基于二氰根铬的一系列氰根桥联CrⅢ-CuⅡ-CrⅢ三核配合物的合成、结构与磁性

基于一系列二氰根铬与[Cu(cyclam)](ClO₄)₂反应合成了3个氰根桥联Cr^Ⅲ-Cu^Ⅱ-Cr^Ⅲ三核配合物[Cu(cyclam)][Cr(bpmb)(CN)₂]₂·4H₂O(1)(cyclam=1,4,8,11-四氮杂环十四,bpmb^2-=1,2-二(2-吡啶甲酰胺基)-4-甲基苯),[Cu(cyclam)][Cr(bpdmb)(CN)₂]₂(2)(bpdmb^2-=1,2-二(2-吡啶甲酰胺基)-4,5-二甲基苯)和[Cu(cyclam)][Cr(bpClb)(CN)₂]₂·4H₂O(3)(bpClb^2-=1,2-二(2-吡啶甲酰胺基)-4-氯苯)。单晶衍射结果表明:3个化合物是结构类似的中性三核配合物,均含有氰根桥联的Cr(Ⅲ)-CN-Cu(Ⅱ)-NC-Cr(Ⅲ)连接;磁性研究表明:氰根桥在Cr^Ⅲ和Cu^Ⅱ离子间传递弱的铁磁耦合作用,基于自旋哈密顿算符Ĥ=-2J_CrCuŜ_Cu(Ŝ_Cr1+Ŝ_Cr2)拟合得到它们的磁耦合常数分别是J_CrCu=1.53(2) cm^-1(1),0.45(1) cm^-1(2)和0.73(2) cm^-1(3)。     

高岭石/苯甲酰胺插层复合物的热分解行为及脱嵌反应动力学

以高岭石/二甲基亚砜为前驱体,利用置换法制备了高岭石/苯甲酰胺插层复合物。XRD和FTIR分析表明苯甲酰胺进入高岭石层间并与其形成新的氢键。采用TG、DSC研究了插层复合物的热分解行为。结果表明复合物在加热过程中发生两步分解,第一步是插层复合物的分解,即插层剂分子于231℃发生脱嵌,第二步为高岭石脱羟基的过程。针对第一阶段的脱嵌反应,采用等转化率法改进后的迭代法、Malek法以及Dollimore法等动力学方法计算得到了完整的动力学三因子:活化能Ea=75.4kJ.mol-1,指前因子A的范围为4.9×1010~8.8×1010s-1,动力学方程为:G(α)=[1-(1-α)1-n]/(1-n),f(α)=(1-α)n。     

Highly Efficient Synthesis of Tricyclic Perimidines under Microwave Heating

A new and efficient access to (Z)‐N‐(2‐argio‐1‐(1H‐perimidin‐2‐yl)vinyl)benzamide derivatives from readily available substrates in HOAc is described with aid of microwave irradiation. The results of our study provide a simple, straightforward synthetic route to these interesting classes of 2‐substituted perimidines analogs in excellent yields.     

方便、有效地合成2-氨基-4-芳基-6-甲基四氢化萘衍生物

芳香醛、4-甲基环己酮和丙二腈在氢氧化钠存在下于95%乙醇中反应,方便地获得一系列新的2-氨基-4-芳基-6-甲基四氢化萘衍生物.本方法具有反应时间短、容易操作、产率高等优点.产物的结构经过核磁、红外和高分辨质谱确定.此方法是合成此类化合物的一条有效途径.     

氰根桥联FeⅢ-CuⅡ一维配合物{[Fe(1-CH3im)(CN)4(μ-CN)Cu(cyclam)]·H2O}n的合成、结构及磁性研究

基于构筑单元K2[Fe(1-CH3im)(CN)5]和[Cu(cyclam)](ClO4)2,合成了一个氰根桥联FeⅢ-CuⅡ中性一维化合物{[Fe(1-CH3im)(CN)4(μ-CN)Cu(cyclam)]·H2O}n(1-CH3im=1-甲基咪唑;cyclam=1,4,8,11-四氮杂环十四烷)(1),并通过X-射线单晶分析表征其结构特征。结果表明:化合物(1)是由氰根桥联的杂金属组成的聚合物,其结构属于三斜晶系,P1空间群,a=0.832 56(17)nm,b=0.899 38(18)nm,c=0.998 3(2)nm,α=111.94(3)°,β=95.06(3)°,γ=116.90(3)°,V=0.587 7(2)nm3,Z=1,Dc=1.554 g·cm-3,μ=1.558 mm-1,F(000)=286,R1=0.051 9,wR2=0.135 3。磁性研究表明:配合物1中CuⅡ和低自旋的FeⅢ离子之间存在弱的铁磁耦合作用。     

Optimised synthesis of diamino-triazinylmethyl benzoates as inhibitors of Rad6B ubiquitin conjugating enzyme

Recently, we have identified the first inhibitors of Rad6B, an E2 enzyme essential for post-replication DNA repair and a potential new drug target for the treatment of breast cancer. We report two newly optimised synthetic routes to our [4-amino-6-(phenylamino)-1,3,5-triazin-2-yl]methyl 4-nitrobenzoate target compounds TZ8 and TZ9 with general applicability for further structure–activity relationship studies around this pharmacophore. The key step involved the condensation/cyclisation between phenylbiguanide and either ethyl bromoacetate or dimethyloxalate in good yield.     

Facile fabrication of hydrangea-like NiSe/FeSe2 nanostructures towards efficient water oxidation

A facile one-pot hydrothermal method has been demonstrated for the fabrication of an innovative hydrangea-like NiSe/FeSe₂ nanocatalyst for boosting oxygen evolution reaction (OER). Benefitting from the advantages of the porous architecture, high specific surface area, facilitated electron transfer rate, an ultralow overpotential of merely 210 mV is required for the optimized NiSe/FeSe_2(1:1.5) to drive the electrocatalytic water oxidation to reach to 10 mA cm^?2. Moreover, by equipping NiSe/FeSe_2(1:1.5) with Pt/C for electrochemical water splitting, a cell potential of merely 1.60 V is demanded to attain 10 mA cm^?2, even outperforming the IrO₂ 6 Pt/C couple. More importantly, the structure and morphology of NiSe/FeSe_2(1:1.5) are still well maintained after a long-term chronopotentiometry test. This work opens a new avenue for constructing effective and durable non-precious electrocatalysts for OER.     

Prediction of an outcome using NETwork Clusters (NET-C)

Birth weight is a key consequence of environmental exposures and metabolic alterations and can influence lifelong health. While a number of methods have been used to examine associations of trace element (including essential nutrients and toxic metals) concentrations or metabolite concentrations with a health outcome, birth weight, studies evaluating how the coexistence of these factors impacts birth weight are extremely limited. Here, we present a novel algorithm NETwork Clusters (NET-C), to improve the prediction of outcome by considering the interactions of features in the network and then apply this method to predict birth weight by jointly modelling trace element and cord blood metabolite data. Specifically, by using trace element and/or metabolite subnetworks as groups, we apply group lasso to estimate birth weight. We conducted statistical simulation studies to examine how both sample size and correlations between grouped features and the outcome affect prediction performance. We showed that in terms of prediction error, our proposed method outperformed other methods such as (a) group lasso with groups defined by hierarchical clustering, (b) random forest regression and (c) neural networks. We applied our method to data ascertained as part of the New Hampshire Birth Cohort Study on trace elements, metabolites and birth outcomes, adjusting for other covariates such as maternal body mass index (BMI) and enrollment age. Our proposed method can be applied to a variety of similarly structured high-dimensional datasets to predict health outcomes.     

Study on backward-facing step flow and heat transfer characteristics of hybrid nanofluids

Journal of Thermal Analysis and Calorimetry - As high heat dissipation has increasingly become the primary factor restricting the capability of electronic elements, and the high temperature of the...     

An ensemble learning framework for potential miRNA-disease association prediction with positive-unlabeled data

To explore the pathogenic mechanisms of MicroRNA (miRNA) on diverse diseases, many researchers have concentrated on discovering the potential associations between miRNA and disease using machine learning methods. However, the prediction accuracy of supervised machine learning methods is limited by lacking of experimentally-validated uncorrelated miRNA-disease pairs. Without these negative samples, training a highly accurate model is much more difficult. Different from traditional miRNA-disease prediction models using randomly selected unknown samples as negative training samples, we propose an ensemble learning framework to solve this positive-unlabeled (PU) learning problem. The framework incorporates two steps, i.e., a novel semi-supervised Kmeans (SS-Kmeans) to extract reliable negative samples from unknown miRNA-disease pairs and subagging method to generate diverse training sample sets to make full use of those reliable negative samples for ensemble learning. Combined with effective random vector functional link (RVFL) network as prediction model, the proposed framework showed superior prediction accuracy comparing with other popular approaches. A case study on lung and gastric neoplasms further confirms the framework’s efficacy at identifying miRNA disease associations.