Ytterbium-Doped Lead–Halide Perovskite Nanocrystals: Synthesis, Near-Infrared Emission, and Open-Source Machine Learning Model for Prediction of Optical Properties

Abstract

Lead–halide perovskite nanocrystals are an attractive class of materials since they can be easily fabricated, their optical properties can be tuned all over the visible spectral range, and they possess high emission quantum yields and narrow photoluminescence linewidths. Doping perovskites with lanthanides is one of the ways to widen the spectral range of their emission, making them attractive for further applications. Herein, we summarize the recent progress in the synthesis of ytterbium-doped perovskite nanocrystals in terms of the varying synthesis parameters such as temperature, ligand molar ratio, ytterbium precursor type, and dopant content. We further consider the dependence of morphology (size and ytterbium content) and optical parameters (photoluminescence quantum yield in visible and near-infrared spectral ranges) on the synthesis parameters. The developed open-source code approximates those dependencies as multiple-parameter linear regression and allows us to estimate the value of the photoluminescence quantum yield from the parameters of the perovskite synthesis. Further use and promotion of an open-source database will expand the possibilities of the developed code to predict the synthesis protocols for doped perovskite nanocrystals.

Ligand-free template-assisted synthesis of stable perovskite nanocrystals with near-unity photoluminescence quantum yield within the pores of vaterite spheres

“…Moreover, the doping of PNCs with lanthanides results in a higher chance of oxidation of Pb 0 defects to Pb 2+ , thereby increasing perovskite stability and emission efficiency. 53 For this reason, to investigate the influence of the Yb 3+ ions on the PLQY of fabricated CsPbBr 3 PNCs, different amounts of Yb 3+ (0-6 mol%) were added during the synthesis (Table S4 † and Fig. 4).…”

Expanding the Horizons of Machine Learning in Nanomaterials to Chiral Nanostructures

“…Those models include multipower regression, RF, K-nearest neighbor, support vector (SV), and principal component analysis (PCA) together with NN, etc. [83,[87][88][89] As an example, Chen et al employed a RF model to quantitatively analyze the Cu level in carbon black particles. [90] The authors used laser-induced breakdown spectra which were used in modeling with and without pre-treatment with variable selection methods.…”

B‐Site Doping of Metal Halide Perovskite Nanoplatelets Influences Their Optical Properties

“…Obvious difficulties in determination of the low levels and changes in dopant amount, and various notations used for the percentage of doping (overall percentage, percentage to B site, percentage to Pb atoms) complicate the analysis of experimental results. Generalization of larger amount of experimental data with the help of machine learning algorithms, [133][134][135] is likely to provide further progress in this direction.…”