Transparent solar cell systems have garnered a great deal of attention as possible alternatives to silicon-based solar cells. While conventional Silicon-based solar cells absorb in limited frequency ranges, transparent solar cells absorb in both the near infrared and ultraviolet regions of the electromagnetic spectrum.
The challenge lies in improving the power conversion efficiency from the current 3.5 \%. It is therefore crucial to have a complete understanding of the electronic and topographic properties of the component materials at the nanoscale to considerably improve their performance. For instance, controlling the morphology and electronic properties of the component acceptor and donor materials will have a direct impact on power conversion efficiencies. Here, I present the use of scanning tunneling microscopy (STM) as a primary tool to analyze these materials with atomic scale resolution.
The materials used in this work are monolayer graphene grown by chemical vapor deposition (CVD) and poly(3-hexylthiophene)(P3HT) monolayer, bilayer, and additive induced thin films, which have great potential for use in transparent solar cells. This work outlines our findings in understanding and characterizing different substrate effects on graphene films, particularly useful for defect analysis and quality control. This thesis presents analyses of the important role of pre-treatment of the copper catalyst on the improvement in quality and continuity of graphene films.
Initial results also suggest substrate dependent charge transfer through acquired work-function curves and a shift in the Fermi level at the interface. With high tunneling currents and low bias voltages, the images reflect the electronic character of both graphene and the underlying copper surface, enabling
us to identify different copper facets via fast Fourier transforms.
In this thesis, we also present the morphological changes occurring in P3HT chains resulting from solvent mixing and propose an annealing free approach for efficient self-organization of chains via pi - pi interactions. We propose the use of two methods: an edge-detection based method and the worm-like chain model for quantifying the persistence length of the polymer chains.