Volume 41(2) - Summer 2025

Recent advancements in digital technologies and machine-learning algorithms have contributed significantly to the development of digital laboratories. These systems autonomously investigate materials by integrating automated experimental setups. In this study, we developed a digital laboratory that connects a sputter deposition system, an X-ray diffraction (XRD) instrument (Rigaku SmartLab), and other measurement instruments. The key features of our system include (1) modularization of each experimental instrument to enable flexible adaptation to various experiments and (2) centralized cloud storage of measurement data in a unified format, allowing for data-driven materials science using machine learning. This article also presents a case study of autonomous experimentation to maximize the X-ray diffraction peak intensity ratio of LiCoO₂ thin films.

In recent years, there have been significant improvements in AI technology, especially in neural networks. We describe profile-based phase identification using neural networks, which does not require peak search. Using cements and excipients as examples, we report that neural networks can be used to identify crystalline phases more accurately even when analysis by the conventional method is difficult.

In the eighth lecture of the Powder X-ray Diffraction Basic Course, we will describe “Crystallinity.” Crystallinity is a parameter that indicates the degree of crystallinity of a sample, such as a pharmaceutical or polymeric material, and can be estimated from a powder X-ray diffraction (PXRD) pattern. It is defined as the ratio of the crystalline phase to the total weight (crystalline phase+amorphous phase). The percent crystallinity (reported as %crystallinity) is evaluated from the difference in profiles between crystalline and amorphous phases. In this paper, we describe a method for calculating the crystallinity by decomposing peaks using a profile fitting method. This method is performed by separating diffraction peaks from the crystalline phase and haloes due to scattering from the amorphous phase, and using the integrated intensity obtained by profile fitting. This method does not require pure crystalline and amorphous materials. However, the results can be influenced by the analyst’s subjectivity, as the crystallinity varies depending on how the halo is estimated. High reproducibility values can be obtained independent of the analyst by carefully selecting the parameters that determine the background estimation and the peak shape of halo.

Regulations on crystalline silica have been becoming increasingly strict worldwide and, in Japan, it has been designated as a material subject to risk assessment. Therefore, it is essential to confirm the applicability of the regulatory cut-off value (0.1%) and to appropriately control the amount of crystalline silica in, for example, glass, ceramics, or bricks based on this threshold. As a result, the demand for quantitative analysis is increasing. However, current regulations do not specify an analytical method for determining silica content in powders.

In this technical note, we propose the use of a calibration curve method employing an X-ray diffractometer for the quantification of crystalline silica. We also compare different diffractometers and slit optics configurations, and consider the optimal approach for accurate quantification.

The limits of detection for three crystalline silica samples were all below 0.1%, the regulatory cut-off value. The quantitative results obtained using both a 0–100% calibration curve (based on a standard sample) and a 0–1% calibration curve (based on a matrix-matching method) showed good agreement with the prepared concentrations, demonstrating the effectiveness of the calibration curve method.

The recycling of rare metals (Li, Ni, Co) from used lithium-ion batteries (LIBs) is important and the demand for compositional analysis of LIB recycling materials is increasing. Currently, ICP atomic emission spectrometry (ICPAES) is widely used for the analysis of LIB recycling materials, but since it requires the use of acid and advanced processing techniques, a simpler analytical method is needed. In this report, the composition of black powder (BP) and black mass (BM), which are LIB recycling materials, was analyzed by X-ray fluorescence analysis (XRF) and the agreement with ICP-AES analysis values was confirmed. BP samples showed good agreement with ICP-AES analysis results using the balance estimation model with the scattering fundamental parameter (FP) method. For heterogeneous BM samples, oxidation treatment and fusion bead sample preparation were carried out to improve the analysis results.