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Owing to the correlation between acetone in human′s exhaled breath (EB) and blood glucose, the development of EB acetone gas-sensing devices is important for early diagnosis of diabetes diseases. In this article, a noninvasive blood glucose detection device through acetone sensing in EB, based on an α-Fe2O3-multiwalled carbon nanotube (MWCNT) nanocomposite, was successfully developed. Different amounts of α-Fe2O3were added to the MWCNTs by a simple solution method. The optimized acetone gas sensor showed a response of 5.15 to 10 ppm acetone gas at 200 °C. Also, the fabricated sensor showed very good sensing properties even in an atmosphere with high relative humidity. Since the EB has high humidity, the proposed sensor is a promising device to exactly detect the amount of acetone in EB with high humidity. The sensor was powered by a 3200 mAh battery with the possibility of charging using mains electricity. To increase the reliability and calibration of the sensing device, a practical test was taken to detect acetone EB from 50 volunteers, and a deep learning algorithm (DLA) was used to detect the effect of various factors on the amount of acetone in each person’s acetone EB. The proposed device with ±15 errors had almost 85% correct responses. Also, the proposed device had excellent response, short response time, good selectivity, and good repeatability, leading it to be a suitable candidate for noninvasive blood glucose sensing.

In this article, a handheld gadget for breath alcohol detection was developed. The proposed device was based on CuO-decorated Fe2O3 nanoflakes (NFs). A miniaturized platinum micro-heater was integrated into the sensor substrate, already equipped with the interdigitated electrodes (IDEs). The heater was calibrated by applying different voltages to provide the desired temperatures. For decoration of CuO nanoparticles (NPs) on Fe2O3 NFs, different thicknesses of Cu thin films were deposited on the Fe2O3 NFs using dc magnetron sputtering, and by subsequent annealing, CuO as isolated NPs were decorated on Fe2O3 NFs. The sensing results showed that the decoration of CuO NPs led to an improvement in the sensing properties of the gas sensor. The gas sensor can be powered by foldable solar panels or mains electricity. The proposed gas sensor was able to wirelessly send the sensing results to the smartphone where the operator could monitor the changes in the gas sensor response in real-time. The optimal gas sensor showed a response of 10–100 ppm ethanol at 250 °C and the response and recovery times were 12 and 57 s, respectively. Hence, relatively fast response time, high response, good repeatability, and selectivity of optimal gas sensors make it a suitable candidate for alcohol sensing applications.

In this research we have proposed a high selectivity Isopropanol gas sensor. The sensor shows significant resistance change only to Isopropanol gas. The synthesis method of flower-like SnO2nanostructures, the electrode material and design, and the optimized working temperature provide the high selectivity and high response of the sensor. The SnO2nanoflowers (NFs) have been synthesized in a two-step process as the gas sensitive layer. The sensor shows its best performance on Au interdigitated electrodes. The optimized working temperature is obtained at 150 °C. The proposed sensor has a high sensitivity, good repeatability, long-term stability and remarkable selectivity. The responses of the sensor to 100 ppm of isopropanol at 150 °C is 71 and the sensor is capable of keeping almost 96% of the initial response in a 40 d period.