PEDOT:PSS/QCM-based multimodal humidity and pressure sensor

Abstract A room temperature multimodal sensor composed of poly(3,4-ethylenedioxythiophene):polystyrene sulfonate (PEDOT:PSS) deposited on an AT-cut quartz crystal microbalance (QCM) crystal was fabricated. The sensor’s nonlinear motional resistance and frequency responses are deconvoluted using a feedforward backpropagation neural network (FBN), which allows a single sensor to function simultaneously as a relative humidity (RH) sensor and a pressure sensor using only two electrodes. We demonstrate that the predictive ability of the sensor is highly influenced by the data used to train the FBN. When training sets are tailored to resemble the operating conditions of the sensor, the sensor achieves an average resolution of

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