Quantitative Analysis of Acetone in Transformer Oil Based on ZnO NPs@Ag NWs SERS Substrates Combined with a Stoichiometric Model

Abstract

Acetone is an essential indicator for determining the aging of transformer insulation. Rapid, sensitive, and accurate quantification of acetone in transformer oil is highly significant in assessing the aging of oil-paper insulation systems. In this study, silver nanowires modified with small zinc oxide nanoparticles (ZnO NPs@Ag NWs) were excellent surface-enhanced Raman scattering (SERS) substrates and efficiently and sensitively detected acetone in transformer oil. Stoichiometric models such as multiple linear regression (MLR) models and partial least square regressions (PLS) were investigated to quantify acetone in transformer oil and compared with commonly used univariate linear regressions (ULR). PLS combined with a preprocessing algorithm provided the best prediction model, with a correlation coefficient of 0.998251 for the calibration set, 0.997678 for the predictive set, a root mean square error in the calibration set (RMSECV = 0.12596 mg/g), and a prediction set (RMSEP = 0.11408 mg/g). For an acetone solution of 0.003 mg/g, the mean absolute percentage error (MAPE) was the lowest among the three quantitative models. For a concentration of 7.29 mg/g, the MAPE was 1.60%. This method achieved limits of quantification and detections of 0.003 mg/g and 1 μg/g, respectively. In general, these results suggested that ZnO NPs@Ag NWs as SERS substrates coupled with PLS simply and accurately quantified trace acetone concentrations in transformer oil.

Keywords: SERS; ZnO NPs@Ag NWs; acetone; quantification; transformer oil.

Figure 1

Characterization of ZnO NPs@Ag NWs (0.02 g) by transmission electron microscopy. TEM (a), HAADF-STEM (b), EDS mapping images (c–e), HRTEM (f), EDS spectrum (g), and SAED pattern (h) of ZnO NPs@Ag NWs (0.02 g).

Figure 2

XRD patterns of Ag NWs and ZnO NPs@Ag NWs.

Figure 3

(a) XPS of ZnO NPs@Ag NWs. (b–d) High-resolution XPS spectra of Ag 3d, Zn 2p, and O 1s.

Figure 4

(a) SEM images of Ag NWs, and the inset shows the diameter distribution of Ag NWs. (b–d) SEM images of ZnO NPs@Ag NWs (0.005, 0.02, and 0.2 g), and the insets are the particle size distribution of the loaded ZnO NPs. (e) SERS spectra of 10⁻⁶ M R6G adsorbed on Ag NWs, ZnO NPs@Ag NWs, and ZnO. (f) Dependence of the peak intensity value at 1649 cm⁻¹ with various ZnCl₂.

Figure 5

SERS spectra of R6G at a concentration of 10⁻⁶ to 10⁻⁹ M (a) and 10⁻¹⁰ to 10⁻¹² M (b). SERS spectra (c) and uniformity (d) of 10⁻⁶ M R6G on ZnO NPs@Ag NWs at 20 random positions.

Figure 6

SERS spectra: long-term stability of 10⁻⁶ M R6G on Ag NWs (a,b) and ZnO NPs@Ag NWs (c,d) was measured every two days during one month at room temperature.

Figure 7

(a) Raman spectra of ZnO NPs@Ag NWs substrates, pure water, acetone, acetone extract (extractant was pure water), and acetone extract enhanced by ZnO NPs@Ag NWs. (b) SERS spectra of different acetone extract concentrations (the inset shows the local magnifications of 0.09, 0.03, 0.01, and 0.003 mg/g).

Figure 8

Analytical results of models. Scatter plots of predicted concentration (log₃ (mg·g⁻¹)) vs. actual concentration (log₃ (mg·g⁻¹)) for ULR (a), MLR (b), and PLS (d) models. (c) RMSECV vs. PLS components for the PLS model.