一种新型双吡啶悬臂Mn(Ⅱ)配合物的合成、晶体结构及DNA结合能力研究

通过3,3′-((乙烷-1,2-二基双(2-甲基吡啶杂氮二基)双(亚甲基))双(2-羟基-5-甲基苯甲醛)与2-羟基-1,3-丙二胺的缩合反应得到一种具有双吡啶悬臂的双核锰配合物。通过X射线单晶衍射确定了该配合物结构,结果显示其分子式为[Mn₂(C₃₇H₄₃N₆O₆)]·(ClO₄)₂。该配合物属于单斜晶系,P2₁/c空间群,晶胞参数为:a=1.096 50(19) nm, b=1.419 5(3) nm, c=3.109 4(5) nm, β=108.153(5)°。进一步分析表明两个二价锰离子分别与(N_amine)₂(N_imine)₂O₃和(N_imine)₂O₄体系配位,它们与配位原子形成的几何构型分别是十面体和扭曲的八面体。两个中心锰离子距离为0.331 6 nm,由酚氧原子和醋酸根共同桥联。另外,本文也利用伏安法和黏度法对该配合物与小牛胸腺DNA的结合能力进行研究,实验结果表明它们之间的结合方式为弱的插入作用。     

Helicate tris(aryl)carbinolates bearing pendant NR2 donors – a new family of supporting ligands for the synthesis of Sc3+ alkyl complexes

The reactions of aryllithium reagents o-LiC₆H₄CH₂NR₂ with (MeO)₂CO afford two new tris(aryl)carbinols bearing pendant-NR₂ donor groups in the side chain [o-R NCH C H ] COH [R = Me, R + R = (CH) ]. These alcohols feature helical chirality due to differently inclined aromatic fragments and are presented in a crystalline cell as two M and P enantiomers. Carbinol (R = Me) readily reacts with (Me3SiCH2)3Sc(THF)2 to give a scandium bis(alkyl) complex [(o-C₆H₄CH₂NMe₂)₃CO]Sc(CH₂SiMe₃)₂ featuring rigid binding of the alkoxy anion through a κ¹-O, κ²-N chelating coordination mode     

热解吸反压低温等离子体电离源质谱检测食品中的邻苯二甲酸酯EI北大核心CSCD

基于低温等离子体设计并实现了一套热解吸反压低温等离子体电离(TD-iLTPI)装置。TD-iLTPI装置由热解吸模块和电离源模块集成在一个π型四通管上组成,进样探针取得样品后插入热解吸模块使其气化,之后随载气进入电离区域被电离。iLTPI内部的针电极接交流电,外部的环形电极接地,电极连接方式与LTP相反。该文对热解吸反压低温等离子体电离源进行了参数的优化,并与三重四极杆质谱联用检测了12种代表性的邻苯二甲酸酯,同时考察了TD-iLTPI-MS的分析性能,并对含有邻苯二甲酸酯的实际样品进行了检测。结果表明,邻苯二甲酸丁苄酯(BBP)的标准曲线具有良好的线性相关系数(R;=0.9958),加标回收率为89.7%~116.8%,相对标准偏差为4.5%~8.2%,对邻苯二甲酸酯的检出限也远低于其他敞开式离子源。对白酒、果汁、面包、奶酪和黄油中的邻苯二甲酸酯进行了快速筛查,并定量检测了果汁中的邻苯二甲酸丁苄酯。     

Comprehensive performance of a ball-milled La0.5Pr0.5Fe11.4Si1.6B0.2Hy/Al magnetocaloric composite

Due to the hydrogen embrittlement effect, La(Fe,Si)₁₃-based hydrides can only exist in powder form, which limits their practical application. In this work, ductile and thermally conductive Al metal was homogeneously mixed with La_0.5Pr_0.5Fe_11.4Si_1.6B_0.2 using the ball milling method. Then hydrogenation and compactness shaping of the magnetocaloric composites were performed in one step via a sintering process under high hydrogen pressure. As the Al content reached 9 wt.%, the La_0.5Pr_0.5Fe_11.4Si_1.6B_0.2H_y/Al composite showed the mechanical behavior of a ductile material with a yield strength of ~44 MPa and an ultimate strength of 269 MPa accompanied by a pronounced improvement in thermal conductivity. Due to the ease of formation of Fe-Al-Si phases and the several micron and submicron sizes of the composite particles caused by ball milling process, the magnetic entropy change of the composites was substantially reduced to ~1.2 J/kg· K-1.5 J/kg· K at 0 T-1.5 T.     

基于快速神经网络架构搜索的鲁棒图像水印网络算法

为解决深度学习在图像水印算法中计算量大且模型冗余的问题，提高图像水印算法在抵抗噪声、旋转和剪裁等攻击时的鲁棒性，提出基于快速神经网络架构搜索（neural architecture search，NAS）的鲁棒图像水印网络算法。通过多项式分布学习快速神经网络架构搜索算法，在预设的搜索空间中搜索最优网络结构，进行图像水印的高效嵌入与鲁棒提取。首先，将子网络中线性连接的全卷积层设置为独立的神经单元结构，并参数化表示结构单元内节点的连接，预先设定结构单元内每个神经元操作的搜索空间；其次，在完成一个批次的数据集训练后，依据神经元操作中的被采样次数和平均损失函数值动态更新概率；最后，重新训练搜索完成的网络。水印网络模型的参数量较原始网络模型缩减了92%以上，大大缩短了模型训练时间。由于搜索得到的网络结构更为紧凑，本文算法具有较高的时间性能和较好的实验效果，在隐藏图像时，对空域信息的依赖比原始网络更少。对改进前后的2个网络进行了大量鲁棒性实验，对比发现，本文算法在CIFAR-10数据集上对抵抗椒盐噪声和旋转、移除像素行（列）等攻击优势显著；在ImageNet数据集上对抵抗椒盐高斯噪声、旋转、中值滤波、高斯滤波、JPEG压缩、裁剪等攻击优势显著，特别是对随机移除行（列）和椒盐噪声有较强的鲁棒性。     

突出矿井瓦斯抽放限制影响因素的AHP-熵权法评价模型

为提高煤与瓦斯突出矿井瓦斯抽放效果,建立了3个一级指标、14个二级指标的突出矿井瓦斯抽放限制影响因素评价指标体系,利用AHP和熵权法分别确定指标因子主、客观权重.通过实地调研分析和反馈验证了AHP-熵权法的可行性和正确性,利用加权平均法确定评价模型的综合权重.研究表明:封孔方式、钻孔半径、抽放时间、煤体裂隙发育程度和抽放负压是目前影响煤矿瓦斯抽放效果的主控因素.     

对氯苯甲酸构筑的铜(Ⅱ)配合物的合成、HSA结合及细胞毒性

本文合成了配合物[Cu(pcba)₂·(phen)(H₂O)] (pcba =对氯苯甲酸,phen = 1,10-邻菲罗啉),该配合物属于三斜晶系,P1空间群,晶胞参数为a=0.790 98(2) nm,b=1.072 40(4) nm,c=1.487 19(6) nm,α=100.613(3)°,β=95.239(3)°,γ=108.334(3)°,Z=2,D_c=1.638 g·cm^-3,F(000)=582,最终结构残差因子R₁=0.035 9,wR₂=0.089 1。采用紫外及荧光研究了配合物和人血清蛋白(HSA)的相互作用方式。结果表明,配合物静态猝灭HSA荧光,可求得配合物与HSA的猝灭常数K_sv=2.35×10⁵ L·mol^-1,猝灭速率常数K_q=2.35×10¹³ L·mol^-1·s^-1,结合常数为K_a=2.14×10¹³ L·mol^-1,结合位点n=2.37。同时,研究了配合物对胃癌细胞A549、宫颈癌细胞Hela和肝癌细胞HepG2的抗增殖能力。     

Optimisation of the ultrasound-assisted extraction of betalains and polyphenols from Amaranthus hypochondriacus var. Nutrisol

The present study optimised the ultrasound-assisted extraction (UAE) of bioactive compounds from Amaranthus hypochondriacus var. Nutrisol. Influence of temperature (25.86–54.14 °C) and ultrasonic power densities (UPD) (76.01–273.99 mW/mL) on total betalains (BT), betacyanins (BC), betaxanthins (BX), total polyphenols (TP), antioxidant activity (AA), colour parameters (L*, a*, and b*), amaranthine (A), and isoamaranthine (IA) were evaluated using response surface methodology. Moreover, betalain extraction kinetics and mass transfer coefficients (K_La) were determined for each experimental condition. BT, BC, BX, TP, AA, b*, K_La, and A were significantly affected (p < 0.05) by temperature extraction and UPD, whereas L*, a*, and IA were only affected (p < 0.05) by temperature. All response models were significantly validated with regression coefficients (R²) ranging from 87.46 to 99.29%. BT, A, IA, and K_La in UAE were 1.38, 1.65, 1.50, and 29.93 times higher than determined using conventional extraction, respectively. Optimal UAE conditions were obtained at 41.80 °C and 188.84 mW/mL using the desired function methodology. Under these conditions, the experimental values for BC, BX, BT, TP, AA, L*, a*, b*, K_La, A, and IA were closely related to the predicted values, indicating the suitability of the developed quadratic models. This study proposes a simple and efficient UAE method to obtain betalains and polyphenols with high antioxidant activity, which can be used in several applications within the food industry.     

A self-powered and sensitive terahertz photodetection based on PdSe2

With the rapid development of terahertz technology, terahertz detectors are expected to play a key role in diverse areas such as homeland security and imaging, materials diagnostics, biology, medical sciences, and communication. Whereas self-powered, rapid response, and room temperature terahertz photodetectors are confronted with huge challenges. Here, we report a novel rapid response and self-powered terahertz photothermoelectronic (PTE) photodetector based on a low-dimensional material: palladium selenide (PdSe₂). An order of magnitude performance enhancement was observed in photodetection based on PdSe₂/graphene heterojunction that resulted from the integration of graphene and enhanced the Seebeck effect. Under 0.1-THz and 0.3-THz irradiations, the device displays a stable and repeatable photoresponse at room temperature without bias. Furthermore, rapid rise (5.0 μs) and decay (5.4 μs) times are recorded under 0.1-THz irradiation. Our results demonstrate the promising prospect of the detector based on PdSe₂ in terms of air-stable, suitable sensitivity and speed, which may have great application in terahertz detection.     

Optimization of heterogeneous Catalyst-assisted fatty acid methyl esters biodiesel production from Soybean oil with different Machine learning methods

There is a growing attention to the bio and renewable energies due to fast depletion of fossil fuels as well as the global warming problem. Here, we developed a modeling and simulation method by means of artificial intelligence (AI) for prediction of the bioenergy production from vegetable bean oil. AI methods are well known for prediction of complex and nonlinear process. Three distinct Adaptive Boosted models including Huber regression, LASSO, and Support Vector Regression (SVR) as well as artificial neural network (ANN) were applied in this study to predict actual yield of Fatty acid methyl esters (FAME) production. All boosted utilizing the Adaptive boosting algorithm. The important influencing parameters on the biodiesel production such as the catalyst loading (CAO/Ag, wt%) and methanol to oil (Soybean oil) molar ratio were selected as the input variables of models while the yield of FAME production was selected as output. Model hyper-parameters were tuned to maintain generality while improving prediction accuracy. The models were evaluated using three distinct metrics Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and R². Error rates of 8.16780E-01, 4.43895E-01, 2.06692E + 00, and 3.92713 E-01 were obtained with the MAE metric for boosted Huber, SVR, LASSO and ANN models. On the other hand, the RMSE error of these models were about 1.092E-02, 1.015E-02, 2.669E-02, and 1.01174E-02, respectively. Finally, the R-square score were calculated for boosted Huber, boosted SVR, and boosted LASSO as 0.976, 0.990, 0.872, and 0.99702, respectively. Therefore, it can be concluded that although the boosted SVR and ANN models were better models for prediction of process efficiency in terms of error, but all algorithms had high accuracy. The optimum yield of 83.77% and 81.60% for biodiesel production were observed at optimum operating values from boosted SVR and ANN models, respectively.