Diels-Alder Reaction Mechanisms of La@C60 and Gd@C60 Studied Using Density Functional Theory.

Encapsulation of transition metals represents a crucial method for modifying the electronic structure and regulating the reactivity of fullerene, thereby expanding its applications. Herein, we present calculations with density functional theory methods to investigate the mechanisms of the Diels-Alder (DA) reactions of cyclopentadiene and La@C60 or Gd@C60 as well as their tricationic derivatives. Our findings indicate that the encapsulation of La and Gd into the C60 cage is thermodynamically favorable. The DA reactions are favored by the presence of La and Gd, with lower barriers, though the regioselectivity, favoring 6-6 bonds in the fullerene, is not affected. The effect of external electric fields has been also considered.

Flexible Hydrazone-Linked Metal-Covalent Organic Frameworks with Copper Clusters for Efficient Electrocatalytic Oxygen Evolution Reaction.

Despite the challenges associated with the synthesis of flexible metal-covalent organic frameworks (MCOFs), these offer the unique advantage of maximizing the atomic utilization efficiency. However, the construction of flexible MCOFs with flexible building units or linkages has rarely been reported. In this study, novel flexible MCOFs are constructed using flexible building blocks and copper clusters with hydrazone linkages. The heterometallic frameworks (Cu, Co) are prepared through the hydrazone linkage coordination method and evaluated as catalysts for the oxygen evolution reaction (OER). Owing to the spatial separation and functional cooperation of the heterometallic MCOF catalysts, the as-synthesized MCOFs exhibited outstanding catalytic activities with an overpotential of 268.8 mV at 10 mA cm-2 for the OER in 1 M KOH, which is superior to those of the reported covalent organic frameworks (COFs)-based OER catalysts. Theoretical calculations further elucidated the synergistic effect of heterometallic active sites within the linkages and frameworks, contributing to the enhanced OER activity. This study thus introduces a novel approach to the fundamental design of flexible MCOF catalysts for the OER, emphasizing their enhanced atomic utilization efficiency.

A comprehensive psychological tendency prediction model for pregnant women based on questionnaires.

More and more people are under high pressure in modern society, leading to growing mental disorders, such as antenatal depression for pregnant women. Antenatal depression can affect pregnant woman's physical and psychological health and child outcomes, and cause postpartum depression. Therefore, it is essential to detect the antenatal depression of pregnant women early. This study aims to predict pregnant women's antenatal depression and identify factors that may lead to antenatal depression. First, a questionnaire was designed, based on the daily life of pregnant women. The survey was conducted on pregnant women in a hospital, where 5666 pregnant women participated. As the collected data is unbalanced and has high dimensions, we developed a one-class classifier named Stacked Auto Encoder Support Vector Data Description (SAE-SVDD) to distinguish depressed pregnant women from normal ones. To validate the method, SAE-SVDD was firstly applied on three benchmark datasets. The results showed that SAE-SVDD was effective, with its F-scores better than other popular classifiers. For the antenatal depression problem, the F-score of SAE- SVDD was higher than 0.87, demonstrating that the questionnaire is informative and the classification method is successful. Then, by an improved Term Frequency-Inverse Document Frequency (TF-IDF) analysis, the critical factors of antenatal depression were identified as work stress, marital status, husband support, passive smoking, and alcohol consumption. With its generalizability, SAE-SVDD can be applied to analyze other questionnaires.