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
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.关键词
机器学习/减反膜/光管理策略/钙钛矿太阳电池/界面修饰Key words
Machine learning/Antireflective coating/Light-management strategy/Perovskite solar cells/Interface modification分类
化学化工引用本文复制引用
梁英,张欣,沈文剑,梁桂杰,李彬,彭勇,胡润,李望南,邓羽恒,余士律,程家豪,宋嘉伟,姚俊,杨亦辰,张万雷,周文靖..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[J].物理化学学报,2025,41(9):73-82,10.基金项目
The project was supported by the National Natural Science Foundation of China(22279031,52422603),the Key Research and Development Plan of Hubei Province(2023BAB109),the Joint Foundation for Innovation and Development of Hubei Natural Science Foundation(2023AFD032,2025AFD026,and 2025AFD074),the Natural Science Foundation of Hubei Province(2023AFB041 and 2023AFA072),the Longzhong Talent Plan,the Graduate Quality Engineering Funding Project of Hubei University of Arts and Sciences(YZ3202304),the Independent Innovation Projects of the Hubei Longzhong Laboratory(2024KF-07),the open research fund of Suzhou Laboratory(SZLAB-1508-2024-TS016),and the Interdiciplinary Research Program of HUST(5003120094). 国家自然科学基金(22279031,52422603),湖北省重点研发计划(2023BAB109),湖北省自然科学基金创新发展联合基金(2023AFD032,2025AFD026,2025AFD074),湖北省自然科学基金(2023AFB041,2023AFA072),襄阳市隆中人才计划,湖北文理学院研究生质量工程建设项目(YZ3202304),湖北隆中实验室自主创新课题(2024KF-07),苏州实验室开放基金(SZLAB-1508-2024-TS016)及华中科技大学交叉研究支持计划项目(5003120094)资助项目 （22279031,52422603）
