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Linearized Equations for the Reduced Ion Mobilities of Polar Aliphatic Organic Compounds

机译:极性脂肪族有机化合物离子迁移率降低的线性化方程

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Over the years, ion mobility spectrometry has evolved into a powerful technique for rapid identification of analytes in very complex sample matrixes such as human breath. Every analyte detected has a characteristic ion mobility value (and a retention time when additional preseparation techniques are employed) which is used to identify the peaks in a spectrum either by comparison with reference analytes or by simultaneous mass spectrometric measurements. In this study, the mass-mobility correlations between compounds in three different homologous series are used to predict the mobilities of the other substances in the same series in a medium of synthetic air. The results show a very high accuracy (>99.5percent) of the prognosis. The linear trend equations of ion mobilities, as a function of the number of carbon atoms, obtained from the different series were then generalized into one linear equation for the reduced ion mobility for the polar aliphatic compounds and is validated by comparing it with the traditional Mason-Schamp equation. To compare the empirical equation obtained from the prognosis and the Mason-Schamp equation, the collision integral term in the latter was split into two terms to linearize it. The resulting novel ion mobility equation could be the starting step to completely describe the relationship between ion collision integral and the ion mobility for polar aliphatic compounds. The splitting of the collision integral into two terms will also give new inputs to describe the various ion models and the different forces that act on the ions and the neutral gas molecules upon which the collision integral is dependent on. This prognosis method could, furthermore, be extended to all other classes of organic compounds and could serve as a useful tool for identification of unknowns in ion mobility spectra, thereby considerably reducing the time-consuming and costly reference measurements and other coupling techniques that are currently employed.
机译:多年来,离子迁移谱已发展成为一种强大的技术,可以快速识别非常复杂的样品基质(例如人的呼吸)中的分析物。检测到的每种分析物都有一个特征性的离子迁移率值(以及采用其他预分离技术时的保留时间),该值可通过与参考分析物进行比较或同时进行质谱测量来鉴定光谱中的峰。在这项研究中,使用三个不同同源序列中的化合物之间的质量迁移率相关性来预测合成空气介质中同一序列中其他物质的迁移率。结果显示预后非常高(> 99.5%)。然后将从不同系列获得的离子迁移率随碳原子数变化的线性趋势方程式概括为一个线性方程式,用于降低极性脂族化合物的离子迁移率,并将其与传统的梅森方法进行比较进行验证-Schamp方程。为了比较从预后获得的经验方程式和Mason-Schamp方程式,将后者中的碰撞积分项分解为两个项以使其线性化。由此产生的新颖的离子迁移率方程可能是完整描述离子碰撞积分与极性脂族化合物离子迁移率之间关系的开始步骤。将碰撞积分分为两个术语也将提供新的输入,以描述各种离子模型以及作用在离子和碰撞积分所依赖的中性气体分子上的不同力。此外,该预后方法可以扩展到所有其他类别的有机化合物,并且可以用作识别离子迁移谱图中未知物的有用工具,从而大大减少了费时且昂贵的参考测量以及当前其他的耦合技术受雇。

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