Organic–Inorganic Halide Perovskite Formation: In Situ Dissociation of Cation Halide and Metal Halide Complexes during Crystal Formation

2017 ◽  
Vol 121 (25) ◽  
pp. 13532-13538 ◽  
Author(s):  
Farzaneh Arabpour Roghabadi ◽  
Vahid Ahmadi ◽  
Karim Oniy Aghmiuni
Nano Letters ◽  
2016 ◽  
Vol 16 (11) ◽  
pp. 7013-7018 ◽  
Author(s):  
Quentin Jeangros ◽  
Martial Duchamp ◽  
Jérémie Werner ◽  
Maximilian Kruth ◽  
Rafal E. Dunin-Borkowski ◽  
...  

2019 ◽  
Author(s):  
Zhi Li ◽  
Mansoor Ani Najeeb ◽  
Liana Alves ◽  
Alyssa Sherman ◽  
Peter Cruz Parrilla ◽  
...  

Metal halide perovskites are a promising class of materials for next-generation photovoltaic and optoelectronic devices. The discovery and full characterization of new perovskite-derived materials are limited by the difficulty of growing high quality crystals needed for single-crystal X-ray diffraction studies. We present the first automated, high-throughput approach for metal halide perovskite single crystal discovery based on inverse temperature crystallization (ITC) as a means to rapidly identify and optimize synthesis conditions for the formation of high quality single crystals. Using this automated approach, a total of 1928 metal halide perovskite synthesis reactions were conducted using six organic ammonium cations (methylammonium, ethylammonium, n-butylammonium, formamidinium, guanidinium, and acetamidinium), increasing the number of metal halide perovskite materials accessible by ITC syntheses by three and resulting in the formation of a new phase, [C<sub>2</sub>H<sub>7</sub>N<sub>2</sub>][PbI<sub>3</sub>]. This comprehensive dataset allows for a statistical quantification of the total experimental space and of the likelihood of large single crystal formation. Moreover, this dataset enables the construction and evaluation of machine learning models for predicting crystal formation conditions. This work is a proof-of-concept that combining high throughput experimentation and machine learning accelerates and enhances the study of metal halide perovskite crystallization. This approach is designed to be generalizable to different synthetic routes for the acceleration of materials discovery.


2019 ◽  
Vol 30 (6) ◽  
pp. 1908337 ◽  
Author(s):  
Tze‐Bin Song ◽  
Zhenghao Yuan ◽  
Megumi Mori ◽  
Faizan Motiwala ◽  
Gideon Segev ◽  
...  

Author(s):  
Zhi Li ◽  
Mansoor Ani Najeeb ◽  
Liana Alves ◽  
Alyssa Sherman ◽  
Peter Cruz Parrilla ◽  
...  

Metal halide perovskites are a promising class of materials for next-generation photovoltaic and optoelectronic devices. The discovery and full characterization of new perovskite-derived materials are limited by the difficulty of growing high quality crystals needed for single-crystal X-ray diffraction studies. We present the first automated, high-throughput approach for metal halide perovskite single crystal discovery based on inverse temperature crystallization (ITC) as a means to rapidly identify and optimize synthesis conditions for the formation of high quality single crystals. Using this automated approach, a total of 1928 metal halide perovskite synthesis reactions were conducted using six organic ammonium cations (methylammonium, ethylammonium, n-butylammonium, formamidinium, guanidinium, and acetamidinium), increasing the number of metal halide perovskite materials accessible by ITC syntheses by three and resulting in the formation of a new phase, [C<sub>2</sub>H<sub>7</sub>N<sub>2</sub>][PbI<sub>3</sub>]. This comprehensive dataset allows for a statistical quantification of the total experimental space and of the likelihood of large single crystal formation. Moreover, this dataset enables the construction and evaluation of machine learning models for predicting crystal formation conditions. This work is a proof-of-concept that combining high throughput experimentation and machine learning accelerates and enhances the study of metal halide perovskite crystallization. This approach is designed to be generalizable to different synthetic routes for the acceleration of materials discovery.


2018 ◽  
Vol 30 (11) ◽  
pp. 1706401 ◽  
Author(s):  
Ke Meng ◽  
Longlong Wu ◽  
Zhou Liu ◽  
Xiao Wang ◽  
Qiaofei Xu ◽  
...  

ACS Nano ◽  
2017 ◽  
Vol 11 (4) ◽  
pp. 3957-3964 ◽  
Author(s):  
Lianfeng Zhao ◽  
Yao-Wen Yeh ◽  
Nhu L. Tran ◽  
Fan Wu ◽  
Zhengguo Xiao ◽  
...  

CrystEngComm ◽  
2014 ◽  
Vol 16 (31) ◽  
pp. 7320 ◽  
Author(s):  
Isurika R. Fernando ◽  
Yirong Mo ◽  
Gellert Mezei

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