Preface

IF 1 Q4 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS
L. Rodríguez-Tembleque, J. Sanz-Herrera, M. Aliabadi
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引用次数: 0

Abstract

Proton Nuclear Magnetic Resonance (1H NMR) is a science to study the relationship between the energy level transitions of hydrogen nuclei in organic compounds under the action of magnetic field and the chemical environment in molecules. The NMR data of organic compounds mainly include the chemical shift (resonance frequency), the number of hydrogen atoms, the peak shape (peak splitting), and the coupling constant of hydrogen functional groups, which are closely related to the structure of organic compounds. In the process of conducting structural identification of organic compounds, we deeply feel that although we have understood the basic principle of nuclearmagnetic resonance phenomenonand the basic theory of chemical shift, peak integral area, spin coupling and spin splitting, coupling constant and so on, if there is no perceptual knowledge about nuclear magnetic resonance of various hydrogen functional groups in organic compounds, it is not enough to help us to analyze the structure of organic compounds skillfully. In addition, in the short 30 years from 1990s to now, with the theory and technology of NMR and computer science becomingmore and more mature, the research on the structure of organic compounds has tended to be micro, fast, and accurate, which greatly shortens the research period of natural organic compounds. On the basis of the development and wide application of separation and purification technology of natural organic compounds represented by conventional chromatography and preparative liquid chromatography, a large number of natural organic compounds with relatively complex structures have been identified, and the NMR signals of these compounds have been fully assigned, thus accumulating a large number of spectral data of natural organic compounds. These data are very important for researchers engaged in the research of organic chemistry (including natural organic chemistry), because they not only help to simplify the structural identification of known compounds obtained in organic chemistry research, but also can be used as an important reference in the structural identification of new similar compounds and even novel compounds. Natural organic chemistry is a basic subject to study the organic composition, structure, and change law of natural biological resources. It has been playing an important role in the research of organic chemistry, pharmaceutical chemistry, biochemistry, botany, and other disciplines, as well as the development of pharmaceutical industry and pesticide industry. For example, through the application of various natural organic compounds separation and purification methods and modern organic structure identification methods, tens of thousands of plant secondary metabolites have been identified in the field of phytochemistry, which not only greatly enriched the structure and types of organic compounds, but also proved that many components have significant physiological activities, or play an important role in the process of plant life. But, the chemical composition ofmanynatural plant resources has not been fully elucidated, and the novel structure still attracts the majority of phytochemical
前言
质子核磁共振(1H NMR)是研究有机化合物中氢原子核在磁场作用下的能级跃迁与分子中化学环境之间关系的科学。有机化合物的核磁共振数据主要包括化学位移(共振频率)、氢原子数、峰形状(峰分裂)和氢官能团的耦合常数,这些与有机化合物的结构密切相关。在对有机化合物进行结构鉴定的过程中,我们深切地感受到,尽管我们已经了解了核磁共振现象的基本原理和化学位移、峰积分面积、自旋耦合和自旋分裂、耦合常数等基本理论,如果没有对有机化合物中各种氢官能团的核磁共振的感性认识,就不足以帮助我们熟练地分析有机化合物的结构。此外,在20世纪90年代至今的短短30年里,随着核磁共振和计算机科学的理论和技术越来越成熟,对有机化合物结构的研究趋于微观、快速、准确,大大缩短了天然有机化合物的研究周期。在以常规色谱法和制备液相色谱法为代表的天然有机化合物分离纯化技术的发展和广泛应用的基础上,鉴定了大量结构相对复杂的天然有机物,并对这些化合物的NMR信号进行了全面的归属,从而积累了大量的天然有机化合物的光谱数据。这些数据对于从事有机化学(包括天然有机化学)研究的研究人员来说非常重要,因为它们不仅有助于简化有机化学研究中获得的已知化合物的结构鉴定,而且可以作为新的类似化合物甚至新化合物结构鉴定的重要参考。天然有机化学是研究自然生物资源的有机组成、结构和变化规律的基础学科。它在有机化学、药物化学、生物化学、植物学等学科的研究以及制药工业和农药工业的发展中发挥了重要作用。例如,通过应用各种天然有机化合物的分离纯化方法和现代有机结构鉴定方法,在植物化学领域鉴定了数以万计的植物次生代谢产物,不仅极大地丰富了有机化合物的结构和类型,而且也证明了许多成分具有显著的生理活性,或在植物生命过程中发挥着重要作用。但是,许多天然植物资源的化学成分尚未完全阐明,而且这种新的结构仍然吸引着大多数植物化学物质
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来源期刊
Journal of Multiscale Modelling
Journal of Multiscale Modelling MATHEMATICS, INTERDISCIPLINARY APPLICATIONS-
CiteScore
2.70
自引率
0.00%
发文量
9
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