Chemie und Prozesstechnik
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Organisationseinheit der BAM
We present a combined experimental and theoretical study to solve the unit-cell and molecular arrangement of the tetracene thin film (TF) phase. TF phases, also known as substrate induced phases (SIP), are polymorphs that exist at interfaces and decisively impact the functionality of organic thin films, e.g., in a transistor channel, but also change the optical spectra due to the different molecular packing. As SIPs only exist in textured ultrathin films, their structure determination remains challenging compared to bulk materials. Here, we use grazing incidence Xray diffraction and atomistic simulations to extract the TF unit-cell parameters of tetracene together with the atomic positions within the unit-cell.
AI-reflectivity is a code based on artificial neural networks trained with simulated reflectivity data that quickly predicts film parameters from experimental X-ray reflectivity curves. This project has a common root with (ML-reflectivity)[https://github.com/schreiber-lab/ML-reflectivity] and evolved in parallel. Both are linked to the following publication:
Fast Fitting of Reflectivity Data of Growing Thin Films Using Neural Networks A. Greco, V. Starostin, C. Karapanagiotis, A. Hinderhofer, A. Gerlach, L. Pithan, S. Liehr, F. Schreiber, S. Kowarik (2019). J. Appl. Cryst.
For an online live demonstration using a pre-trained network have a look at github.
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.
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.