We present four-dimensional (4D) scanning transmission electron microscopy (STEM) analysis to obtain a high level of detail regarding the nanoscale ordering within largely disordered organic semiconducting polymers. Understanding nanoscale molecular ordering in semiconducting polymers is crucial due to its connection to the materials' important properties. However, acquiring such information in a spatially localized manner has been limited by the lack of a nanoscale experimental probe, weak signal from ordering, and radiation damage to the sample. By collecting nanodiffraction patterns with a high dynamic range pixelated detector, we acquired statistically robust, high signal-to-noise ratio diffraction patterns from semiconducting organic materials, including poly(3-hexylthiophene-2,5-diyl) (P3HT), P3HT/[6,6]-phenyl C61 butyric acid methyl ester, and indacenodithiophene-co-benzothiadiazole (IDTBT), which largely have disordered structures. Real-space images of the ordered domains were reconstructed from the 4D-STEM data set for a variety of scattering vectors and in-plane angles to capture the different molecular stacking distances and their in-plane orientation. These were then analyzed to obtain the average size of the ordered domains within the sample. Such measurements were arranged in a two-dimensional (2D) histogram, which showed a direct relationship between the type and size of molecular ordering. Complementary analyses, such as intensity variance and angular correlation, were applied to obtain ordering and symmetry information. These analyses enabled us to directly characterize the alkyl and π-π stacking of P3HT, as well as the fullerene domains caused by donor segregation in the P3HT sample. Furthermore, the analysis also captured changes in the P3HT domains when the fullerenes are incorporated. Lastly, IDTBT showed a much lesser degree of ordering without much disinclination between the domains within the 2D histogram. The 4D-STEM analysis that we report here unveils new details of molecular ordering that can be used to optimize the properties of this important class of materials.
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