Abstract

AbstractSelective detection of acetone levels in exhaled human breath is important to diagnose complications of diabetes mellitus by the non‐invasive route. In the current study, we are reporting a hybrid graphene oxide (GO) field‐effect transistor (FET) based multisensory array for the selective detection of breath acetone by using a feature extraction and pattern recognition route. Backgated GO‐FET was functionalized with WO3 flower‐shaped nanostructures and noble metal (Au, Pd, Pt) nanoparticles to form hybrid FET sensors. By applying optimized gate voltages, the sensor response was enhanced several orders at room temperature. Therefore, an array of six sensors was tested in synthetic breath consisting of a complex mixture of multiple volatile organic compounds, various gases, 70% relative humidity and different levels of acetone. The sensors′ outcomes were then processed with phase space entire feature extraction method, and the different levels of acetone were differentiated in the synthetic breath by using Fisher discriminant analysis.

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