Modeling Differential Mobility from First Principles

这个研究项目将研究离子在电场的牵引下穿过气体的速度，这取决于离子本身、气体的成分和电场强度。所谓离子迁移率的场依赖性在差分迁移率光谱（DMS）中被使用，DMS是分析化学中的一种工具，用于分离分析物分子的复杂混合物。对导致分离的过程的基本理解是非常有趣的。之前的工作(特别是j.a m。Soc。质谱，30,2711(2019))已经揭示了重要的参数：离子与背景气体之间的相互作用表现出特征场依赖性。此外，背景气体粒子可以与离子结合（聚集）。结合/未结合离子的部分又是一个与场相关的量。通过精细的计算，实现这些效应，我们能够定性地再现实验数据。这已经提高了我们对微分流动性的基本理解。然而，由于我们引入了许多简化和假设，还没有达成定量的一致。这个项目的目标是更详细地研究这些简化，如果可能的话，用更复杂的方法代替它们。有了这个，定量预测能力的目标是实现。特别是，我们将1)开发一个更好的模型来描述簇部分，2)考虑快速变化领域（如DMS中应用的）过程的动力学，3)将我们的模型扩展到更复杂但广泛使用的分析物分子，如肽或蛋白质。这不仅会增加对正在发生的过程的基本理解，从而有助于解释尚未理解的实验数据；但开发一种工具也是一个目标，人们可以用它来为给定的任务设计实验。因此，将开发一个程序并在社区中分发。

This research project will deal with the speed with which ions can move through a gas when dragged by an electrical field - in dependence of the ion itself, the composition of the gas and the electrical field strength. The field dependence of the so called ion mobility is used in Differential Mobility Spectrometry (DMS), a tool in analytical chemistry, to separate complex mixtures of analyte molecules. A fundamental understanding of the processes leading to separation is of great interest.Preceding work (in particular J. Am. Soc. Mass Spectrom. 30, 2711 (2019)) already revealed important parameters: The interaction between the ion and the background gas shows a characteristic field dependence. Additionally, the background gas particles can bind to the ion (clustering). The portion of bound/unbound ions is then again a field dependent quantity. Using elaborate calculations, implementing these effects, we were able to qualitatively reproduce experimental data. This already improved our fundamental understanding of differential mobility. However, because we introduced many simplifications and assumptions, a quantitative agreement was not yet achieved.It is the goal of this project to study these simplifications in greater detail and, if possible, replace them by more elaborate methods. With this, a quantitative prediction capability is aimed to achieve. In particular, we will 1) develop a better model for the description of the cluster portion, 2) consider the dynamics of the processes for fast varying fields (as applied in DMS) and 3) expand our model to more complicated, but widely used analyte molecules such as peptide or proteins.This will not only increase the fundamental understanding of the processes happening and will thus help to interpret experimental data, not yet understood; but it is also a goal to develop a tool, people can use to to design their experiments for the task given. Consequently, a program will be developed and distributed in the community.