Ion Mobility (IM) coupled to Mass Spectrometry (MS) has been used for several decades, bringing a fast separation dimension to the MS detection. IM-MS is a convenient tool for structural elucidation. The folding of macromolecules is often assessed with the support of computational chemistry. However, this strategy is strongly dependent on computational initial guesses. Here, we propose the analysis of the Collision Cross-Section (CCS) trends of synthetic homopolymers based on a fitting method which does not rely on computational chemistry a prioris of the three-dimensional structures. The CCS trends were evaluated as a function of the polymer chain length and the charge state. This method is also applicable to mobility trends. It leads to two parameters containing all information available through IM(-MS) measurements. One parameter can be interpreted as an apparent density. The second parameter is related to the shape of the ions and leads us to introduce the concept of trends with constant apparent density. Based on the two fitting parameters, a method for IM trend predictions is elaborated. Experimental deviations from the predictions facilitate detecting structural rearrangements and three-dimensional structure differences of the cationized polymer ions. This leads for instance to an easy identification and prediction of the presence of different polymer topologies in complex polymer mixtures. The classification of predicted trends could as well allow for software-assisted data processing. Finally, we suggest the link between the CCS trends of homopolymers and those obtained from (monodisperse) biomolecules to interpret potential folding differences during IM-MS studies.

中文翻译：

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使用表观密度概念预测聚合物的离子迁移率-质谱趋势

离子淌度 (IM) 与质谱 (MS) 联用已经使用了几十年，为 MS 检测带来了快速分离维度。IM-MS 是一种方便的结构解析工具。大分子的折叠通常在计算化学的支持下进行评估。然而，这种策略强烈依赖于计算初始猜测。在这里，我们建议基于拟合方法分析合成均聚物的碰撞截面 (CCS) 趋势，该方法不依赖于三维结构的先验计算化学。CCS 趋势被评估为聚合物链长和电荷状态的函数。这种方法也适用于流动性趋势。它导致两个参数包含通过 IM(-MS) 测量可用的所有信息。一个参数可以解释为表观密度。第二个参数与离子的形状有关，并引导我们引入具有恒定表观密度的趋势的概念。基于这两个拟合参数，阐述了一种IM趋势预测方法。与预测的实验偏差有助于检测阳离子化聚合物离子的结构重排和三维结构差异。这导致例如在复杂聚合物混合物中容易识别和预测不同聚合物拓扑结构的存在。预测趋势的分类也可以允许软件辅助数据处理。最后，