The Effect of Methylammonium Iodide on the Supersaturation and Interfacial Energy of the Crystallization of Methylammonium Lead Triiodide Single Crystals

Li, Bichen; Isikgor, Furkan H.; Coskun, Hikmet; Ouyang, Jianyong

doi:10.1002/anie.201710234

Cited by 16 publications

(13 citation statements)

References 29 publications

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“…The accelerating dissolution of perovskite precursor salts should be expected due to the presence of complexation between PbI 2 and I − in precursor solution . However, it is reported that the size of colloids clusters in perovskite precursor solution prepared in GBL decreased with the increase of temperature due to the release of free ions into the solutions, leading to the shift of the equilibrium of the chemical reaction to the conversion of low‐order iodoplumbates of Pb 3 − and PbI 2 . Nevertheless, the precursor solution aging time seems to become a critical factor in determining the size and structure of colloid clusters in the precursor solutions.…”

Section: Colloidal Characteristics Of Perovskite Precursor Solutionsmentioning

confidence: 99%

“…Due to the chemical equilibrium between iodoplumbate coordination complexes and the competition among I − , solvent molecules, and additive to interact with Pb 2+ , the nature of perovskite precursor solution is sensitive to the temperature, components, and aging . In general, heating the precursor solution for extended duration at a mild temperature (70–180 °C) is suggested to fully dissolve the perovskite salts 73a.…”

Section: Colloidal Characteristics Of Perovskite Precursor Solutionsmentioning

confidence: 99%

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Engineering Halide Perovskite Crystals through Precursor Chemistry

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Cao

et al. 2019

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The composition, crystallinity, morphology, and trap‐state density of halide perovskite thin films critically depend on the nature of the precursor solution. A fundamental understanding of the liquid‐to‐solid transformation mechanism is thus essential to the fabrication of high‐quality thin films of halide perovskite crystals for applications such as high‐performance photovoltaics and is the topic of this Review. The roles of additives on the evolution of coordination complex species in the precursor solutions and the resulting effect on perovskite crystallization are presented. The influence of colloid characteristics, DMF/DMSO‐free solutions and the degradation of precursor solutions on the formation of perovskite crystals are also discussed. Finally, the general formation mechanism of perovskite thin films from precursor solutions is summarized and some questions for further research are provided.

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Section: Colloidal Characteristics Of Perovskite Precursor Solutionsmentioning

confidence: 99%

Section: Colloidal Characteristics Of Perovskite Precursor Solutionsmentioning

confidence: 99%

Engineering Halide Perovskite Crystals through Precursor Chemistry

Binks

Cao

et al. 2019

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108

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“…The observation clearly indicates high immiscibility between MAI and SAN. Considering the measured contact angle (∼24°), the dispersive component of the surface tension for SAN (0.036 J/m 2 ), the estimated surface tension of MAI (0.2 J/m 2 ), and Young’s equation, we find that the interfacial tension γ between SAN and MAI are estimated to be ∼0.15 J/m 2 ; this very large surface tension is then equivalent to χ >10 based on the Helfand–Tagami equation, thus confirming that MAI and SAN are highly immiscible.…”

Section: Discussionmentioning

confidence: 53%

Improving Thermal Stability of Perovskite Solar Cells by Suppressing Ion Migration Using Copolymer Grain Encapsulation

et al. 2021

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Thermal stability of organic–inorganic hybrid perovskites (OIHPs) remains as one of the critical challenges against the stable operation of perovskite solar cells (PSCs) in direct sunlight with elevated temperatures. Here, we show that the addition of a polystyrene-co-polyacrylonitrile (SAN) copolymer can significantly enhance thermal stability of OIHPs and improve the stability of the corresponding PSCs by suppressing the migration of organic cations in OHIP. The methylammonium lead iodide (MAPI) with SAN incorporated within the perovskite layer featured a superior thermal stability compared to pure MAPI without SAN, only displaying an average of 5–15% decrease in PCE even after continuous thermal aging for 24 h at 100 °C. The secondary ion mass spectrometry revealed that the thermal degradation of the pure MAPI was largely associated with MA+ out-migration. Conducting atomic force microscopy analysis further indicated that the incorporated SAN led to a suppression of ionic currents present at the grain boundaries of the perovskite film, which was understood by high immiscibility between SAN and MA+ components as confirmed by the experimentally estimated Flory–Huggins parameter between them. This study newly identifies a promising potential of using polymer grain encapsulation for enhancing thermal stability of OIHPs and their solar cell performance by suppressing the out-diffusion of cationic organic components.

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“…Single crystal perovskites are especially important as they have longer charge diffusion lengths, , higher carrier mobilities, , higher optoelectronic performance than thin film analogues, , and the atomic structures obtained via single-crystal X-ray diffraction are a starting point for first-principles simulations. Predicting perovskite crystallization is difficult because the precursor solutions are concentrated, nonideal electrolytes, − subject to reagent compositional variation, , and sensitive to potentially uncontrolled experimental conditions such as temperature and humidity . As a result, most efforts to grow novel perovskite single crystals proceed through trial and error.…”

Section: Introductionmentioning

confidence: 99%

“…Predicting perovskite crystallization is difficult because the precursor solutions are concentrated, nonideal electrolytes, 25−27 subject to reagent compositional variation, 28,29 and sensitive to potentially uncontrolled experimental conditions such as temperature and humidity. 30 As a result, most efforts to grow novel perovskite single crystals proceed through trial and error.…”

Section: ■ Introductionmentioning

confidence: 99%

Can Machines “Learn” Halide Perovskite Crystal Formation without Accurate Physicochemical Features?

et al. 2020

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Discovery of new perovskite materials is motivated by a broad range of materials applications and accelerated by recent advances in machine learning (ML). We herein report dataset augmentation, benchmarking, and interrogation for an ongoing experimental campaign consisting of 9483 halide perovskite synthesis experiments. To address limitations in previous work, we developed an improved description of the reactant concentrations in the experiments (validated against experimental observations) and performed experiments quantifying the excess volume of mixing of γ-butyrolactone/formic acid mixtures used in the perovskite syntheses. Combining this improved description of reactant concentration with other physicochemical features of the reactants, we constructed 1108 ML models to elucidate the roles of the algorithm (k-nearest neighbors, linear support-vector machine, and gradient boosted tree), feature set (12 in total), preprocessing regime (e.g., standardization), and training data holdout scheme on ML predictive ability. ML comparisons illustrated that the chemical accuracy of less sophisticated physical models in a dataset do not hinder interpolative model performance. Analysis of feature contributions showed how ML models "learn" competitive representations for concentration using raw experimental descriptions. Interrogation of the most performant models indicated that the numerical values of physicochemical features were not important, rather these features were being used to identify and interpolate within a particular reactant set. ML models were shown to be capable of making rudimentary extrapolations to untrained chemical systems when compared against basic benchmarks, and models which included the newly developed chemical features were shown to be more reliable than models trained without. These results illustrate how a stepwise comparative approach to machine learning can provide insight into what and how much models are "learning" for a given prediction task.

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The Effect of Methylammonium Iodide on the Supersaturation and Interfacial Energy of the Crystallization of Methylammonium Lead Triiodide Single Crystals

Cited by 16 publications

References 29 publications

Engineering Halide Perovskite Crystals through Precursor Chemistry

Engineering Halide Perovskite Crystals through Precursor Chemistry

Improving Thermal Stability of Perovskite Solar Cells by Suppressing Ion Migration Using Copolymer Grain Encapsulation

Can Machines “Learn” Halide Perovskite Crystal Formation without Accurate Physicochemical Features?

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