Extremely Stable Zeolites Developed via Designed Liquid-Mediated Treatment.

Improving the stability of porous materials for practical applications is highly challenging. Aluminosilicate zeolites are utilized for adsorptive and catalytic applications, wherein they are sometimes exposed to high-temperature steaming conditions (∼1000 °C). As the degradation of high-silica zeolites originates from the defect sites in their frameworks, feasible defect-healing methods are highly demanded. Herein, we propose a method for healing defects to create extremely stable high-silica zeolites. High-silica (SiO2/Al2O3 > 240) zeolites with *BEA-, MFI-, and MOR-type topologies could be stabilized by significantly reducing the number of defect sites via a liquid-mediated treatment without using additional silylating agents. Upon exposure to extremely high temperature (900-1150 °C) steam, the stabilized zeolites retain their crystallinity and micropore volume, whereas the parent commercial zeolites degrade completely. The proposed self-defect-healing method provides new insights into the migration of species through porous bodies and significantly advances the practical applicability of zeolites in severe environments.

Simulated carbon K edge spectral database of organic molecules.

Here we provide a database of simulated carbon K (C-K) edge core loss spectra of 117,340 symmetrically unique sites in 22,155 molecules with no more than eight non-hydrogen atoms (C, O, N, and F). Our database contains C-K edge spectra of each carbon site and those of molecules along with their excitation energies. Our database is useful for analyzing experimental spectrum and conducting spectrum informatics on organic materials.

Automatic determination of the spectrum-structure relationship by tree structure-based unsupervised and supervised learning.

Spectroscopy is widely used for the analysis of chemical, vibrational, and bonding information. Interpretations of the spectral features have been performed by comparing the objective spectra with reference spectra from experiments or simulations. However, the interpretation process by humans is not always straightforward, especially for spectra obtained from unknown or new materials. In the present study, we developed a method using machine learning techniques to obtain human-like interpretation automatically. We combined unsupervised and supervised learning methods; then applied it to the spectrum database which includes more than 400 spectra of water and organic molecules containing various ligands and chemical bonds. The proposed method has successfully found the correlations between the spectral features and descriptors of the atoms, bonds, and ligands. We demonstrated that the proposed method enabled the automatic determination of reasonable spectrum-structure relationships such as between π* resonance in C-K edges and multiple bonds. The proposed method enables the automatic determination of physically and chemically reasonable spectrum-structure relationships without arbitrariness in data-driven manner, which is considerably difficult only with simulation or conventional machine leaning techniques. Such relationships are useful for understanding what structural parameters cause changes in the spectrum, providing a way for the better interpretation of spatial distributed or time evolutionary data. Furthermore, although the present work focused on the ELNES/XANES spectrum from small organic molecules, the proposed method can be readily extended to other spectral data. It is expected to contribute to a better understanding of the spectrum-structure relationship in various spectroscopy applications.