Investigating the property of water driven lead-free stable inorganic halide double perovskites

Air stable highly luminescent 2D tin halide perovskite nanocrystals as photodetectors

Hybrid and inorganic perovskite nanocrystals are a hot topic in materials chemistry due to their versatile optoelectronic properties. Herein, we report highly luminescent water stable lead-free orange emissive (OleylAm)₂SnBr₄ (OleylAm = oleylammonium cation) 2D tin halide perovskite nanocrystals in humid conditions in a solution-based process. The photoresponse study performed with the synthesized nanocrystals exhibits a responsivity of 4.9 mA W^-1 at a 5 V operating voltage.

Deciphering the Role of Water in Promoting the Optoelectronic Performance of Surface-Engineered Lead Halide Perovskite Nanocrystals

Lead halide perovskites are promising candidates for high-performance light-emitting diodes (LEDs); however, their applicability is limited by their structural instability toward moisture. Although a deliberate addition of water to the precursor solution has recently been shown to improve the crystallinity and optical properties of perovskites, the corresponding thin films still do not exhibit a near-unity quantum yield. Herein, we report that the direct addition of a minute amount of water to post-treated formamidinium lead bromide (FAPbBr₃) nanocrystals (NCs) substantially enhances the stability while achieving a 95% photoluminescence quantum yield in a NC thin film. We unveil the mechanism of how moisture assists in the formation of an additional NH₄Br component. Alongside, we demonstrate the crucial role of moisture in assisting localized etching of the perovskite crystal, facilitating the partial incorporation of NH₄⁺, which is key for improved performance under ambient conditions. Finally, as a proof-of-concept, the application of post-treated and water-treated perovskites is tested in LEDs, with the latter exhibiting a superior performance, offering opportunities toward commercial application in moisture-stable optoelectronics.

Aromatic Amino Acid-Mediated Perovskite Nanocrystals: Fluorescence Tuning and Morphological Evolution

Lead halide perovskites with high fluorescent and tunable morphology appeared at the forefront of materials chemistry because of their corresponding impressive optoelectronic properties. The current advancement of metal halide perovskites put forward the functional and bidentate ligand to expand their utilization in modified ligand chemistry. We successfully introduced nontoxic aromatic amino acid as a capping ligand to synthesize the perovskite nanocrystals (PNCs). The implementation of aromatic amino acid for the construction of CsPbX₃ nanocrystals (NCs) provides the synergetic service of the carboxylic and amine groups with the phenyl residue, which prompts the formation of NCs with high fluorescence intensity. The experimental results demonstrate the emissive property of PNCs in a whole visible region with long-term stability. Additionally, the morphology of the NCs has been tuned. We performed several characterization techniques to investigate the nature of the NCs in the solid and solution phases.

Photoelectrochemical Properties, Machine Learning, and Symbolic Regression for Molecularly Engineered Halide Perovskite Materials in Water

The machine learning techniques are capable of predicting virtual material design space and optimizing material fabrication parameters. In this article, we construct machine learning models to describe the photoelectrochemical properties of molecularly engineered halide perovskite materials based on CH₃NH₃PbI₃ in an aqueous solution and predict a complex multidimensional design space for the halide perovskite materials. The machine learning models are trained and tested based on an experimental photocurrent data set consisting of 360 data points with varying experimental conditions and dye structures. Machine learning algorithms including support vector machine (SVM), random forest, k-nearest neighbors, Rpart, Xgboost, and Kriging algorithms are compared, with the Kriging algorithm achieving the best accuracies (r = 0.99 and R² = 0.98) and SVM achieving the second best. A total of 50,905 data points representing the complex multidimensional design space are predicted via the machine-learned models to benefit the future perovskite studies. In addition, the symbolic regression based on the genetic algorithms effectively and automatically designs hybrid descriptors that outperform the individual descriptors. This article highlights the machine learning and symbolic regression methods for designing stable and high-performance halide perovskite materials and serves as a platform for further experimental optimization of halide perovskite materials.

Charge transfer excitons in unfunctionalized graphite-wrapped MAPbBr3 nanocrystal composites with different morphologies

Charge transfer from perovskite nanocrystals to graphite sheets.

Probing the emissive behaviour of the lead-free Cs2AgBiCl6 double perovskite with Cu(ii) doping

Cu ion induced change in photoluminescence behaviour of Cs2AgBiCl6 double perovskite.