G)/Platinum Oxide (PtO)/n-Silicon (Si) heterostructure. The sensor was tested with differ
Continuous Glucose Monitoring (CGM) has enabled important opportunities for diabetes management. This study explores the use of CGM data as input for digital decision support tools. We investigate how Recurrent Neural Networks (RNNs) can be used for
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