The hydrogen sensing capability of zinc oxide-containing sensors: Modeling by the general regression artificial neural network

Abstract

This research applies general regression (GR) neural networks to model the hydrogen sensing capability of pure zinc oxide (ZnO) and ZnO nanocomposites containing different additives. The sensor's topology/composition, temperature, and hydrogen concentration in the atmosphere are the independent features of the GR models. Sensitivity analysis approves that the Gaussian spread value must adjust to 23.5 × 10−6 to achieve the most accurate predictions. This model forecasts 297 actual responses of ten ZnO-containing sensors with mean absolute error, relative absolute error, and regression coefficient of 0.29, 1.56%, and 0.9977, respectively. These prediction accuracies are better than those obtained by cascade feedforward, radial basis, Elman recurrent, and multilayer perceptron neural networks. The relevancy test shows that the sensor's hydrogen detection ability improves with the sensor geometry and ZnO additive dose. ZnO–Co3O4 is the best sensor composition to detect even low hydrogen concentrations. Also, the nanowire and nanofiber are the best sensor topologies for the considered task.