卟啉超分子化合物在分子器件中的应用

分子电子器件已成为近年来的一个研究热点，卟啉类化合物因为光敏性好、性能稳定、易于修饰等优点成为分子器件研究的理想模型化合物。本文着重介绍了它在分子器件中的最新应用进展。     

Black fungus derived aerogel with double faced properties

Black fungus aerogel (BFA) exhibited interesting double-faced properties. Weexplored the diverse properties of each side of the black fungus in three aspects:water contact angle measurements, liquid selective absorption capacity and air pollutant adsorption abilities.     

Dynamic coating of a capillary with room-temperature ionic liquids for the separation of amino acids and acid drugs by capillary electrophoresis

A room-temperature ionic liquid (IL), 1-ethyl-3-methyl-imidazolium tetrafluoroborate (1E-3MI-TFB), used for the coating of a silica capillary enables one to reduce or invert the electroosmotic flow (EOF) in capillary zone electrophoresis. Excellent separations of amino acids and ary lalkanoic acids were obtained. Such separations could not be obtained in a naked capillary in the presence of the cationic surfactants cetyltrimethylammonium bromide (CTAB) or polycationic polymer hexadimethrine bromide (HDB). The results indicate that 1E-3MI-TFB not only modulates the EOF but also acts as a discriminator. Further experiments indicate that the interaction between hydrogen at C-2 carbon of IL and acid drugs plays an important role in the separation. The text was submitted by the authors in English.     

A preliminary study on fabrication of nanoscale fibrous chitosan membranes in situ by biospecific degradation

A new approach for in situ fabrication of nanoscale fibrous chitosan membrane by biospecific degradation under physiological situation was studied. The chitosan binary blend membranes were fabricated by solvent casting of chitosan solution containing highly deacetylated chitosan (HDC) and moderately deacetylated chitosan (MDC) with different ratio. The biodegradation process was performed in PBS (pH 7.4) containing lysozyme at the temperature of 37 °C. Experimental results from weight loss, reducing sugar in surrounding media, FT-IR, X-ray diffraction, gel permeation chromatography (GPC) and SEM throughout the study showed that the biospecific degradation by lysozyme had removed MDC component selectively. When the ratio of MDC in the binary blend membranes amounted to 0.5, nanoscale domains of HDC and MDC were obtained, and thus a nanoscale fibrous structure was fabricated after biospecific degradation of MDC. This nanofibrous structure and the biospecific degradation of chitosan membranes can have potential advantages and interesting implications in tissue engineering and drug delivery.     

双功能型嵌入镍纳米颗粒的碳棱柱状微米棒电极用于电化学甲醇氧化助力的节能产氢

With the rapid development of human society, clean energy forms are imperative to sustain the normal operations of various mechanical and electrical facilities under a cozy environment. Hydrogen is considered among the most promising clean energy sources for the future. Recently, electrochemical water splitting has been considered as one of the most efficient approaches to harvest hydrogen energy, which generates only non-pollutant water on combustion. However, the sluggish anodic oxygen evolution reaction significantly restricts the efficiency of water splitting and requires a relatively high cell voltage to drive the electrolysis. Therefore, seeking a thermodynamically favorable anodic reaction to replace the sluggish oxygen evolution reaction by utilizing highly active bifunctional electrocatalysts for the anodic reaction and hydrogen evolution are crucial for achieving energy-efficient hydrogen production for industrial applications. Nevertheless, it is known that the oxygen evolution reaction can be replaced with other useful and thermodynamically favorable reactions to reduce the electrolysis voltage for realizing energy-efficient hydrogen production. Therefore, in this study, we present a bifunctional nickel nanoparticle-embedded carbon (Ni@C) prism-like microrod electrocatalyst synthesized via a two-step method involving the synthesis of a precursor metal-organic framework-74 and subsequent carbonization treatment for methanol oxidation and hydrogen evolution. The interfacial structure consisting of a nickel and carbon skeleton was realized via in situ carbonization. However, the dispersed nickel nanoparticles do not easily aggregate owing to the partition by the surrounding carbon as it would sufficiently expose the active Ni sites to the electrolytes, ensuring fast charge transfer between the catalyst and electrolytes by accelerating the electrochemical kinetics. In the anodic methanol oxidation, the products were detected as carbon dioxide and formate with faradaic efficiencies of 36.2% and 62.5%, respectively, at an applied potential of 1.55 V. Meanwhile, the Ni@C microrod catalyst demonstrated high activity and durability (2.7% current decay after 12 h of continuous operation) toward methanol oxidation, which demonstrates that methanol oxidation precedes oxidation under voltage forces. Notably, the bifunctional catalyst not only exhibits excellent performance toward methanol oxidation but also yields a low overpotential of 155 mV to drive 10 mA∙cm⁻² toward hydrogen evolution in 1.0 mol∙L⁻¹ KOH aqueous solution with 0.5 mol∙L⁻¹ methanol at room temperature, which guarantees the hydrogen production efficiency. More importantly, the constructed two-electrode electrolyzer produced a current density of 10 mA∙cm⁻² at a low cell voltage of 1.6 V, which decreased by 240 mV after replacing the oxygen evolution reaction with methanol oxidation.     

应用极化水分子模型研究单分子层受限水的介电性质

受限条件下水的介电性质因测量极具挑战,其在诸多电化学过程与反应输运过程中如何扮演关键角色从未被定量地澄清.本工作利用平衡态分子动力学模拟和受限体系介电性质计算方法,系统性地探索了0.65 nm限域尺寸、5×10⁸ Pa限域压强、不同温度条件下单分子受限冰和受限水的介电性质.详细比较了恒定偶极矩SPC/E水分子模型和可极化的SWM4-NDP水分子模型在描述受限冰、水结构与介电性质上的优劣势,包括统计分析SWM4-NDP模型模拟的单分子层受限水和受限冰的瞬时分子偶极矩概率密度分布,计算每个模拟体系的静态结构因子、静态偶极空间关联函数、静态介电常数、体系偶极时间关联函数和德拜弛豫时间.首次发现了极化水分子模型描述的低维度受限水和受限冰的奇异分子极性变化,并观察到两种模型描述静态结构性质的效果相当, SWM4-NDP模型对于静态介电常数描述的优势会因受限条件的增强而被大幅削减.但在受限水介电极化弛豫动力学性质描述上SWM4-NDP模型明显优于SPC/E模型.我们推断SWM4-NDP模型在探索受限水结构相变动力学以及受限体系离子输运和溶剂化动力学等过程的模拟研究中是比SPC...     

A Cyanine-based Liposomal Nanophotosensitizer for Enhanced Cancer Chemo-Photodynamic Therapy

Currently, chemotherapy is one of the most important treatment modalities for malignant tumors in the clinic, however, it exhibits some shortcomings, such as poor selectivity, limited efficacy and serious adverse effects. Therefore, synergistic therapy and accurate drug delivery at tumor sites become a promising strategy for achieving tumor eradication. Herein, a smart NIR fluorescence imaging-guided nanoliposome was fabricated by encapsulating a chemotherapeutic drug(doxorubicin, DOX), liposomes(L) and a near-infrared(NIR) photosensitizer(CY) to form L@CY@DOX, which could realize enhanced therapeutic efficacy of chemo-PDT in cancer therapy(PDT=photodynamic therapy). L@CY@DOX can induce mitochondrial apoptosis and produce severe toxicity at the cellular level, and L@CY@DOX can enrich in the tumor site, which significantly induces tumor death. In vitro and in vivo studies demonstrated that L@CY@DOX exhibited great antitumor efficacy compared with each one of these monotherapies, indicating that the combination of chemotherapy and PDT possessed potential development prospects and is anticipated in clinical application.     

Dynamic Programming BN Structure Learning Algorithm Integrating Double Constraints under Small Sample Condition

The Bayesian Network (BN) structure learning algorithm based on dynamic programming can obtain global optimal solutions. However, when the sample cannot fully contain the information of the real structure, especially when the sample size is small, the obtained structure is inaccurate. Therefore, this paper studies the planning mode and connotation of dynamic programming, restricts its process with edge and path constraints, and proposes a dynamic programming BN structure learning algorithm with double constraints under small sample conditions. The algorithm uses double constraints to limit the planning process of dynamic programming and reduces the planning space. Then, it uses double constraints to limit the selection of the optimal parent node to ensure that the optimal structure conforms to prior knowledge. Finally, the integrating prior-knowledge method and the non-integrating prior-knowledge method are simulated and compared. The simulation results verify the effectiveness of the method proposed and prove that the integrating prior knowledge can significantly improve the efficiency and accuracy of BN structure learning.     

Highly Accurate Visual Method of Mars Terrain Classification for Rovers Based on Novel Image Features

It is important for Mars exploration rovers to achieve autonomous and safe mobility over rough terrain. Terrain classification can help rovers to select a safe terrain to traverse and avoid sinking and/or damaging the vehicle. Mars terrains are often classified using visual methods. However, the accuracy of terrain classification has been less than 90% in read operations. A high-accuracy vision-based method for Mars terrain classification is presented in this paper. By analyzing Mars terrain characteristics, novel image features, including multiscale gray gradient-grade features, multiscale edges strength-grade features, multiscale frequency-domain mean amplitude features, multiscale spectrum symmetry features, and multiscale spectrum amplitude-moment features, are proposed that are specifically targeted for terrain classification. Three classifiers, K-nearest neighbor (KNN), support vector machine (SVM), and random forests (RF), are adopted to classify the terrain using the proposed features. The Mars image dataset MSLNet that was collected by the Mars Science Laboratory (MSL, Curiosity) rover is used to conduct terrain classification experiments. The resolution of Mars images in the dataset is 256 × 256. Experimental results indicate that the RF classifies Mars terrain at the highest level of accuracy of 94.66%.     

Structure-Based Discovery and Biological Assays of a Novel PRMT5 Inhibitor for Non-Small Cell Lung Cancer

Protein arginine methyltransferase 5 (PRMT5) is a popular anticancer target that regulates histone or nonhistone methylation and is linked to the development and poor prognosis of non-small cell lung cancer. PRMT5 inhibitors have shown great promise in clinical trials as a cancer therapy. However, most inhibitors reported recently act in a SAM-competitive mode and lack structural diversity. In this paper, a novel non-SAM inhibitor, 3039-0164, was discovered by the structure-based virtual screening method. The binding mechanism of 3039-0164 to PRMT5 was revealed via molecular docking and molecular dynamics simulations. 3039-0164 inhibited PRMT5 enzymatic activity, downregulated the expression of PRMT5 downstream target genes (FGFR3 and eIF4E), and blocked the activation of the PI3K/AKT/mTOR and ERK signaling pathways. The discovery of 3039-0164 provides precise and creative hit compounds for the design optimization of PRMT5 lead compounds in non-small cell lung cancer.