Authors
Kang An, Xin Zhang, Xinyu Jiang, Xingwang Kang, Feiyue Lu, Xue Wang, Ziyu Xiong, Wei Meng, Liang Gao, Sarathlal Koyiloth Vayalil, Keyou Yan, Lei Ying, Jiaping Lin, Stephan V Roth, Ning Li
Published in
Advanced materials (Deerfield Beach, Fla.). Pages e75344. Oct 09, 2026. Epub Oct 09, 2026.
Abstract
Solvent optimization in organic solar cells (OSCs) remains largely empirical, requiring repeated device fabrication and characterization across a broad formulation space. Here, we introduce a high-throughput workflow to decipher a figure of merit (FoM) for solvent optimization of non-fullerene acceptor (NFA)-based OSCs. Using D18:L8BO as a representative system, we prepare a diverse solvent formulation via high-throughput experimentation and in situ optical characterization during film formation. Machine learning analysis unveils the initial donor-to-acceptor absorption ratio during deposition (I_D/A) as a key FoM, which enables the efficient optimization of solvents in OSCs. Comprehensive characterization identifies the photophysical and morphological properties associated with the optimized formulation. Evaluation across several polymer:Y-series NFA systems further supports I_D/A as a FoM for solvent optimization. This study establishes an experimentally accessible strategy that integrates the exploration of broad solvent formulation spaces with targeted device validation into a targeted, data-driven workflow, offering a practical route for accelerating OSC development.
PMID:
42851159
Bibliographic data and abstract were imported from PubMed on 09 Oct 2026.
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