Electrospun Polyacrylonitrile/Polyvinylidene Fluoride Nanofiber-Coated QCM: Preparation, Characterization, Gas Sensing Properties, and Data Validation via Artificial Neural Networks

Abstract

This study investigated the sensing properties of electrospun NFs composed of polyacrylonitrile (PAN) and polyvinylidene fluoride (PVDF) coated on quartz crystal microbalance (QCM) in response to different volatile organic compounds (VOCs), including dichloromethane, chloroform, carbon tetrachloride, benzene, and trichloroethylene. Characterization of the PAN/PVDF NFs was performed using scanning electron microscopy (SEM), thermogravimetric analysis (TGA), and Brunauer-Emmett-Teller (BET) analysis. Five different concentrations of PVDF were tried to obtain well-ordered and straightforward morphology with a thin diameter of nanofiber and characterization results indicated that PAN/PVDF NFs containing 25 wt% displayed desired morphology with the average nanofiber diameter of 183.6 +/- 40 nm. Besides, NFs indicated a high surface area of 26.0464 m2/g and 37.3729 nm average pore size. The QCM nanofiber sensor demonstrated the highest response to dichloromethane vapor among the VOCs tested. The values of the sensitivity and LOD for the nanofiber sensor PAN/PVDF was calculated as 0.0249 Hz ppm-1 and 132.53 ppm, respectively. The kinetic data for the vapors indicated that a nonlinear autoregressive neural network with exogenous input was utilized for the most accurate molecular modelling based on frequency shift values. The nonlinear autoregressive with exogenous input artificial neural network (NARX-ANN) model exhibited superior performance in fitting the experimental data for dichloromethane compared to the other VOCs, as shown by the correlation coefficient values. For all VOC modelling results, the correlation coefficient values for the QCM nanofiber sensor ranged from approximately 0.9815-0.9964.