α‐ZrP/Uracil/Cu2+ nanoparticles as an efficient catalyst in the Morita‐Baylis‐Hillman reaction

A facile synthesis of uracil‐Cu²⁺ nanoparticles immobilized on alpha‐zirconium hydrogen phosphate (α‐ZrP), abbreviated as α‐ZrP/Uracil/Cu²⁺, was presented. This compound was synthesized by the thermal method and used as a reusable catalyst for the Morita‐Baylis‐Hillman reaction without any additives. First, (3‐ iodopropyl) trimethoxysilane as a linker is reacted with α‐ZrP support to give the α‐ZrP/IPTMOS. Addition of uracil and then the addition of copper (II) acetate to α‐ZrP/IPTMOS results in the production of selected catalyst. The Morita‐Baylis‐Hillman reaction catalyzed by α‐ZrP/Uracil/Cu^{2 +} demonstrated high product yield, short reaction time and a straightforward work‐up. The catalyst with enough outside surface was easily recovered using centrifugation and reused five times without a significant reduction in its activity.     

Isolated Platinum Atoms Stabilized by Amorphous Tungstenic Acid: Metal–Support Interaction for Synergistic Oxygen Activation

Oxygen activation plays a crucial role in many important chemical reactions such as oxidation of organic compounds and oxygen reduction. For developing highly active materials for oxygen activation, herein, we report an atomically dispersed Pt on WO₃ nanoplates stabilized by in situ formed amorphous H₂WO₄ out‐layer and the mechanism for activating molecular oxygen. Experimental and theoretical studies demonstrate that the isolated Pt atoms coordinated with oxygen atoms from [WO₆] and water of H₂WO₄, consequently leading to optimized surface electronic configuration and strong metal–support interaction (SMSI). In exemplified reactions of butanone oxidation sensing and oxygen reduction, the atomic Pt/WO₃ hybrid exhibits superior activity than those of Pt nanoclusters/WO₃ and bare WO₃ as well as enhanced long‐term durability. This work will provide insight into the origin of activity and stability for atomically dispersed materials, thus promoting the development of highly efficient and durable single atom‐based catalysts.     

Rapid Diagnosis of Type II Diabetes Using Fourier Transform Mid-Infrared Attenuated Total Reflection Spectroscopy Combined with Support Vector Machine

Type II diabetes was diagnosed by Fourier transform mid-infrared (FTMIR) attenuated total reflection (ATR) spectroscopy in combination with support vector machine (SVM). Spectra of serum samples from 65 patients with clinical confirmed type II diabetes mellitus and 55 healthy volunteers were acquired using ATR-FTMIR and were first pretreated by three pretreatments (Savitzky–Golay smoothing, multiple scattering correction, and wavelet transforms algorithms) to reduce the interfering information before establishing the SVM models. The parameters of SVM (penalty factor C and kernel function parameter gamma) were optimized to improve the generalization abilities of the models. A grid search method (GS), genetic algorithm (GA), and particle swarm optimization (PSO) algorithm, were used to find out the optimal parameter values. The results showed that the maximum accuracies were 95.74, 97.87, and 89.36% for the optimized GS, GA, and PSO algorithms. The maximum sensitivities were 96, 100, and 92, and the maximum specificity were 95.45, 95.45, and 86.36%, respectively. The results indicated that the accuracy of type II diabetes was improved using the GS, GA, and PSO algorithms for optimizing the SVM parameters. The GA was found to be slightly better than the GS and PSO. The results of the experiment confirmed that the combination of the ATR-FTMIR spectroscopy and SVM was able to rapidly and accurately diagnose type II diabetes without reagents.     

Co-Mo/SiO2-Al2O3润滑油加氢处理催化剂的制备

以Pluronic P₁₂₃作结构导向剂,采用Al （NO₃）₃-NaAlO₂双水解法合成氧化铝,在成胶过程中加入正硅酸乙酯,制备硅质量分数分别为5%、10%、15%的SiO₂-Al₂O₃载体,并通过共浸渍法制备出Co-Mo/SiO₂-Al₂O₃润滑油加氢处理催化剂。通过XRD、N₂吸附-脱附、Py-FTIR、NH₃-TPD、H₂-TPR、TEM和XRF等手段对载体及催化剂的性质进行表征。结果表明,硅质量分数为10%的SiO₂-Al₂O₃具有优良的孔结构、较多的中强酸以及部分有序的介孔结构。以此为载体制备的Co-Mo/10% SiO₂-Al₂O₃催化剂中,MoS₂颗粒均匀地分散在载体上,具有更多的B酸性位和Ⅱ型CoMoS活性相。以减二线蜡油为原料油的固定床活性评价结果表明,生成油中主要组分为链烷烃与环烷烃;尤其Co-Mo/10% SiO₂-Al₂O₃催化剂具有优良的加氢性能,在15 MPa、380℃、氢油比为1000、空速为0.6 h^-1的反应条件下,其HDS和HDN数值均超过99%,产品中S含量小于10 μg/g,N含量小于2 μg/g,可以满足后续异构脱蜡等对原料的要求。     

内分子DIELS-ALDER反应合成DRIMANE类倍半萜化合物DRIMENIN, POLYGODIAL及CINNAMOLIDE的全合成

近年来有不少报道从事于生理活性Drimane类倍半萜的合成,较多采用了分子间Diels-Alder反应来建立Drimane的骨架。本文首次采用内分子Diels-Alder反应作为关键反应合成了Drimane类化合物。同时还在引入了手性诱导基团后,首次完成了从无手性的原料化合物β-紫罗兰酮至光学活性(-)-drimenin1的不对称合成。     

主成分分析-支持向量机用于肝病分类模型

将主成分分析(PCA)用于肝功能检测数据特征提取,然后用支持向量机(SVM)对乙肝、丙肝、肝硬化、正常人样本建立分类模型。采用高斯径向基函数(RBF)为核函数,调节核函数参数C及σ以建立最佳支持向量机模型。该模型对训练集的识别率为99.3%,对预测集的预测率为96.4%。结果表明:PCA-SVM法建立的肝病分类模型能较好的区分乙肝、丙肝、肝硬化及正常人,且分类效果优于传统支持向量机及人工神经网络(ANN)分类模型。     

Matrix-assisted laser desorption/ionization-mass spectrometry of cuticular lipid profiles can differentiate sex, age, and mating status of Anopheles gambiae mosquitoes

Malaria is a devastating mosquito-borne disease, which affects hundreds of millions of people each year. It is transmitted predominantly by Anopheles gambiae, whose females must be >10 days old to become infective. In this study, cuticular lipids from a laboratory strain of this mosquito species were analyzed using a mass spectrometry method to evaluate their utility for age, sex and mating status differentiation. Matrix-assisted laser desorption/ionization-mass spectrometry (MALDI-MS), in conjunction with an acenaphthene/silver nitrate matrix preparation, was shown to be 100% effective in classifying A. gambiae females into 1, 7–10, and 14 days of age. MALDI-MS analysis, supported by multivariate statistical methods, was also effective in detecting cuticular lipid differences between the sexes and between virgin and mated females. The technique requires further testing, but the obtained results suggest that MALDI-MS cuticular lipid spectra could be used for age grading of A. gambiae females with precision greater than with other available methods.     

A sequence-based computational model for the prediction of the solvent accessible surface area for α-helix and β-barrel transmembrane residues

Predicting the solvent accessible surface area (ASA) of transmembrane (TM) residues is of great importance for experimental researchers to elucidate diverse physiological processes. TM residues fall into two major structural classes (α-helix membrane protein and β-barrel membrane protein). The reported solvent ASA prediction models were developed for these two types of TM residues respectively. However, this prevents the general use of these methods because one cannot determine which model is suitable for a given TM residue without information of its type. To conquer this limitation, we developed a new computational model that can be used for predicting the ASA of both TM α-helix and β-barrel residues. The model was developed from 78 α-helix membrane protein chains and 24 β-barrel membrane protein. Its prediction ability was evaluated by cross validation method and its prediction result on an independent test set of 20 membrane protein chains. The results show that our model performs well for both types of TM residues and outperforms other prediction model which was developed for the specific type of TM residues. The prediction results also proved that the random forest model incorporating conservation score is an effective sequence-based computational approach for predicting the solvent ASA of TM residues.