节点文献
机器学习辅助减反膜结构设计与界面修饰协同优化的高效稳定钙钛矿太阳能电池
Machine learning-guided antireflection coatings architectures and interface modification for synergistically optimizing efficient and stable perovskite solar cells
【摘要】 近年来,单节钙钛矿太阳电池(PSCs)通过对功能层进行多元优化策略最小化能量损失使器件效率迅速提升,逐渐逼近肖克利-奎伊瑟(S-Q)理论效率极限。作为光管理策略的重要组成部分,减反射涂层(ARC)在降低光学能量损耗实现高效率方面发挥着关键作用。开发出具有多功能的ARC,能够同时提升可见光透过率、抑制紫外光(UV)透射,并且在玻璃基底上具有优异附着性、耐磨性是目前研发的重点。本研究利用贝叶斯优化算法的机器学习方法指导超薄多层二氧化物ARC的结构设计。优化流程包括多层氧化物薄膜的参数化建模、采用传递矩阵法(TMM)的物理模拟,以及抗反射性能评估。经过优化的ARC采用100 nm SiO2-10 nm TiO2-10 nm SiO2(STS)叠层结构,使导电玻璃基底在400–800 nm范围内的透光率提升了9.2%。该结构应用在PSCs中获得了最高96.94%的外量子效率,使短路电流密度和光电转换效率均提升了4%,紫外光照持续300 h后仍保持有初始效率的81.2%,而标样组的效率降至~69%,表明STS ARC具备有效的紫外光过滤性能。STS ARC经过国际标准测试具有超过9H的硬度和ISO 0级以及ASTM 5B级附着度,满足太阳电池户外应用需求。除光学能量损失外,钙钛矿表面缺陷态富集导致非辐射复合能量损失,同时也是晶格降解的起始位点。因此,本文采用3-脒基吡啶氢碘酸盐(3-PyADI)对界面缺陷进行钝化修饰,协同将PSCs的效率提升至24.44%,未封装的器件在大气环境下放置1000 h后保留初始效率的93%。本研究所提出的增透减反薄膜与钙钛矿界面修饰协同实现器件性能和稳定性的同步提升,为钙钛矿太阳电池的产业化发展探索出具有前景且实用的路径。
【Abstract】 In recent years, single-junction perovskite solar cells(PSCs) have experienced unprecedented development, approaching the Shockley-Queisser(S-Q) theoretical efficiency limit, due to versatile optimization strategies targeting functional layers to minimize energy loss. The antireflection coating(ARC), as part of the light-management strategy, plays a critical role in reducing optical loss to achieve higher efficiency. The development of multifunctional ARC that can simultaneously enhance visible light transmittance while suppressing ultraviolet(UV) light transmission, along with excellent adhesion and wear resistance on glass substrates, remains a significant challenge in current research.Herein, we propose ultra-thin ARC made of multilayer dioxides, SiO2-TiO2-SiO2(STS) films, optimized using a machine learning approach with a Bayesian optimization algorithm. This process involved parameterized modeling of multilayer dioxide ARC, physical simulations using the Transfer Matrix Method(TMM), and evaluation of antireflective performance. The optimal configuration of STS ARC consists of 100 nm SiO2, 10 nm TiO2, and 10 nm SiO2, increasing the transmittance of FTO glass by 9.2% in the 400–800 nm wavelength range. The ARC effectively enhances external quantum efficiency, achieving 96.94%, thereby increasing the short-circuit current density(JSC) and power conversion efficiency(PCE) by 4%.PSCs with STS ARC retain 81.2% of their initial efficiency after continuous UV illumination for 300 h, while control devices degrade to approximately 69%, demonstrating effective UV filtration and improved operational stability. This ARC exhibit hardness exceeding 9H on the pencil hardness scale and achieve ISO class 0/ASTM class 5B in adhesion tests, meeting the outdoor durability requirements for PSCs. In addition to optical energy loss, the accumulation of defects on the surface of the perovskite layer induces non-radiative recombination energy loss and serves as initiation sites for lattice degradation. To address this, we use 3-amidinopyridinium iodide(3-PyADI) to passivate interface defects, further improving the PCE to 24.44%. The stability of the device remains at 93% of the initial PCE after 1000 h under atmospheric conditions. The proposed ARC and PSCs structure are expected to enhance optoelectronic performance and environmental stability, providing a promising and practical path for the development of PSCs.
【Key words】 Machine learning; Antireflective coating; Light-management strategy; Perovskite solar cells; Interface modification;
- 【文献出处】 物理化学学报 ,Acta Physico-Chimica Sinica , 编辑部邮箱 ,2025年09期
- 【分类号】TB383.2;TM914.4;TP181
- 【下载频次】45