Abstract
Most NO gas sensors are evaluated using continuous NO gas, making it difficult to accurately recognize discontinuous gas flow. Here, to reveal the response characteristics in discontinuous gas flows, we investigated a response in various NO gas flows using a boron nitride-based memristor gas sensor. In conventional continuous gas flow, the response characteristic of 16% showed for 5 ppm NO gas, while in the pulse like gas injection with a width of 1 second and an interval of 1 second, the response only increased to 8.13%, meaning that it is difficult to estimate the overall environment of NO gas using only continuous gases, as well as showing that a host of data is needed for discontinuous gases. As a result, we found that a neural network model trained by continuous/discontinuous NO gas data accurately predicts the concentration of discontinuous NO gas with a low error of 5.6%.
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