Structural characterization of ions using differential mobility spectrometry (DMS) and spectroscopy - implications for modeling the DMS process

差分迁移率光谱法（DMS）是一种新兴的动力工具，用于快速增长的数量和各种各样的应用，涉及离子检测和表征。与制药工业、医学研究和环境监测有关的分析实验室现在都采用DMS技术。DMS通过带电化学物质在动态电场条件下通过惰性气体的迁移率来表征带电化学物质。离子的分离是基于它们的质荷比、形状以及它们与碰撞池中气体的相互作用。仪器的灵敏度通常在十亿分之几到万亿分之几的范围内。最近，我们与SCIEX的研究人员合作，证明了动态聚类和去聚类是DMS与其他分离技术相比所显示的增强分离的根源。此外，我们已经证明，仪器条件可以调整，使被探测的物种将表现出气相或溶液相的属性，和离子的clusteringpropensity强烈相关的凝聚相属性。这项工作是与SCIEX合作进行的，它将为DMS分离和隔离的分子离子提供明确的结构表征。在这里，我们将表征各种化合物的DMS行为，同时用高分辨率激光光谱探测这些物质。这些研究也将得到高级量子化学计算的支持。我们获得的实验和计算结果将用于构建我们将应用机器学习的数据库。这项工作将提供一个深刻的，基本的描述动态集群环境中的DMS仪器，并将提供一个明确的图片之间的关系的离子的结构和它的物理化学性质在气相和凝聚相，这些信息是至关重要的发展DMS为基础的物理化学性质的测定。

Differential Mobility Spectrometry, or DMS, is emerging as a power tool for use in a rapidly growing numberand variety of applications involving ion detection and characterization. Analytical laboratories associated withthe pharmaceutical industry, medical research, and environmental monitoring are all now employing DMStechnology. DMS characterizes charged chemical species by their mobility through an inert gas under dynamicelectric field conditions. Ions are separated based on their mass-to-charge ratios, their shapes, and how theyinteract with the gas in the collision cell. Instrument sensitivities are typically on the parts-per-billion toparts-per-trillion range. Recently, in collaboration with researchers at SCIEX, we demonstrated that dynamicclustering and de-clustering lies at the origin or the enhanced separation displayed by DMS in comparison withother separation techniques. Moreover, we have demonstrated that instrument conditions can be tuned such thatthe species being probed will exhibit either gas-phase or solution-phase properties, and that the clusteringpropensities of ions correlate strongly with their condensed phase properties. This work, which is conducted inpartnership with SCIEX, will provide an unambiguous structural characterization of the molecular ions whichare separated and isolated by DMS.Here, we will characterize the DMS behaviour of a variety of compounds while simultaneously probing thesespecies with high-resolution laser spectroscopy. These studies will also be supported by high-level quantumchemical calculations. The experimental and computational results that we obtain will be used to build adatabase to which we will apply machine learning. This work will provide a deep, fundamental description ofthe dynamic clustering environment in the DMS instrument, and will provide an unambiguous picture of therelationship between an ion's structure and its physicochemical properties in the gas- and condense-phases.This information is critical for the development for DMS-based physicochemical properties assays.