ABSTRACT
Polarized resonant soft X-ray scattering (P-RSoXS) has emerged as a powerful synchrotron-based tool that combines the principles of X-ray scattering and X-ray spectroscopy. P-RSoXS provides unique sensitivity to molecular orientation and chemical heterogeneity in soft materials such as polymers and biomaterials. Quantitative extraction of orientation information from P-RSoXS pattern data is challenging, however, because the scattering processes originate from sample properties that must be represented as energy-dependent three-dimensional tensors with heterogeneities at nanometre to sub-nanometre length scales. This challenge is overcome here by developing an open-source virtual instrument that uses graphical processing units (GPUs) to simulate P-RSoXS patterns from real-space material representations with nanoscale resolution. This computational framework - called CyRSoXS (https://github.com/usnistgov/cyrsoxs) - is designed to maximize GPU performance, including algorithms that minimize both communication and memory footprints. The accuracy and robustness of the approach are demonstrated by validating against an extensive set of test cases, which include both analytical solutions and numerical comparisons, demonstrating an acceleration of over three orders of magnitude relative to the current state-of-the-art P-RSoXS simulation software. Such fast simulations open up a variety of applications that were previously computationally unfeasible, including pattern fitting, co-simulation with the physical instrument for operando analytics, data exploration and decision support, data creation and integration into machine learning workflows, and utilization in multi-modal data assimilation approaches. Finally, the complexity of the computational framework is abstracted away from the end user by exposing CyRSoXS to Python using Pybind. This eliminates input/output requirements for large-scale parameter exploration and inverse design, and democratizes usage by enabling seamless integration with a Python ecosystem (https://github.com/usnistgov/nrss) that can include parametric morphology generation, simulation result reduction, comparison with experiment and data fitting approaches.
ABSTRACT
The use of polymer-polymer blends to tailor mechanical properties and improve electrical performance is becoming widespread in the field of printed electronics. Similarly, meniscus-guided coating can be used to tailor electrical properties through alignment of the semiconducting material. We report on a long-wavelength instability during blade coating of a semiconducting polymer/elastomer blend for organic transistor applications that results in significant variation of the semiconducting polymer nanofibril alignment across the instability period. By correlating measurements over diverse (nm to mm) length scales, we can directly relate the charge transport in top-gate transistors to the local polymer nanofibril alignment. Hole mobility is directly correlated to the local alignment and shows an ≈2 × variation across the instability for devices aligned with the coating direction. The potential for long-wavelength instabilities to create device-relevant morphology variations should be considered when optimizing coating conditions. These results reveal considerable potential for error in assuming that smooth films are necessarily structurally uniform; material structure may spatially vary for some coating methods, leading to a correlated, spatially varying device performance.
