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Organisationseinheit der BAM
In this work, two novel donor/acceptor (D/A) complexes, namely, diindenoperylene (DIP)/1,3,4,5,7,8-hexafluoro-tetracyanonaphthoquinodimethane (F6TCNNQ) and alpha-sexithiophene (6T)/F6TCNNQ, are studied. The D/A complexes segregate in form of π−π stacked D/A cocrystals and can be observed by X-ray scattering. The different conformational degrees of freedom of the donor molecules, respectively, seem to affect the thin-film crystalline texture and composition of the D/A mixtures significantly. In equimolar mixtures, for DIP/F6TCNNQ, the crystallites are mostly uniaxially oriented and homogeneous, whereas for 6T/F6TCNNQ, a mostly 3D (isotropic) orientation of the crystallites and coexistence of domains of pristine compounds and D/A complex, respectively, are observed. Using optical absorption spectroscopy, we observe for each of the two mixed systems a set of new, strong transitions located in the near-IR range below the gap of the pristine compounds: such transitions are related to charge-transfer (CT) interactions between donor and acceptor. The optical anisotropy of domains of the D/A complexes with associated new electronic states is studied by ellipsometry. We infer that the
CT-related transition dipole moment is perpendicular to the respective π-conjugated planes in the D/A complex.
X-ray reflectivity (XRR) is a powerful and popular scattering technique that can give valuable insight into the growth behavior of thin films. This study Shows how a simple artificial neural network model can be used to determine the thickness, roughness and density of thin films of different organic semiconductors [diindenoperylene, copper(II) phthalocyanine and alpha-sexithiophene] on silica from their XRR data with millisecond computation time and with minimal user input or a priori knowledge. For a large experimental data set of 372 XRR curves, it is shown that a simple fully connected model can provide good results with a mean absolute percentage error of 8–18% when compared with the results obtained by a genetic least mean squares fit using the classical Parratt formalism. Furthermore, current drawbacks and prospects for improvement are discussed.