Towards predicting the piezoelectricity and physiochemical properties of the electrospun P(VDF-TrFE) nanogenrators using an artificial neural network

Electrospun P(VDF-TrFE) nanogenrators with a wide range of piezoelectricity performance and physiochemical properties is fabricated through modification of the processing parameters such as polymer concentration, applied voltage, feed rate and electrospinning time/fibres mat thickness. In order to estimate and predict the relationships of the process parameters with the piezoelectricity performance and fibres morphology, an Artificial Neural Networks (ANN) model is developed. Results of the developed ANN model is found to be in a good agreement with experimental results with less than 5% error and shows the good potential to model physiochemical properties of the nanogenrators to predict untested conditions. •Fabrication of P(VDF-TrFE)nanofiber composite by electrospinning method with wide range of morphological and piezolectrical performance.•Considering all the processing parameters effecting on final properties of the fibres.•Successfully developing a MIMO neural network modelling for the prediction of physical and chemical properties of P(VDF-TrFE) nanofiber composite.•Validating the appropriate models by real data and revealing a specific pattern for interrelationship between crystalline structure and electrical output of nanofibers to electrospinning conditions.•Feasibility of applying the proposed model for untested conditions.