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High-Throughput Study of Antisolvents on the Stability of Multicomponent Metal Halide Perovskites through Robotics-Based Synthesis and Machine Learning Approaches
Journal of the American Chemical Society ( IF 15.0 ) Pub Date : 2021-11-18 , DOI: 10.1021/jacs.1c10045
Kate Higgins 1 , Maxim Ziatdinov 2, 3 , Sergei V Kalinin 2 , Mahshid Ahmadi 1
Affiliation  

Antisolvent crystallization methods are frequently used to fabricate high-quality metal halide perovskite (MHP) thin films, to produce sizable single crystals, and to synthesize nanoparticles at room temperature. However, a systematic exploration of the effect of specific antisolvents on the intrinsic stability of multicomponent MHPs has yet to be demonstrated. Here, we develop a high-throughput experimental workflow that incorporates chemical robotic synthesis, automated characterization, and machine learning techniques to explore how the choice of antisolvent affects the intrinsic stability of binary MHP systems in ambient conditions over time. Different combinations of the end-members, MAPbI3, MAPbBr3, FAPbI3, FAPbBr3, CsPbI3, and CsPbBr3 (MA, methylammonium; FA+, formamidinium), are used to synthesize 15 combinatorial libraries, each with 96 unique combinations. In total, roughly 1100 different compositions are synthesized. Each library is fabricated twice by using two different antisolvents: toluene and chloroform. Once synthesized, photoluminescence spectroscopy is automatically performed every 5 min for approximately 6 h. Nonnegative matrix factorization (NMF) is then utilized to map the time- and compositional-dependent optoelectronic properties. Through the utilization of this workflow for each library, we demonstrate that the selection of antisolvent is critical to the intrinsic stability of MHPs in ambient conditions. We explore possible dynamical processes, such as halide segregation, responsible for either the stability or eventual degradation as caused by the choice of antisolvent. Overall, this high-throughput study demonstrates the vital role that antisolvents play in the synthesis of high-quality multicomponent MHP systems.

中文翻译:

通过基于机器人的合成和机器学习方法对多组分金属卤化物钙钛矿稳定性的抗溶剂高通量研究

抗溶剂结晶方法经常用于制造高质量的金属卤化物钙钛矿 (MHP) 薄膜、生产相当大的单晶以及在室温下合成纳米颗粒。然而,对特定反溶剂对多组分 MHP 内在稳定性的影响的系统探索尚未得到证实。在这里,我们开发了一个高通量实验工作流程,该工作流程结合了化学机器人合成、自动表征和机器学习技术,以探索反溶剂的选择如何影响二元 MHP 系统在环境条件下随时间的内在稳定性。末端成员的不同组合,MAPbI 3,MAPbBr 3,FAPbI 3,FAPbBr 3,CsPbI3 , 和 CsPbBr 3 (MA, 甲基铵; FA +, 甲脒), 用于合成 15 个组合库,每个组合库有 96 种独特的组合。总共合成了大约 1100 种不同的组合物。每个文库使用两种不同的反溶剂制造两次:甲苯和氯仿。合成后,光致发光光谱每 5 分钟自动执行一次,持续约 6 小时。然后利用非负矩阵分解 (NMF) 来绘制依赖于时间和成分的光电特性。通过对每个文库使用此工作流程,我们证明了抗溶剂的选择对于 MHP 在环境条件下的内在稳定性至关重要。我们探索了可能的动态过程,例如卤化物偏析,负责由选择反溶剂引起的稳定性或最终降解。总体,
更新日期:2021-12-01
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