Sensitivity prediction and analysis of nanofiber-based gas sensors using solubility and vapor pressure parameters

Abstract

Here, we propose a simple yet effective method to predict gas sensor sensitivity based on solubility and vapor pressure. As sensing devices for the case study, we employed quartz crystal microbalance sensors coated with polyvinyl acetate (PVAc) nanofibers. The solubility was represented by the relative energy density (RED), while the vapor pressure was expressed by the logarithm of the vapor pressure (log P). To create a prediction model, a chemometric technique involving a machine learning algorithm of k-nearest neighbor (KNN) regression was used in the analysis. Using both parameters (i.e., RED and log P) as input, a determination coefficient (R 2) of up to 1 was obtained, indicating highly correlated parameters. This proposed method could not only enable an accurate prediction of sensor sensitivity, but also provide a path to select the suitable sensing materials for specific target analytes in high-performance gas sensors.