RESUMEN
There are notably few literature reports of electron donor-acceptor oligoynes, even though they offer unique opportunities for studying charge transport through "all-carbon" molecular bridges. In this context, the current study focuses on a series of carbazole-(C≡C)n-2,5-diphenyl-1,3,4-oxadiazoles (n = 1-4) as conjugated π-systems in general and explores their photophysical properties in particular. Contrary to the behavior of typical electron donor-acceptor systems, for these oligoynes, the rates of charge recombination after photoexcitation increase with increasing electron donor-acceptor distance. To elucidate this unusual performance, we conducted detailed photophysical and time-dependent density functional theory investigations. Significant delocalization of the molecular orbitals along the bridge indicates that the bridging states come into resonance with either the electron donor or acceptor, thereby accelerating the charge transfer. Moreover, the calculated bond lengths reveal a reduction in bond-length alternation upon photoexcitation, indicating significant cumulenic character of the bridge in the excited state. In short, strong vibronic coupling between the electron-donating N-arylcarbazoles and the electron-accepting 1,3,4-oxadiazoles accelerates the charge recombination as the oligoyne becomes longer.
RESUMEN
Automatic transmission fluids (ATF) are highly complex multi-component systems with a variety of different additive packages which suffer from manifold aging processes due to interfering factors. This work describes the development of a straightforward approach to model the aging effects by means of Fourier Transform Infrared (FTIR) spectroscopy combined with multivariate data analysis. Therefore, ATF samples were artificially aged under defined conditions by considering effects of product type, temperature, storage time and exposure to metallic materials, yielding 144 samples. For multivariate data analysis, three different approaches have been applied and compared: supervised Fisher's Linear Discriminant Analysis of principal components (PCFDA), regularized FDA (RFDA) of variables, and unsupervised PCA after orthogonalization using Error Removal by Orthogonal Subtraction (EROS + PCA). All methods worked well in reducing unwanted effects and transforming the relevant information to the first components. Combined with k-Nearest-Neighbor (kNN) prediction, RFDA leads to the best model, improving the accuracy ratios by 13%, 41%, and 12% in comparison with direct kNN classification for the target classes storage temperature, additional material and aging level, respectively. These results suggest that RFDA is highly suitable for the reduction of unwanted effects in a dataset with manifold perturbation influences. The model also predicted a correct aging level ranking when applied to unknown field samples.
