Ammonia identification using shear horizontal surface acoustic wave sensor and quantum neural network model

The ammonia detection at room temperature employing a shear horizontal surface acoustic wave (SH-SAW) sensor coated with polyaniline (PANI) film and the recognition of ammonia concentrations in humid environments based on quantum neural network (QNN) have been investigated in this study. Studies were performed in the ranges of 0–67.5% relative humidity and 15–72 ppm ammonia. The frequency shift of SH-SAW was measured to detect the presence of ammonia. The SH-SAW sensor in this study responded to the ammonia gas and could be recovered using dry nitrogen. Detecting at an ammonia concentration of 40.91 ppm in dry environment, the frequency shift was 0.75 ppm and the noise level was 0.08 ppm. In humid environment, the frequency shift increased as the humidity increased. In order to recognize the ammonia in humid environment, the QNN was used as the identifier. From the performance results shown, the neural model we proposed can effectively perform the identification of ammonia in a common ambience and overcome the inference of humidity caused.