Preparation of high-strength silica aerogels by two-step surface modification via ambient pressure drying

Journal of Porous Materials - High-strength silica aerogels were prepared successfully by a new two-step surface modification (TSSM) method via ambient pressure drying (APD). Methyltrimethoxysilane...     

Enzyme-Activated Prodrug-Based Smart Liposomes Specifically Enhance Tumor Hemoperfusion with Efficient Drug Delivery to Pancreatic Cancer Cells and Stellate Cells

Tumor-specific enhanced delivery of chemotherapeutics and modulators to tumor cells and activated pancreatic stellate cells (aPSCs), respectively, represents safer and more effective therapy for pancreatic cancer. Herein, a membrane type 1-matrix metalloproteinase (MT1-MMP)-cleavable spacer is used to assemble low-density cRGDfK onto thermosensitive liposomes loaded with phosphorylated calcipotriol (PCAL) and doxorubicin (DOX), yielding MR-T-PD. The liposome-linked cRGDfK prodrug on MR-T-PD surface is first activated by MT1-MMP, which is selectively expressed on tumor endothelial cells, to release cRGDfK. The free cRGDfK specifically promotes tumor angiogenesis, leading to 3.4-fold higher accumulation and a wider distribution of MR-T-PD in tumors. Furthermore, MR-T-PD rapidly releases PCAL and DOX into the interstitium under heat treatment. The released DOX enters tumor cells to induce apoptosis, whereas the PCAL prodrug is converted to CAL by alkaline phosphatase on the surface of aPSCs; CAL can then enter aPSCs to induce quiescence and promote the antitumor effect of DOX. Finally, by enhancing the exposure of DOX and CAL to tumor cells and aPSCs, respectively, in a tumor-specific manner, MR-T-PD exerts superior efficacy (a 5.9-fold decrease in tumor weight) without causing additional side effects. Overall, this prodrug-based smart liposome system represents a promising paradigm for pancreatic cancer therapy.     

农村光棍成因及治理机制研究

以驻村扶贫点滇西南L县X村的光棍为研究对象,侧重个体精准性,强调个体因果性的特征,运用小数据分析该村光棍的贫困特征和生成原因,基于马斯洛需求层次理论探索光棍内生动力激发机制.研究表明:农村光棍趋于年轻化,形势严峻;光棍成因受到外部资源匮乏的限制,又受到自身资源缺陷的制约,内外因相互影响,但外因只有通过内因才能发挥作用.探讨通过需求原理激发光棍群体的内生动力,构建长效治理机制,让光棍群体最大限度实现人生价值,提升社会获得感和幸福感.     

Stable Radicals with Protective Umbrellas Integrated on the Surface of 2D Layered Materials

Radicals are closely related to human life and health and have been widely used in biology, chemistry, functional materials, etc. However, the high reactivity, disorder, and short half-lives limit their wide applications. Therefore, it remains a great challenge to prepare stable and ordered radicals. Herein, radicals are prepared with protective umbrellas (diethylmethyleneamine, DEMA) that are integrated on the surface of 2D layered materials to isolate water and oxygen and enhance the stability of radicals. Taking 2D black phosphorus (BP) as an example: triethylamine reacts with dichloromethane to form quaternary ammonium salts with further Hoffmann elimination to produce DEMA radicals that could react with one electron of a lone pair electrons in P on the surface of BP to produce P radicals, which shows a prolonged half-life of 21 days at room temperature. First-principle calculations and electron paramagnetic resonance fitting confirm that the steric hindrance constructed by dense DEMA passivation layer acts as a protective umbrella and the 2D coupling of P radicals and other P atoms in 2D BP plane to enhance the stability and strong superexchange interaction of P radicals. Furthermore, it is a general strategy to produce stable radicals integrated on the 2D plane.     

Enhanced thermal conductivity and stability of boron nitride/phenyl silicone rubber composites via surface modification and grain alignment

Journal of Materials Science - With the extensive use of high-power electronic appliances, polymer-based thermal insulation composites with excellent thermal properties are utilized in the field of...     

CDBN‑IKELM的轴承变工况故障诊断方法

针对现有方法在轴承变工况方面存在的诊断精度低、人工提取特征不充分等问题，提出了基于卷积深度置信网络（convolutional deep belief network，简称CDBN）与改进核极限学习机 （improved Kernel?based extreme learning machine，简称IKELM）的滚动轴承故障智能识别方法。首先，由卷积深度置信网络对原始信号内的故障特征进行深层自适应提取；其次，利用等距特征映射对提取的多维特征进行降维，去除冗余特征信息；然后，采用改进的核极限学习机对特征进行分类，使用粒子群（particle swarm optimization， 简称PSO）对模型重要参数进行优化，实现滚动轴承变工况下的故障识别；最后，将所提方法应用于不同工况下多种轴承故障的诊断。实验结果表明，该方法能够智能有效地识别变工况的轴承故障，诊断结果优于已有的智能故障诊断方法。     

Engineering A-site cation deficiency into LaCoO3 thin sheets for improved microwave absorption performance

Journal of Materials Science - Rationally designing microwave absorption materials with highly efficient and tunable bandwidth is in great demand but remains a huge challenge. In this study,...