In order to predict the early failure of organic insulator, Co3O4@TiO2@Y2O3 nanocomposites was prepared and characterized (XRD, SEM, EDS, FTIR, UV-vis-NIR, XPS) to detect decomposition CO gas. A simple experimental platform was built to verify the excellent adsorption, stability, selectivity and repeatability of the composite. Then, the mechanism of adsorption enhancement was analyzed by heterojunction. Aiming at 170 sets of gas sensing data sets, Successive Projections Algorithm (SPA) was used to extract data features, and grey wolf optimization vector machine regression (GWO-SVR) model was established to predict carbon monoxide concentration. The correlation coefficient (RP), root mean square error (RMSEP) and calculation time of prediction set were 99.3025%, 0.0418 and 1.47 s, respectively. Therefore, the combination of the superior properties of a composite sensitive material and the small sample quantitative prediction model is a promising method for gas sensors in the future.
Keywords: CO; Gas sensor; Nanocomposites; Quantitative model.
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