University of Pittsburgh, Chevron Science Center, 219 Parkman Avenue, Pittsburgh, PA, 15260, United States.
J Chromatogr A. 2019 Mar 29;1589:73-82. doi: 10.1016/j.chroma.2018.12.055. Epub 2018 Dec 26.
Predicting retention and enthalpy allows for the simulation and optimization of advanced chromatographic techniques including gradient separations, temperature-assisted solute focusing, multidimensional liquid chromatography, and solvent focusing. In this paper we explore the fits of three expressions for retention as a function of mobile phase composition and temperature to retention data of 101 small molecules in reversed phase liquid chromatography. The three retention equations investigated are those by Neue and Kuss (NK) and two different equations by Pappa-Louisi et al., one based on a partition model (PL-P) and one based on an adsorption model (PL-A). More than 25 000 retention factors were determined for 101 small molecules under various mobile phase and temperature conditions. The pure experimental uncertainty is very small, approximately 0.22% uncertainty in retention factors measured on the same day (2.1% when performed on different days). Each of the three equations for ln(k) was fit to the experimental data based on a least-squares approach and the results were analyzed using lack-of-fit residuals. The PL-A model, while complex, gives the best overall fits. In addition to examining the equations' adequacy for retention, we also examined their use for apparent retention enthalpy. This enthalpy can be predicted by taking the derivative of these expressions with respect to the inverse of absolute temperature. The numerical values of the fitted parameters based on retention data can then be used to predict retention enthalpy. These enthalpy predictions were compared to those obtained from a modified van 't Hoff equation that included a quadratic term in inverse temperature. Based on analysis of 1 211 van 't Hoff plots (solute-mobile phase-day combinations), ninety-eight percent showed a significantly better fit when using the modified van 't Hoff expression, justifying its use to provide apparent enthalpies as a function of mobile phase composition and temperature. The foregoing apparent enthalpies were compared to the apparent enthalpies predicted by the three models. The PL-A model, which contains a temperature dependent enthalpy, provided the best enthalpy prediction. However, there is virtually no correlation between the overall lack of fit to experimental ln(k) for each model and the corresponding lack of fit of the linear (in 1/T) van 't Hoff expression. Thus, the temperature-dependent enthalpy is apparently not the cause of a model's ability to fit ln(k) as a function of mobile phase composition and temperature. The value in these expressions is their ability to predict chromatograms, allowing for optimization of an advanced chromatographic technique. The two simpler models NK and PL-P, which do not contain a temperature dependent enthalpy, have their merits in modelling retention (NK being the better of the two) and enthalpy (PL-P being the better of the two) if a simpler expression is required for a given application.
预测保留和焓允许模拟和优化先进的色谱技术,包括梯度分离、温度辅助溶质聚焦、多维液相色谱和溶剂聚焦。在本文中,我们探索了三种作为流动相组成和温度函数的保留表达式对反相液相色谱中小分子 101 个保留数据的拟合。研究的三种保留方程是 Neue 和 Kuss (NK) 的方程和 Pappa-Louisi 等人的两个不同方程,一个基于分配模型 (PL-P),另一个基于吸附模型 (PL-A)。在各种流动相和温度条件下,为 101 个小分子确定了超过 25000 个保留因子。纯实验不确定度非常小,同一天测量的保留因子的不确定度约为 0.22%(不同天测量时为 2.1%)。根据最小二乘法,将这三个关于 ln(k)的方程分别拟合到实验数据中,并使用残差分析结果。PL-A 模型虽然复杂,但总体拟合效果最好。除了检查这些方程在保留方面的适用性外,我们还检查了它们在表观保留焓方面的适用性。通过对这些表达式相对于绝对温度倒数的导数,可以预测该焓。然后可以使用基于保留数据的拟合参数数值来预测保留焓。将这些焓预测值与从包括逆温度二次项的修改的范特霍夫方程中获得的值进行了比较。基于对 1211 个范特霍夫图(溶质-流动相-天组合)的分析,98%的情况下,当使用修改后的范特霍夫方程时,拟合效果显著更好,证明其可用于提供作为流动相组成和温度函数的表观焓。将上述表观焓与三个模型预测的表观焓进行了比较。PL-A 模型包含一个与温度相关的焓,提供了最好的焓预测。然而,对于每个模型,实验 ln(k)的总体不拟合与线性(在 1/T)范特霍夫表达式的不拟合之间几乎没有相关性。因此,温度相关焓显然不是模型拟合作为流动相组成和温度函数的 ln(k)的能力的原因。这些表达式的价值在于它们能够预测色谱图,从而允许优化先进的色谱技术。两个更简单的模型 NK 和 PL-P,它们不包含与温度相关的焓,如果给定应用程序需要更简单的表达式,则在建模保留(NK 是两个中更好的一个)和焓(PL-P 是两个中更好的一个)方面具有其优点。