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.     

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...     

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%.     

Semisynthesis and Pesticidal Activities of Novel Cholesterol Ester Derivatives Containing Cinnamic Acid-like Fragments

Due to the extensive use of agrochemicals resulting in the emergence of pesticide resistance and ecological environment problems, the research and development of new alternatives for crop protection is highly desirable. In order to discover potent natural product-based insecticide candidates, a series of new cholesterol ester derivatives containing cinnamic acid-like fragments at the C-7 position were synthesized. Some derivatives showed potent pesticidal activities. Against Mythimna separata Walker, compounds 2a, Id, Ig, and IIg showed 2.1–2.4-fold growth-inhibitory activity of the precursor cholesterol. Against Plutella xylostella Linnaeus, compounds Ig, IIf, and IIi exhibited 1.9–2.1-fold insecticidal activity of cholesterol. These results will pave the way for the future synthesis of cholesterol-based derivatives as agrochemicals.     

Urea Synthesis from Isocyanides and O-Benzoyl Hydroxylamines Catalyzed by a Copper Salt

In the presence of CuOAc, a series of unsymmetric ureas can be generated in moderate to good yields under mild reaction conditions (10 mol% of CuOAc, 2 equiv t-BuONa or PhONa, 30 °C), using aryl isocyanides and O-benzoyl hydroxylamines as the readily accessible starting materials. The reactions might undergo a cascade process involving isocyanide insertion into the N-O bond and Mumm-type rearrangement. This work represents a rare example of isocyanide insertion into N-O bonds, which would extend isocyanide insertion chemistry.     

Sargassum fusiforme Polysaccharide-Based Hydrogel Microspheres Enhance Crystal Violet Dye Adsorption Properties

In this study, a polysaccharide-based hydrogel microsphere (SFP/SA) was prepared using S. fusiforme polysaccharide (SFP) and sodium alginate (SA). Fourier transform infrared spectroscopy (FT-IR) demonstrated that SFP was effectively loaded onto the hydrogel microsphere. Texture profile analysis (TPA) and differential scanning calorimetry (DSC) showed that, with the increase of SFP concentration, the hardness of SFP/SA decreased, while the springiness and cohesiveness of SFP/SA increased, and the thermal stability of SFP/SA improved. The equilibrium adsorption capacity of SFP/SA increased from 8.20 mg/g (without SFP) to 67.95 mg/g (SFP accounted 80%) without swelling, and from 35.05 mg/g (without SFP) to 81.98 mg/g (SFP accounted 80%) after 24 h swelling. The adsorption of crystal violet (CV) dye by SFP/SA followed pseudo-first order and pseudo-second order kinetics (both with R² > 0.99). The diffusion of intraparticle in CV dye was not the only influencing factor. Moreover, the adsorption of CV dye for SFP/SA (SFP accounted 60%) fit the Langmuir and Temkin isotherm models. SFP/SA exhibited good regenerative adsorption capacity. Its adsorption rate remained at > 97% at the 10th consecutive cycle while SFP accounted for 80%. The results showed that the addition of Sargassum fusiforme polysaccharide could increase the springiness, cohesiveness and thermal stability of the hydrogel microsphere, as well as improve the adsorption capacity of crystal violet dye.     

Learning size-adaptive molecular substructures for explainable drug–drug interaction prediction by substructure-aware graph neural network

Drug–drug interactions (DDIs) can trigger unexpected pharmacological effects on the body, and the causal mechanisms are often unknown. Graph neural networks (GNNs) have been developed to better understand DDIs. However, identifying key substructures that contribute most to the DDI prediction is a challenge for GNNs. In this study, we presented a substructure-aware graph neural network, a message passing neural network equipped with a novel substructure attention mechanism and a substructure–substructure interaction module (SSIM) for DDI prediction (SA-DDI). Specifically, the substructure attention was designed to capture size- and shape-adaptive substructures based on the chemical intuition that the sizes and shapes are often irregular for functional groups in molecules. DDIs are fundamentally caused by chemical substructure interactions. Thus, the SSIM was used to model the substructure–substructure interactions by highlighting important substructures while de-emphasizing the minor ones for DDI prediction. We evaluated our approach in two real-world datasets and compared the proposed method with the state-of-the-art DDI prediction models. The SA-DDI surpassed other approaches on the two datasets. Moreover, the visual interpretation results showed that the SA-DDI was sensitive to the structure information of drugs and was able to detect the key substructures for DDIs. These advantages demonstrated that the proposed method improved the generalization and interpretation capability of DDI prediction modeling.

SA-DDI is designed to learn size-adaptive molecular substructures for drug–drug interaction prediction and can provide explanations that are consistent with pharmacologists.