AI 中文总结
本文研究将量子光学现象整合到下一代光伏系统设计中，结合光子结构、界面工程、激光计量及机器学习与密度泛函理论，助力提升太阳能电池的效率、稳定性与功能性。
AI 中文摘要
我们分析了腔量子电动力学（CQED）、法布里-珀罗共振、强光-物质耦合等量子光学现象在下一代光伏系统设计与工程中的整合方式。研究探讨了如何通过光学腔、等离子体材料、超表面等光子结构利用这些现象，以改善未来太阳能电池器件的光捕获、光吸收及载流子动力学。研究重点涉及钙钛矿、有机物、过渡金属二硫化物（TMD）、碲化镉（CdTe）、硅等半导体材料。针对钙钛矿太阳能电池，我们分析了器件架构、超支化聚合物的界面工程，以及利用分子掺杂剂和纳米片进行添加剂优化以提升薄膜形貌与稳定性。此外，我们还研究了用于薄膜表征的激光计量学，以及涉及频率梳和高次谐波产生的相干光谱技术。本文还展示了机器学习（ML）与密度泛函理论（DFT）结合如何加速下一代太阳能吸收材料的筛选与性能预测。这些进展表明，量子光电子学设计原理正在变革光伏研究，助力太阳能器件实现更高效率、稳定性与功能性。
英文摘要
We analyze the integration of quantum optical phenomena, such as cavity quantum electrodynamics (CQED), Fabry Perot resonances, and strong light-matter coupling, into the design and engineering of next generation photovoltaic systems. We examine how these phenomena can be harnessed through photonic structures including optical cavities, plasmonic materials, and metasurfaces to improve light trapping, absorption, and carrier dynamics in future solar cell devices. Specific focus is given to semiconductor materials such as perovskites, organics, transition metal dichalcogenides (TMD), cadmium telluride (CdTe), and silicon. For perovskite solar cells, we analyze device architectures, interfacial engineering with hyperbranched polymers, and additive optimization using molecular dopants and nanosheets to enhance film morphology and stability. We further examine laser-based metrology for thin-film characterization and coherent spectroscopy techniques involving frequency combs and high-harmonic generation. The paper also shows how machine learning (ML), combined with density functional theory (DFT), accelerates material screening and performance prediction for next-generation solar cell absorbers. These developments demonstrate how quantum optoelectronic design principles are transforming photovoltaic research and enabling higher efficiency, stability, and functionality in solar energy devices.
