Modeling Differential Mobility from First Principles

该研究项目将处理离子在电场拖曳下通过气体的速度-取决于离子本身，气体的成分和电场强度。所谓的离子迁移率的场依赖性被用于差分迁移率光谱法（DMS）（分析化学中的工具）中，以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意义的。质谱学会30，2711（2019））已经揭示了重要的参数：离子和背景气体之间的相互作用显示出特征场依赖性。此外，背景气体粒子可以结合到离子（成簇）。然后，结合/未结合离子的部分再次是场依赖量。通过精心计算，实现这些效应，我们能够定性地重现实验数据。这已经提高了我们对差别流动性的基本理解。然而，由于我们引入了许多简化和假设，尚未达成定量协议。本项目的目标是更详细地研究这些简化，如果可能的话，用更精细的方法取代它们。通过这种方式，旨在实现定量预测能力。特别是，我们将1）开发一个更好的模型来描述集群部分，2）考虑快速变化场的过程动力学（如DMS中所应用的）和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。这不仅将增加对发生的过程的基本理解，从而有助于解释实验数据，还没有被理解;但它也是一个目标，开发一个工具，人们可以用来设计他们的实验任务。因此，将制定一个方案，并在社区分发。

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.