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Real-time probing of the interplay between spinodal decomposition and crystallization during morphological evolution in printed organic solar cells

2025/07/03 by Jinsheng Zhang, Lin Xie, Zerui Li +12 · 1 voice
Engineering · #Organic Electronics and Photovoltaics #Thin-Film Transistor Technologies #Silicon and Solar Cell Technologies

paper · doi:10.1016/j.nanoen.2025.111301

openalex publication_date 2025/07/03 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01

Abstract

The performance of organic solar cells (OSCs) strongly depends on the phase separation and crystalline properties within the active layer. However, the lack of deep understanding of morphological evolution, particularly regarding spinodal decomposition and crystallization mechanisms, presents substantial challenges in achieving precise morphological control. In this work, we systematically investigate the film formation of PBDB-TF-TTz: BTP-4F-24 blends during slot-die coating while comparing o-xylene and chlorobenzene (CB) as solvents to create distinct polymer/solvent/non-solvent systems. The complex interplay between the spinodal decomposition and crystallization processes is elucidated through complementary in situ grazing incidence small-angle X-ray scattering (GISAXS) and in situ grazing incidence wide-angle X-ray scattering (GIWAXS) together with the calculation of spinodal curves. Our findings indicate that CB-processed active layers generate larger initial clusters, promoting domain coarsening while suppressing crystallization. In contrast, o-xylene-processed films exhibit optimized phase separation, larger crystallites, and face-on molecular orientations, enhancing charge transport. Additionally, polymer-dominated thermodynamic and kinetic evolution plays a critical role in shaping out the final morphology. Consequently, OSCs fabricated with o-xylene achieve higher power conversion efficiency than those processed with CB. These insights enrich the understanding of morphological evolution and provide valuable guidelines for morphology optimization.

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