A novel graph-based formulation for characterizing morphology with   application to organic solar cells
  Organic solar cells have the potential for widespread usage due to their promise of low cost, roll-to-roll manufacturability, and mechanical flexibility. However, deployment is impeded by their relatively low power conversion efficiencies. The last decade has seen significant progress in enhancing the power conversion of these devices through various strategies. One such approach is based on morphology control. This is because morphology affects all phenomena involved in solar conversion: light absorption and electron-hole pair (exciton) generation; exciton diffusion and dissociation into free charges; and transport of charges to the electrodes. Progress in experimental characterization and computational modeling now allow reconstruction and imaging of the thin film morphology. This opens up the possibility of rationally linking fabrication with morphology, as well as morphology with performance. In this context, a comprehensive set of computational tools to rapidly quantify and classify the heterogeneous internal structure of thin films will be invaluable in linking process, structure and property. We present a novel graph-based framework to efficiently construct a broad suite of physically meaningful morphology descriptors. These morphology descriptors are further classified according to the physical subprocesses within an organic solar cells. The approach is motivated by the equivalence between a discretized morphology and a labeled, weighted, undirected graph. We utilize this approach to pose key questions related to structure characterization. We subsequently construct estimates and upper bounds of various efficiencies. The approach is showcased by characterizing the effect of thermal annealing on time-evolution of a thin film morphology. We conclude by formulating natural extensions to characterize crystallinity and anisotropy of the morphology using the framework. 
Olga Wodo and Srikanta Tirthapura and Sumit Chaudhary and Baskar   Ganapathysubramanian
cond-mat.mtrl-sci