KR-20260065516-A - METHOD AND APPARATUS FOR ANALYZING CRYSTAL STRUCTURE OF PEROVSKITE QUANTUM DOTS

Abstract

The present invention relates to perovskite quantum dots, and a method for analyzing the crystal structure of perovskite quantum dots according to the present invention may include: a step of acquiring X-ray diffraction (XRD) data for a plurality of perovskite quantum dot learning samples; a step of acquiring crystal structure distribution data including the distribution of a plurality of crystal structures of quantum dot particles included in each of the plurality of perovskite quantum dot learning samples; a step of training a Lasso regression-based machine learning model configured to predict a crystal structure from the input XRD data by using the XRD data as an input variable and the crystal structure distribution data as a target variable; and a step of determining the crystal structure of the perovskite quantum dot to be analyzed by inputting the XRD data of the perovskite quantum dot to be analyzed into the trained machine learning model.

Inventors

• 이한림

Assignees

• 명지대학교 산학협력단

Dates

Publication Date

20260508

Application Date

20251014

Priority Date

20241101

Claims (11)

1. A step of acquiring X-ray diffraction (XRD) data for a plurality of perovskite quantum dot training samples; A step of acquiring crystal structure distribution data including the distribution of a plurality of crystal structures of quantum dot particles included in each of the plurality of perovskite quantum dot learning samples; A step of training a Lasso regression-based machine learning model configured to predict a crystal structure from input XRD data by using the XRD data as an input variable and the crystal structure distribution data as a target variable; and A method for analyzing the crystal structure of a perovskite quantum dot, comprising the step of inputting the XRD data of the perovskite quantum dot to be analyzed into the learned machine learning model to determine the crystal structure of the perovskite quantum dot to be analyzed.
2. In claim 1, the crystal structure distribution data is A method for analyzing the crystal structure of a perovskite quantum dot, comprising the phase ratio of the plurality of crystal structures above.
3. In claim 1, the step of obtaining the crystal structure distribution data is, A method for analyzing the crystal structure of perovskite quantum dots, wherein the crystal structure distribution data is obtained by analyzing transmission electron microscope (TEM) images of the plurality of perovskite quantum dot learning samples.
4. In claim 1, the Lasso regression-based machine learning model is, A method for analyzing the crystal structure of a perovskite quantum dot, comprising a plurality of weights representing the correlation between the XRD data and the crystal structure distribution data.
5. In claim 1, the plurality of crystal structures are, A method for analyzing the crystal structure of perovskite quantum dots, including cubic and orthorhombic structures.
6. In claim 5, the above-mentioned learning step is, A method for analyzing the crystal structure of perovskite quantum dots, wherein a first-direction weight is assigned to the features of the XRD data related to the cubic structure, and a second-direction weight is assigned to the features of the XRD data related to the orthorhombic structure.
7. In claim 6, the step of determining the crystal structure is, A method for analyzing the crystal structure of a perovskite quantum dot, classifying the crystal structure of the perovskite quantum dot to be analyzed into the cubic structure or the orthorhombic structure according to the directionality indicated by the sum of weights calculated from the machine learning model.
8. In claim 1, the step of acquiring the XRD data is, A method for analyzing the crystal structure of perovskite quantum dots, wherein data from specific angle intervals with a relatively high noise generation rate is excluded from the training data set.
9. In claim 1, the plurality of perovskite quantum dot learning samples are, A method for analyzing the crystal structure of perovskite quantum dots, which are generated to have different phase ratios for the plurality of crystal structures mentioned above.
10. A method for analyzing the crystal structure of a perovskite quantum dot, further comprising, in claim 1, a step of performing at least one preprocessing of Z-score normalization or non-negative matrix factorization (NMF) on the XRD data prior to the learning step.
11. A data acquisition unit for acquiring X-ray diffraction (XRD) data; Memory; and A processor configured to execute instructions stored in the memory; comprising The above memory is, Based on the XRD data and associated crystal structure distribution data for multiple perovskite quantum dot training samples, a pre-trained Lasso regression-based machine learning model is stored, and The above processor is, A crystal structure analysis device for perovskite quantum dots, which inputs the XRD data of a perovskite quantum dot sample to be analyzed, obtained from the data acquisition unit, into the pre-trained machine learning model to determine the crystal structure of the perovskite quantum dot sample to be analyzed.

Description

Method and apparatus for analyzing the crystal structure of perovskite quantum dots The present invention relates to perovskite quantum dots, and more specifically, the present invention relates to a method and apparatus for accurately analyzing the phase ratio of a perovskite quantum dot sample in which a plurality of crystal structures are mixed. Perovskite quantum dots (QDs) have attracted significant attention in recent years due to their excellent optical properties, such as high photoluminescence quantum yield (PLQY), excellent color purity, tunable emission wavelength, and low synthesis cost. Due to these advantages, their potential applications are being explored in various fields, including solar cells, lighting, bioimaging, and secondary batteries. However, the biggest obstacle to the commercialization of perovskite quantum dots is their low chemical and operational stability. This stability is known to be closely related to the crystal structure of the quantum dots. In particular, perovskite quantum dots such as C s PbBr 3 can exist in multiple crystal structures, including cubic and orthorhombic structures, and generally, the cubic structure exhibits higher optical stability compared to the orthorhombic structure. The problem is that cubic and orthorhombic structures are crystallographically very similar, resulting in nearly identical X-ray diffraction (XRD) patterns. Furthermore, nanoscale particles such as quantum dots exhibit XRD peak broadening due to the 'size effect,' making it very difficult to clearly distinguish between the two structures using conventional analytical methods. Therefore, to maximize the performance of perovskite quantum dots, technology capable of accurately and reliably analyzing the phase ratios of coexisting crystal structures is required. FIG. 1 is a flowchart of a method for analyzing the crystal structure of perovskite quantum dots according to one embodiment of the present invention. FIG. 2 is a block diagram of a crystal structure analysis apparatus for perovskite quantum dots according to one embodiment of the present invention. FIG. 3 is a diagram illustrating a Lasso regression-based machine learning model according to one embodiment of the present invention. FIGS. 4(a) to 4(e) are drawings for explaining the characteristics of NH2 core-shell perovskite quantum dot samples synthesized under the same reaction conditions according to one embodiment of the present invention. FIGS. 5(a) to 5(d) are TEM images of NH2 core-shell perovskite quantum dot samples having a cubic structure and an orthorhombic structure according to one embodiment of the present invention, and graphs showing the correlation between the aspect ratio and the crystal structure. FIGS. 6(a) to 6(f) are histograms showing the size distribution and aspect ratio distribution of a plurality of perovskite quantum dot samples produced under different synthesis conditions according to one embodiment of the present invention. FIGS. 7(a) to 7(f) are drawings showing TEM images and size distributions of NH2 core-shell perovskite quantum dot samples according to one embodiment of the present invention. FIGS. 8(a) to 8(d) are drawings for explaining the characteristics of -O- core-shell perovskite quantum dot samples synthesized at various ligand concentrations according to one embodiment of the present invention. FIGS. 9(a) and 9(b) are diagrams showing the temperature versus time curve and phase ratio of -O- core-shell perovskite quantum dot samples according to one embodiment of the present invention. FIGS. 10(a) to 10(f) are drawings showing TEM images and size distributions of -O- core-shell perovskite quantum dot samples according to one embodiment of the present invention. FIGS. 11(a) to 11(c) are drawings illustrating the results of lasso regression analysis on XRD data of -O- core-shell perovskite quantum dot samples synthesized at various ligand concentrations according to one embodiment of the present invention. FIGS. 12(a) to 12(c) are graphs showing changes in phase ratio according to various synthesis conditions according to one embodiment of the present invention. FIGS. 13(a) and 13(b) are drawings showing TEM images of perovskite quantum dots produced under specific ligand conditions according to one embodiment of the present invention. FIGS. 14(a) and 14(b) are graphs showing the XRD patterns and photoluminescence (PL) stability over time of a cubic-dominant sample and an orthorhombic-dominant sample according to one embodiment of the present invention. FIGS. 15(a) to 15(g) are drawings showing TEM images and size distributions of -O- core-shell perovskite quantum dot samples synthesized under various conditions according to one embodiment of the present invention. Specific structural or functional descriptions regarding embodiments according to the concept of the present invention disclosed in this specification or application are provided merely for the purpose of explaining embodiments