Understanding Surface Wetting and Vapor Adsorption Induced Degradation Pathways of Organic-Inorganic Hybrid Perovskites through Predictive Atomistic Simulations

通过预测原子模拟了解有机-无机杂化钙钛矿的表面润湿和蒸汽吸附诱导的降解途径

• 批准号: 1708968
• 负责人: William Oates
• 金额: $ 22.8万
• 依托单位: Florida State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-08-01 至 2020-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1708968&HistoricalAwards=false
• 关键词: Understanding; Surface; Wetting; Vapor; Adsorption

Hybrid perovskites, a class of materials that have organic and inorganic components, have emerged as promising light absorbers for photovoltaic cells and emitters for light-emitting diodes. These new materials feature the integration of useful organic and inorganic material characteristics, thereby enabling unique electronic, magnetic, and/or optical properties. The surface properties of hybrid perovskites are key for practical applications yet have been largely unexplored. The instability of hybrid perovskites also remains a technological bottleneck for their commercialization. This research project involves computational and data-enabled research and aims to understand the surface stability and interfacial, or surface-to-surface, compatibility of hybrid perovskites with water and vapor under different ambient humidity levels. Such understanding will further enable the design and use of stable hybrid perovskites systems for practical applications in solar energy harvesting and energy-efficient lighting. This project supports the training and education of undergraduate and graduate students as well as broader educational efforts for the research community. The researchers on this project are developing an Integrated Computational Materials Engineering-related curriculum for the new Materials Science & Engineering program at Florida State University (FSU). They are also hosting the FSU Young Scholars Program to encourage Florida high school students to pursue careers in STEM fields.This project involves theoretical and computational research to shed light on experimental observations and to predict materials properties at the hybrid perovskite-water interface that are difficult to access experimentally. Density functional theory-based quantum mechanical simulations have been used extensively to model hybrid perovskites. However, the time scales associated with dynamic processes are comparable to, or much longer than, the typical 100 picosecond time scale accessible using ab initio molecular dynamics simulations. In addition, the length scales that can be modeled using quantum mechanical simulations is still limited to a few nm, which does not allow for direct prediction of the formation of material structures that are larger than 10 nanometers, such as polycrystalline grain-boundaries. Therefore, quantum mechanics-informed classical molecular dynamics simulations would be an ideal technique to bridge this gap. The development of a predictive molecular dynamics force field to capture the structural, surface, and interfacial properties of hybrid perovskites inevitably involves parameterization based on reproducing relevant experimental data or quantum mechanical information. To evaluate the fidelity and sensitivity of the developed force-field parameters, uncertainty quantification methods, such as Bayesian statistics, are being used, and Markov chain Monte Carlo sampling of the various force-field parameters against target material properties are being conducted. The synergistic combination of potential of mean force calculations with surface wetting, vapor adsorption, and reaction kinetic theories can lead to accurate perovskite lifetime predictions. The development of the new force fields will be a major contribution to the field, because the new force fields can then be transferrable to model the thermal, ionic transport, and interfacial properties of single and polycrystalline perovskites. With this fundamental understanding in hand, are designing chemically-stable hybrid perovskites passivated by water- and vapor-resistive ligands.

杂化钙钛矿是一类既有有机成分又有无机成分的材料，已成为光伏电池的光吸收材料和发光二极管的发射器。这些新材料的特点是集成了有用的有机和无机材料特性，从而实现了独特的电子、磁性和/或光学性能。杂化钙钛矿的表面性质是实际应用的关键，但在很大程度上还没有被探索。杂化钙钛矿的不稳定性也是其商业化的技术瓶颈。这项研究项目涉及计算和数据启用的研究，旨在了解不同环境湿度水平下杂化钙钛矿与水和水蒸气的表面稳定性和界面或表面对表面的兼容性。这样的理解将进一步使设计和使用稳定的混合钙钛矿系统用于太阳能收集和节能照明的实际应用。该项目支持对本科生和研究生的培训和教育，以及对研究界的更广泛的教育努力。该项目的研究人员正在为佛罗里达州立大学(FSU)的新材料科学与工程项目开发一门与综合计算材料工程相关的课程。他们还主办了FSU青年学者计划，以鼓励佛罗里达州的高中生在STEM领域追求职业生涯。该项目涉及理论和计算研究，以阐明实验观察，并预测钙钛矿-水混合界面上的材料性质，这些材料很难通过实验获得。基于密度泛函理论的量子力学模拟已被广泛用于模拟杂化钙钛矿。然而，与动态过程相关的时间尺度与使用从头算分子动力学模拟可获得的典型的100皮秒时间尺度相当，或远远长于该时间尺度。此外，可以使用量子力学模拟来模拟的长度尺度仍然被限制在几纳米，这不允许直接预测大于10纳米的材料结构的形成，例如多晶晶界。因此，量子力学的经典分子动力学模拟将是弥合这一差距的理想技术。预测分子动力学力场的发展，以捕捉杂化钙钛矿的结构、表面和界面性质，不可避免地涉及到基于再现相关实验数据或量子力学信息的参数化。为了评估所开发的力场参数的保真度和敏感性，正在使用贝叶斯统计等不确定性量化方法，并正在对各种力场参数与目标材料属性进行马尔可夫链蒙特卡罗抽样。平均作用力计算势与表面润湿、蒸汽吸附和反应动力学理论的协同结合可以导致准确的钙钛矿寿命预测。新力场的发展将是对场的主要贡献，因为新力场可以被转移来模拟单晶和多晶钙钛矿的热、离子输运和界面性质。在掌握了这一基本知识的基础上，正在设计化学稳定的杂化钙钛矿，这些杂化钙钛矿由耐水和抗蒸汽的配体钝化。

项目成果

Molecular Insights into Early Nuclei and Interfacial Mismatch during Vapor Deposition of Hybrid Perovskites on Titanium Dioxide Substrate

• DOI: 10.1021/acs.cgd.7b00626
• 发表时间: 2017-11
• 期刊: Crystal Growth & Design
• 影响因子: 3.8
• 作者: Jingfan Wang;Lingling Zhao;Mingchao Wang;Shangchao Lin

Suppressed phase separation of mixed-halide perovskites confined in endotaxial matrices

• DOI: 10.1038/s41467-019-08610-6
• 发表时间: 2019-02
• 期刊: Nature Communications
• 影响因子: 16.6
• 作者: Xi Wang;Yichuan Ling;X. Lian;Y. Xin;Kamal B. Dhungana;Fernando Perez-Orive;Javon M. Knox