Mixed-dimensional self-assembly organic–inorganic perovskite microcrystals for stable and efficient photodetectors

Wang, Yunuan; Tang, Yang; Jiang, Jingzan; Zhang, Quan; Sun, Jingru; Hu, Yufeng; Cui, Qiuhong; Teng, Feng; Lou, Zhidong; Hou, Yanbing

doi:10.1039/d0tc00686f

Cited by 18 publications

(15 citation statements)

References 60 publications

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“…Perovskite materials have attracted much attention in many scientific fields for the composition diversity, easily available synthetic conditions and a variety of attractive properties 1,2 . For instance, hybrid organic-inorganic perovskite has widely applied in the fields of solar cells, light-emitting diodes, lasers, and photodetectors due to its longer charge diffusion lengths both for electrons and holes, higher carrier mobility and broad tunable bandgap (E g ) 3,4 . ABO 3 -type perovskite oxide has gradually become a research hotspot in modern industrial catalysis and thermoelectricity for the controllable structure, outstanding stability and low cost 5,6 .…”

Section: Introductionmentioning

confidence: 99%

Machine learning for perovskite materials design and discovery

et al. 2021

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The development of materials is one of the driving forces to accelerate modern scientific progress and technological innovation. Machine learning (ML) technology is rapidly developed in many fields and opening blueprints for the discovery and rational design of materials. In this review, we retrospected the latest applications of ML in assisting perovskites discovery. First, the development tendency of ML in perovskite materials publications in recent years was organized and analyzed. Second, the workflow of ML in perovskites discovery was introduced. Then the applications of ML in various properties of inorganic perovskites, hybrid organic–inorganic perovskites and double perovskites were briefly reviewed. In the end, we put forward suggestions on the future development prospects of ML in the field of perovskite materials.

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Section: Introductionmentioning

confidence: 99%

Machine learning for perovskite materials design and discovery

et al. 2021

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“…[ 1 ] The PCE of PSCs can be improved so quickly in a short time, which is inseparable from the excellent properties of the perovskite material itself (tunable bandgap, faster carrier mobility, high absorption coefficients, lower exciton binding energy, etc.). [ 2–6 ] Of course, it is even more inseparable from the optimization design of PSCs by many scholars, such as the optimization of energy‐level alignment; the development of new transport layer materials; the passivation of material defects. [ 7–9 ] However, without exception, these require a lot of time and resources for trial and error experiments.…”

Section: Introductionmentioning

confidence: 99%

How Machine Learning Predicts and Explains the Performance of Perovskite Solar Cells

et al. 2022

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Characterizing the electrical parameters of perovskite solar cells (PSCs) usually requires a lot of time to fabricate complete devices. Here, machine learning (ML) is used to reduce the device fabrication process and predict the electrical performance of PSCs. Using ML algorithms and 814 valid data cleaned from 2735 peer‐reviewed publications, ML prediction models are built for bandgap, conduction band minimum, valence band maximum of perovskites, and electrical parameters of PSCs. These prediction models have excellent accuracy, and the root mean square error of the prediction models for bandgap and power conversion efficiency (PCE) reaches 0.064 eV and 1.58%, respectively. Among the many factors that affect the performance of PSCs, those factors play a major role in the lack of comprehensive explanation. Through the prediction model of electrical parameters and Shapley Additive explanations theory, the factors affecting the PCE of PSCs are explained and analyzed. It can not only verify the objective physical laws from the perspective of ML, but also conclude that among the 13 features, the content of formamidinium/NH2CHNH2+ plays the most important role in improving the PCE of PSCs. These results show that ML has great application possibilities in the PSC field.

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“…The MCs are known to exhibit lower grain boundaries, reduced trap densities, and long charge carrier diffusion lengths compared to the nanocrystalline counterpart. [ 12 ] Additionally, MCs demonstrated enhanced environmental stability and better endurance. Although the synthesis of lead‐based halide perovskite MCs is well established using hot injection, inverse temperature crystallization and laser trapping in a precursor solution, [ 13–16 ] while the synthesis of halide DP MCs is yet to be explored.…”

Section: Introductionmentioning

confidence: 99%

Hot‐Spin Casting Synthesis of Freestanding Cs₂AgBiBr₆ Double Perovskite Facet‐Oriented Microcrystals for Efficient Photodetectors

Hossain

Sheikh

Rao

2021

Adv Materials Inter

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Facet‐orientated double perovskite (DP) microcrystals (MCs) are of particular interest in optoelectronics due to the special symmetry and homogeneous interfaces, which extend the carrier lifetime and enhance the mobility to realize robust device performance. Here, a simple hot‐spin casting method is developed to instantaneously synthesize the highly crystalline lead‐free halide DP Cs2AgBiBr6 MCs with facet‐orientation. The low temperature (130 °C) hot‐spin casting synthesis of DP MCs instigates growth only along the highly packed (111) plane attributed to heterogeneous nucleation. The Cs2AgBiBr6 DP MCs demonstrate reduced lattice parameters with enhanced light absorbance resulting in decreased bandgap (ΔEg ≈ 0.23 eV). The facet‐oriented DP MCs exhibit the enhancement in photoluminescence and carrier lifetime compared to the nanocrystalline thin‐film. Moreover, the DP MCs are successfully transferred on the desired substrate using micro‐imprint lithography keeping its original orientation intact. The DP Cs2AgBiBr6 MCs‐based photodetectors manifest broader spectral response, high detectivity (1.72 × 1011 Jones) and improved external quantum efficiency (EQE, 451%). The freestanding and facet orientated DP MCs synthesis is easily extendable to other types of halide perovskites and has the potential to fabricate high‐performance optoelectronic devices.

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Mixed-dimensional self-assembly organic–inorganic perovskite microcrystals for stable and efficient photodetectors

Abstract: Mixed-dimensional (PEA)2(MA)4Pb5Br16 perovskite microcrystals are synthesized by an anti-solvent vapor-assisted method. The photodetector based on the individual perovskite microcrystal exhibits improved performances and humidity resistance.

Cited by 18 publications

References 60 publications

Machine learning for perovskite materials design and discovery

Machine learning for perovskite materials design and discovery

How Machine Learning Predicts and Explains the Performance of Perovskite Solar Cells

Hot‐Spin Casting Synthesis of Freestanding Cs₂AgBiBr₆ Double Perovskite Facet‐Oriented Microcrystals for Efficient Photodetectors

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Mixed-dimensional self-assembly organic–inorganic perovskite microcrystals for stable and efficient photodetectors

Abstract: Mixed-dimensional (PEA)2(MA)4Pb5Br16 perovskite microcrystals are synthesized by an anti-solvent vapor-assisted method. The photodetector based on the individual perovskite microcrystal exhibits improved performances and humidity resistance.

Cited by 18 publications

References 60 publications

Machine learning for perovskite materials design and discovery

Machine learning for perovskite materials design and discovery

How Machine Learning Predicts and Explains the Performance of Perovskite Solar Cells

Hot‐Spin Casting Synthesis of Freestanding Cs2AgBiBr6 Double Perovskite Facet‐Oriented Microcrystals for Efficient Photodetectors

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Hot‐Spin Casting Synthesis of Freestanding Cs₂AgBiBr₆ Double Perovskite Facet‐Oriented Microcrystals for Efficient Photodetectors