The crystallite size and cumulative lattice disorder of three prototypical, high-performing organic semiconducting materials are investigated using a Fourier-transform peak shape analysis routine based on the method of Warren and Averbach (WA). A thorough incorporation of error propagation throughout the multistep analysis and a weighted fitting of Fourier-transformed data to the WA model allows for more accurate results than typically obtained and for determination of confidence bounds. We compare results obtained when assuming two types of column-length distributions, and discuss the benefits of each model in terms of simplicity and accuracy. For strongly disordered materials, the determination of a crystallite size is greatly hindered because disorder dominates the coherence length, not finite size. A simple analysis based on trends of peak widths and Lorentzian components of pseudo-Voigt line shapes as a function of diffraction order is also discussed as an approach to more easily and qualitatively assess the amount and type of disorder present in a sample. While applied directly to organic systems, this methodology is general for the accurate deconvolution of crystalline size and lattice disorder for any material investigated with diffraction techniques. © 2011 American Physical Society.
Bibliographical noteKAUST Repository Item: Exported on 2020-10-01
Acknowledged KAUST grant number(s): KUS-C1-015-21
Acknowledgements: The authors thank Iain McCulloch and Martin Heeney (Imperial College, London) and Antonio Facchetti (Polyera Corp, Skokie, IL) for generously providing materials [PBTTT and P(NDI2OD-T2)]. Portions of this research were carried out at the Stanford Synchrotron Radiation Lightsource, a national user facility operated by Stanford University on behalf of the U.S. Department of Energy, Office of Basic Energy Sciences. A. S. and J.R. gratefully acknowledge financial support from the National Science Foundation. This publication was partially based on work supported by the Center for Advanced Molecular Photovoltaics (Award No. KUS-C1-015-21), made by King Abdullah University of Science and Technology (KAUST).
This publication acknowledges KAUST support, but has no KAUST affiliated authors.