Beyond single crystals: Imaging rubrene polymorphism across crystalline batches through lattice phonon Raman microscopy

Margaret Clapham, Ryan E. Leighton, Christopher J Douglas, Renee R. Frontiera

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Polymorphism is an issue troubling numerous scientific fields. A phenomenon where molecules can arrange in different orientations in a crystal lattice, polymorphism in the field of organic photovoltaic materials can dramatically change electronic properties of these materials. Rubrene is a benchmark photovoltaic material showing high carrier mobility in only one of its three polymorphs. To use rubrene in devices, it is important to quantify the polymorph distribution arising from a particular crystal growth method. However, current methods for characterizing polymorphism are either destructive or inefficient for batch scale characterization. Lattice phonon Raman spectroscopy has the ability to distinguish between polymorphs based on low frequency intermolecular vibrations. We present here the addition of microscopy to lattice phonon Raman spectroscopy, which allows us to not only characterize polymorphs efficiently and nondestructively through Raman spectroscopy but also concurrently gain information on the size and morphology of the polymorphs. We provide examples for how this technique can be used to perform large, batch scale polymorph characterization for crystals grown from solution and physical vapor transport. We end with a case study showing how Raman microscopy can be used to efficiently optimize a green crystal growth method, selecting for large orthorhombic crystals desired for rubrene electronic device applications.

Original languageEnglish (US)
Article number234703
JournalJournal of Chemical Physics
Issue number23
StatePublished - Dec 21 2021

Bibliographical note

Funding Information:
We would like to thank Dr. Lee Penn and Maetzin Cruz-Reyes for powder diffraction analysis and Dr. Victor Young, Jr., and the X-Ray Crystallographic Laboratory at the University of Minnesota for resources and instrumentation. We acknowledge the Minnesota Supercomputing Institute (MSI) at the University of Minnesota for resources. This work was supported by DOE under Grant No. DE-SC0018203 and by the National Science Foundation Graduate Research Fellowship under Grant No. 00074041.

Publisher Copyright:
© 2021 Author(s).

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