Institute of Chemistry, University of Tartu, Tartu, Estonia.
Rapid Commun Mass Spectrom. 2021 Nov 15;35(21):e9178. doi: 10.1002/rcm.9178.
The first comprehensive quantitative scale of the efficiency of electrospray ionization (ESI) in the positive mode by monoprotonation, containing 62 compounds, was published in 2010. Several trends were found between the compound structure and ionization efficiency (IE) but, possibly because of the limited diversity of the compounds, some questions remained. This work undertakes to align the new data with the originally published IE scale and carry out statistical analysis of the resulting more extensive and diverse data set to derive more grounded relationships and offer a possibility of predicting logIE values.
Recently, several new IE studies with numerous compounds have been conducted. In several of them, more detailed investigations of the influence of compound structure, solvent properties, or instrument settings have been conducted. IE data from these studies and results from this work were combined, and the multilinear regression method was applied to relate IE to various compound parameters.
The most comprehensive IE scale available, containing 334 compounds of highly diverse chemical nature and spanning 6 orders of magnitude of IE, has been compiled. Several useful trends were revealed.
The ESI ionization efficiency of a compound by protonation is mainly affected by three factors: basicity (expressed by pK in water), molecular size (expressed by molar volume or surface area), and hydrophobicity of the ion (expressed by charge delocalization in the ion or its partition coefficient between a water-acetonitrile mixture and hexane). The presented models can be used for tentative prediction of logIE of new compounds (under the used conditions) from parameters that can be computed using commercially available software. The root mean square error of prediction is in the range of 0.7-0.8 log units.
2010 年发表了第一个包含 62 种化合物的通过单质子化作用来衡量电喷雾电离(ESI)正离子模式效率的综合定量尺度。在化合物结构和电离效率(IE)之间发现了几种趋势,但由于化合物的多样性有限,可能仍然存在一些问题。这项工作旨在使新数据与最初发表的 IE 尺度保持一致,并对由此产生的更广泛和更多样化的数据集进行统计分析,以得出更有根据的关系,并提供预测 logIE 值的可能性。
最近,已经进行了几项具有大量化合物的新 IE 研究。在其中的一些研究中,对化合物结构、溶剂性质或仪器设置的影响进行了更详细的调查。综合了这些研究中的 IE 数据以及本工作的结果,并应用多元线性回归方法将 IE 与各种化合物参数联系起来。
编制了迄今为止最全面的 IE 尺度,其中包含 334 种具有高度化学多样性且跨越 6 个 IE 数量级的化合物。揭示了一些有用的趋势。
质子化作用下化合物的 ESI 电离效率主要受三个因素的影响:碱性(以水中的 pK 表示)、分子大小(以摩尔体积或表面积表示)和离子疏水性(以离子中的电荷离域或其在水-乙腈混合物与正己烷之间的分配系数表示)。所提出的模型可用于根据可使用商业软件计算的参数,从参数上初步预测新化合物(在使用条件下)的 logIE。预测的均方根误差在 0.7-0.8 个 log 单位范围内。