Room temperature operated flexible MWCNTs/Nb2O5 hybrid breath sensor for the non-invasive detection of an exhaled diabetes biomarker.

Abstract

Advancements in diabetes management increasingly rely on non-invasive monitoring of biomarkers present in exhaled breath. This study introduces a novel room temperature operated flexible acetone sensing platform, leveraging a hybrid material composed of multi-walled carbon nanotubes (MWCNTs) and niobium oxide (Nb₂O₅). The platform demonstrates sensitivity and selectivity towards acetone, a prominent biomarker of diabetes, offering promise for real-time health monitoring applications. The sensor exhibited a characteristic feature of fast response (25 s) and recovery times (46 s) at 50 ppm, good selectivity, and stability with a detection limit of 330 ppb. Additionally, the sensor's characteristic features were collected, and four different machine learning (ML) algorithms were applied to visualize and classify the gases with good quantification. Out of all algorithms, the random forest (RF) algorithm demonstrates the best performance. Furthermore, regression modelling was also used to quantitatively predict the gas concentration. In addition, the sensor was shown to distinguish between signals from simulated diabetic and healthy breath samples. These sensing performances indicate that the breath sensor has practical applications that could potentially provide a non-invasive monitoring method for diabetic patients.