Flavonoids from Lycium barbarum Leaves Exhibit Anti-Aging Effects through the Redox-Modulation

Lycium barbarum leaves are a kind of vegetable, and modern nutrition studies have found that they have an anti-aging function. Our study aims to investigate the anti-aging effects of Lycium barbarum leaf flavonoid (LBLF) extracts and its underlying molecular mechanism. LBLFs were purified using D101 and polyamide resin, characterized by ultraperformance liquid chromatography coupled with mass spectrometry, and administered to hydrogen peroxide (H₂O₂)-treated human umbilical vein endothelial cells (HUVECs) and Caenorhabditis elegans. Appropriate enrichment conditions were optimized through dynamic adsorption and desorption experiments, the content of flavonoids reached 909.84 mg/g, rutin and kaempferol being the main ones. LBLFs attenuated H₂O₂-induced HUVEC apoptosis, decreased reactive oxygen species and malondialdehyde production levels, increased superoxide dismutase, glutathione peroxidase and catalase activities. Furthermore, pre-treatment with LBLF increased mRNA expression of erythropoietin (EPO) and heme oxygenase-1 (HO-1) via the mitogen-activated protein kinase (MAPK) signaling pathway in HUVECs. Compared with 100 µM rutin monomer, LBLF prolonged the lifespan of Caenorhabditis elegans, enhanced their mobility in middle life stages and upregulated expression of sod-2, gcs-1 and skn-1 genes, which indicated that the anti-aging effects of LBLF were due to its redox-modulation.     

Syntheses,Structures and Photocurrent Response Properties of Two Crystals Based on Tetrathiafulvalene Derivatives

In this paper, two compounds [Zn_2~(2+)(2,6-bis(4'-pyridyl)-TTF)(TPA)_2~(2-)](1) and [Cd~(2+)(2,6(7)-bis(4'-pyridyl)-TTF)(TPA)~(2-)(H_2O)_2](TTF = tetrathiafulvalene, TPA = terephthalic acid)(2) were synthesized by using solvothermal method and characterized by single-crystal X-ray. The purity of the two compounds was confirmed by their PXRD data. We also tested the photocurrent responses of these two compounds, and found they could generate photocurrent signal when exposed to light, but the photocurrent intensity of compound 2 is significantly greater than that of 1. From the crystal structure analysis, the possible reason for this phenomenon is that 2 has a more compact ligand arrangement than 1, leading to a higher carrier density and easier excitation.     

Pyrene分子(小片石墨晶体)的电子传导特性

利用量子理论中基于Green函数的tight-binding方法,对pyrene分子的电子传导和电子流分布进行了理论研究。在考虑到界面耦合和Hopping积分的情况下,得出了电子透射谱和流分布的模拟结果。结果显示透射与电子的能量紧密相关;谱的振荡特征是能级量子化的结果;流分布有着特定的方向,并且在每一个原子点上符合Kirchhoff量子流守恒定律。另外还发现了桥接pyrene分子的正负能开关特性。     

High-performance potassium poly(heptazine imide) films for photoelectrochemical water splitting

Photoelectrochemical (PEC) water splitting is an appealing approach by which to convert solar energy into hydrogen fuel. Polymeric semiconductors have recently attracted intense interest of many scientists for PEC water splitting. The crystallinity of polymer films is regarded as the main factor that determines the conversion efficiency. Herein, potassium poly(heptazine) imide (K-PHI) films with improved crystallinity were in situ prepared on a conductive substrate as a photoanode for solar-driven water splitting. A remarkable photocurrent density of ca. 0.80 mA cm⁻² was achieved under air mass 1.5 global illumination without the use of any sacrificial agent, a performance that is ca. 20 times higher than that of the photoanode in an amorphous state, and higher than those of other related polymeric photoanodes. The boosted performance can be attributed to improved charge transfer, which has been investigated using steady state and operando approaches. This work elucidates the pivotal importance of the crystallinity of conjugated polymer semiconductors for PEC water splitting and other advanced photocatalytic applications.

Potassium poly(heptazine imide) photoanode is synthesized, and owing to the improved crystallinity, it has presented a remarkable performance for solar-driven water splitting.     

Rapid Identification for the Pterocarpus Bracelet by Three-Step Infrared Spectrum Method

In order to explore a rapid identification method for the anti-counterfeit of commercial high value collections, a three-step infrared spectrum method was used for the pterocarpus collection identification to confirm whether a commercial pterocarpus bracelet (PB) was made from the precious species of Pterocarpus santalinus (P. santalinus). In the first step, undertaken by Fourier transform infrared spectroscopy (FTIR) spectrum, the absorption peaks intensity of PB was slightly higher than that of P. santalinus only at 1594 cm⁻¹, 1205 cm⁻¹, 1155 cm⁻¹ and 836 cm⁻¹. In the next step of second derivative IR spectra (SDIR), the FTIR features of the tested samples were further amplified, and the peaks at 1600 cm⁻¹, 1171 cm⁻¹ and 1152 cm⁻¹ become clearly defined in PB. Finally, by means of two-dimensional correlation infrared (2DIR) spectrum, it revealed that the response of holocellulose to thermal perturbation was stronger in P. santalinus than that in PB mainly at 977 cm⁻¹, 1008 cm⁻¹, 1100 cm⁻¹, 1057 cm⁻¹, 1190 cm⁻¹ and 1214 cm⁻¹, while the aromatic functional groups of PB were much more sensitive to the thermal perturbation than those of P. santalinus mainly at 1456 cm⁻¹, 1467 cm⁻¹, 1518 cm⁻¹, 1558 cm⁻¹, 1576 cm⁻¹ and 1605 cm⁻¹. In addition, fluorescence microscopy was used to verify the effectiveness of the above method for wood identification and the results showed good consistency. This study demonstrated that the three-step IR method could provide a rapid and effective way for the anti-counterfeit of pterocarpus collections.     

EGFAFS: A Novel Feature Selection Algorithm Based on Explosion Gravitation Field Algorithm

Feature selection (FS) is a vital step in data mining and machine learning, especially for analyzing the data in high-dimensional feature space. Gene expression data usually consist of a few samples characterized by high-dimensional feature space. As a result, they are not suitable to be processed by simple methods, such as the filter-based method. In this study, we propose a novel feature selection algorithm based on the Explosion Gravitation Field Algorithm, called EGFAFS. To reduce the dimensions of the feature space to acceptable dimensions, we constructed a recommended feature pool by a series of Random Forests based on the Gini index. Furthermore, by paying more attention to the features in the recommended feature pool, we can find the best subset more efficiently. To verify the performance of EGFAFS for FS, we tested EGFAFS on eight gene expression datasets compared with four heuristic-based FS methods (GA, PSO, SA, and DE) and four other FS methods (Boruta, HSICLasso, DNN-FS, and EGSG). The results show that EGFAFS has better performance for FS on gene expression data in terms of evaluation metrics, having more than the other eight FS algorithms. The genes selected by EGFAGS play an essential role in the differential co-expression network and some biological functions further demonstrate the success of EGFAFS for solving FS problems on gene expression data.     

A Novel Method for Pattern Recognition of GIS Partial Discharge via Multi-Information Ensemble Learning

Partial discharge (PD) is the main feature that effectively reflects the internal insulation defects of gas-insulated switchgear (GIS). It is of great significance to diagnose the types of insulation faults by recognizing PD to ensure the normal operation of GIS. However, the traditional diagnosis method based on single feature information analysis has a low recognition accuracy of PD, and there are great differences in the diagnosis effect of various insulation defects. To make the most of the rich insulation state information contained in PD, we propose a novel multi-information ensemble learning for PD pattern recognition. First, the ultra-high frequency and ultrasonic data of PD under four typical defects of GIS are obtained through experiment. Then the deep residual convolution neural network is used to automatically extract discriminative features. Finally, multi-information ensemble learning is used to classify PD types at the decision level, which can complement the shortcomings of the independent recognition of the two types of feature information and has higher accuracy and reliability. Experiments show that the accuracy of the proposed method can reach 97.500%, which greatly improves the diagnosis accuracy of various insulation defects.     

Facile Fabrication of 1-Methylimidazole/Cu Nanozyme with Enhanced Laccase Activity for Fast Degradation and Sensitive Detection of Phenol Compounds

Facile construction of functional nanomaterials with laccase-like activity is important in sustainable chemistry since laccase is featured as an efficient and promising catalyst especially for phenolic degradation but still has the challenges of high cost, low activity, poor stability and unsatisfied recyclability. In this paper, we report a simple method to synthesize nanozymes with enhanced laccase-like activity by the self-assembly of copper ions with various imidazole derivatives. In the case of 1-methylimidazole as the ligand, the as-synthesized nanozyme (denoted as Cu-MIM) has the highest yield and best activity among the nanozymes prepared. Compared to laccase, the K_m of Cu-MIM nanozyme to phenol is much lower, and the v_max is 6.8 times higher. In addition, Cu-MIM maintains excellent stability in a variety of harsh environments, such as high pH, high temperature, high salt concentration, organic solvents and long-term storage. Based on the Cu-MIM nanozyme, we established a method for quantitatively detecting phenol concentration through a smartphone, which is believed to have important applications in environmental protection, pollutant detection and other fields.     

Precise electro-reduction of alkyl halides for radical defluorinative alkylation

Reported here is a precise electro-reduction strategy for radical defluorinative alkylation towards the synthesis of gem-difluoroalkenes from α-trifluoromethylstyrenes. According to the redox-potential difference of the radical precursors, direct or indirect electrolysis is respectively adopted to realize the precise reduction. An easy-to-handle, catalyst-and metal-free condition is developed for the reduction of alkyl radical precursors that are generally easier to be reduced than α-trifluorome...     

Preparation of Silica Aerogel/Resin Composites and Their Application in Dental Restorative Materials

As the most advanced aerogel material, silica aerogel has had transformative industrial impacts. However, the use of silica aerogel is currently limited to the field of thermal insulation materials, so it is urgent to expand its application into other fields. In this work, silica aerogel/resin composites were successfully prepared by combining silica aerogel with a resin matrix for dental restoration. The applications of this material in the field of dental restoration, as well as its performance, are discussed in depth. It was demonstrated that, when the ratio of the resin matrix Bis-GMA to TEGDMA was 1:1, and the content of silica aerogel with 50 μm particle size was 12.5%, the composite achieved excellent mechanical properties. The flexural strength of the silica aerogel/resin composite reached 62.9546 MPa, which was more than five times that of the pure resin. Due to the presence of the silica aerogel, the composite also demonstrated outstanding antibacterial capabilities, meeting the demand for antimicrobial properties in dental materials. This work successfully investigated the prospect of using commercially available silica aerogels in dental restorative materials; we provide an easy method for using silica aerogels as dental restorative materials, as well as a reference for their application in the field of biomedical materials.