峨山褐煤的分子结构和分子模拟

作为重要的化石能源，褐煤资源潜力巨大、分布广泛但综合利用率低。研究褐煤的分子结构模型，有助于预测褐煤在热解、液化和气化过程中的化学反应机理及反应路径，进而提高褐煤的综合应用水平。以云南峨山褐煤为研究对象，利用傅里叶变换红外光谱、13C核磁共振波谱及X射线光电子能谱等分析测试方法，获取了峨山褐煤的含碳、含氧及含氮结构参数。在此基础上，借助Gaussian 09计算平台，采用量子化学建模的方法构建并优化了峨山褐煤的分子结构模型。研究结果表明：峨山褐煤的芳碳率为39.20%，芳香碳结构主要为苯和萘，且芳香桥头碳与周边碳的比值χ_b为0.07；脂碳率为49.51%，脂肪碳结构主要为亚甲基，季碳和氧接脂碳；氧原子主要存在于羟基、醚氧、羰基和羧基结构中；含氮结构则以吡啶为主。基于元素分析、13C 核磁共振波谱分析，又经过热重实验消除褐煤中残余水分的影响后，计算出峨山褐煤的分子式为C₁₅₃H₁₃₇O₃₅N₂。依据分子式及分析结果计算出峨山褐煤的结构单元含量并构建出其初始结构模型，采用半经验法PM 3基组及密度泛函理论M06-2X/3-21G基组对初始分子构型进行优化。优化后的分子模型具有明显的三维立体特征，芳香环之间较为分散且在空间中排列不规则，芳香簇主要通过亚甲基、醚氧基、羰基、酯基和脂肪环连接，含氧官能团主要分布在分子边缘，脂肪族侧链较多。对优化后的分子模型进行振动频率计算进而获得了分子模型的模拟红外光谱，其与实验红外谱图吻合度良好，证明了峨山褐煤分子结构模型的准确性、合理性。分子结构模型的构建有利于直观地了解峨山褐煤的分子结构特征，从而有助于从微观分子角度研究峨山褐煤的宏观性质。同时，峨山褐煤分子结构模型可为其在热解、液化和气化等领域研究中提供理论指导。     

Circular regular dessins

Journal of Algebraic Combinatorics - A dessin is called circular if the boundary of each face is a circuit (a simple cycle). In this paper, we address the problem of characterizing edge-transitive...     

直接碳燃料电池燃料的研究进展

直接碳燃料电池(DCFC)是一种清洁高效利用碳资源发电的装置。其因能量转换率高,对环境污染小,燃料选择范围广等优点获得了越来越多的关注。DCFC的性能与使用的燃料密切相关,为了探究燃料对DCFC的影响,本文分别阐述了石墨、炭黑、中密度纤维板、生物质、煤、活性炭的特性及改性方法,分析讨论了燃料表面含氧官能团以及燃料中的金属催化剂对阳极电化学反应的促进作用,发现燃料表面化学性质要比比表面积更加重要。同时,本文也提出了对生物质这一优良的可再生资源的期待,为未来DCFC燃料的发展提供参考。     

射频等离子制备石墨烯负载Co3O4催化剂及其析氧性能研究

氢能的引入能有效提升配电网的供电可靠性,而电解水制氢是实现低碳转型的关键技术,开发高效的电解水催化剂势在必行。过渡金属氧化物储量大、催化活性高,是具有广阔应用前景的析氧反应催化剂。本文通过射频等离子体处理制备石墨烯上负载Co₃O₄析氧催化剂,XRD、Raman和XPS测试结果显示,二维结构石墨烯的引入加速表面电子迁移,增大了反应面积。等离子体处理促进了纳米粒子在石墨烯上的负载,利用等离子体刻蚀作用在催化剂表面制造出大量碳结构缺陷和氧空位结构,改善了活性位点分布,有效调控Co₃O₄电子结构,提高析氧催化活性。电化学测试表明,本文中合成的Co₃O₄@rGO在电流密度为50 mA·cm^-2时的过电位为410 mV,动力学反应速率较快,表现出优于商业IrO₂的析氧催化活性。     

Two new aromadendrane sesquiterpenes from Verticillium psalliotae

The chemical constituents of the fungus Verticillium psalliotae were studied. Two new aromadendrane sesquiterpenes inonotin M (1) and inonotin N (2) were isolated from the EtOAc extract of the fungal culture broth. The structures of compounds were elucidated mainly by HRESIMS experiments, and 1D, 2D-NMR spectroscopy analysis.     

Nukiangendines A and B,two novel 13,14-seco-abietanes from Abies nukiangensis

From Abies nukiangensis, two novel 13,14-seco-abietane diterpenes, nukiangendines A (1) and B (2) were isolated. Their relative structures were determined by extensive NMR and HRMS spectroscopic analysis. The absolute configuration of 1 was established by ECD calculations. Compound 1 showed significant anti-hepatitis C virus (HCV) effects.     

Synthesis and antifungal activity of carvacrol and thymol esters with heteroaromatic carboxylic acids

Aiming to obtain the more effective pathogen inhibitive ingredients and explore the influence of introducing different heterocyclic units to carvacrol and thymol esters, twenty ester derivatives with different heterocyclic units were synthesized. And the in vitro antifungal activity of title compounds against five plant pathogenic fungi was evaluated by mycelium growth rate method. The results showed that some carvacrol and thymol esters showed good to excellent antifungal activity, and compound 9d (4-bromo-5-isopropyl-2-methylphenyl picolinate) exhibited a broad antifungal spectrum. Preliminary study indicated that the introduction of furan, thiophene and pyridine unit could enhance the antifungal activity of carvacrol and thymol esters against Botrytis cinerea and a bromine atom on the para position of benzene moiety could enhance their antifungal activity.     

Anti-Staphylococcus aureus evaluation of gallic acid by isothermal microcalorimetry and principle component analysis

Journal of Thermal Analysis and Calorimetry - The emergence of antibiotic-resistant Staphylococcus aureus requires new antibacterial drugs. In this study, using a thermal activity monitor air...     

Transfer learning in deep neural network based under-sampled MR image reconstruction

In Magnetic Resonance Imaging (MRI), the success of deep learning-based under-sampled MR image reconstruction depends on: (i) size of the training dataset, (ii) generalization capabilities of the trained neural network. Whenever there is a mismatch between the training and testing data, there is a need to retrain the neural network from scratch with thousands of MR images obtained using the same protocol. This may not be possible in MRI as it is costly and time consuming to acquire data. In this research, a transfer learning approach i.e. end-to-end fine tuning is proposed for U-Net to address the data scarcity and generalization problems of deep learning-based MR image reconstruction. First the generalization capabilities of a pre-trained U-Net (initially trained on the human brain images of 1.5 T scanner) are assessed for: (a) MR images acquired from MRI scanners of different magnetic field strengths, (b) MR images of different anatomies and (c) MR images under-sampled by different acceleration factors. Later, end-to-end fine tuning of the pre-trained U-Net is proposed for the reconstruction of the above-mentioned MR images (i.e. (a), (b) and (c)). The results show successful reconstructions obtained from the proposed method as reflected by the Structural SIMilarity index, Root Mean Square Error, Peak Signal-to-Noise Ratio and central line profile of the reconstructed images.     

Fully Piecewise Linear Vector Optimization Problems

Journal of Optimization Theory and Applications - We distinguish two kinds of piecewise linear functions and provide an interesting representation for a piecewise linear function between two normed...