Device-Level Performance Enhancement of Perovskite Solar Cells Using Emerging ۲D Materials: A Critical Review of Electronic Interface Engineering and Operational Stability

Perovskite solar cells (PSCs) have achieved power conversion efficiencies exceeding ۲۶%, approaching the Shockley-Queisser limit for single-junction devices. However, their commercial viability remains critically hindered by device-level instability manifested as fill factor degradation, open-circuit voltage loss, and series resistance increase under operational stress. This review provides a critical analysis of emerging two-dimensional materials(MXenes, Borophene, black phosphorus (Phosphorene), and Antimonene) from an electronic device perspective, focusing on their role in modulating key photovoltaic parameters through interface engineering. We systematically elucidate how these materials influence device physics: MXenes with tunable work function modify the built-in potential and reduce series resistance, achieving over ۲۵.۷% PCE with fill factors exceeding ۸۳%. Ordered ۲D/۳D heterojunctions demonstrate >۹۹% initial efficiency retention after ۱۵۰۰ hours by suppressing non-radiative recombination and maintaining stable diode characteristics. We provide quantitative analysis of recombination resistance (Rec), ideality factor, and shunt resistance improvements achieved with different ۲D materials. Simulation studies using SCAPS-۱D predict that optimized MXene-based devices could reach ۲۷.۸% efficiency through enhanced carrier extraction and reduced interface recombination. Persistent challenges include device-to-device reproducibility, large-area uniformity of electronic parameters, and standardized stability protocols. We outline a roadmap integrating device simulation, machine learning for parameter optimization, and pathways toward industrial-scale manufacturing, offering an electronic device perspective for unlocking the full potential of PSCs.

کلیدواژه ها:

Perovskite solar cells ، ۲D materials ، interface recombination ، SCAPS simulation ، machine learning