Controlled hydrothermal synthesis of Ag nanowires and their antimicrobial properties

First-principles study of mercaptoundecanoic acid molecule adsorption and gas molecule penetration onto silver surface: an insight for corrosion protection

Recently, 11-mercaptoundecanoic acid (MUA) molecule has attracted attention as a promising passivation agent of Ag nanowire (NW) network electrode for corrosion inhibition, but the underneath mechanism has not been elaborated. In this work, we investigate adsorption of MUA molecule on Ag(1 0 0) and Ag(1 1 1) surface, adsorption of air gas molecules of H2O, H2S and O2 on MUA molecular end surface, and their penetrations into the Ag surface using the first-principles calculations. Our calculations reveal that the MUA molecule is strongly bound to the Ag surface with the binding energies ranging from -0.47 to -2.06 eV and the Ag-S bond lengths of 2.68-2.97 Å by Lewis acid-base reaction. Furthermore, we find attractive interactions between the gas molecules and the MUA@Ag complexes upon their adsorptions and calculate activation barriers for their migrations from the outermost end of the complexes to the top of Ag surface. It is found that the penetrations of H2O and H2S are more difficult than the O2 penetration due to their higher activation barriers, while the O2 penetration is still difficult, confirming the corrosion protection of Ag NW network by adsorbing the uniform monolayer of MUA. With these findings, this work can contribute to finding a better passivation agent in the strategy of corrosion protection of Ag NW network electrode.

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

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.