Waters Corporation, Instrument/Core Research/Fundamental, 34 Maple Street, Milford, MA, 01757, USA.
Department of Chemistry, Philipps-Universität Marburg, Hans-Meerwein-Strasse 4, Marburg 35032, Germany.
J Chromatogr A. 2022 Dec 6;1685:463627. doi: 10.1016/j.chroma.2022.463627. Epub 2022 Nov 3.
An alternative method to the classical fit of semi-empirical, statistical, or artificial intelligence-based models to retention data is proposed to predict surface excess adsorption and retention factors in liquid chromatography. The approach is based on a fundamental, microscopic description of the liquid-to-solid adsorption of analytes taking place at the interface between a bulk liquid phase and a solid surface. Molecular dynamics (MD) simulations are performed at T=300 K in a 100 Å wide slit-pore model (β-cristobalite-C surface in contact with an acetonitrile/water mobile phase) to quantify a priori the retention factors of small molecules expected in reversed phase liquid chromatography (RPLC). Uracil is chosen as the reference "non-retained" marker, whereas benzyl alcohol, acetophenone, benzene, and ethylbenzene are four selected retained, neutral compounds. The MD simulations allow to determine the pore-level density profiles of these five compounds, i.e., the variation of the analyte concentration as a function of distance from the silica surface. The retention factors of the retained analytes are expressed using their respective calculated surface excess adsorption relative to uracil. By definition, the retention factors are proportional to the surface excess adsorbed and the proportionality constant is directly scaled to the retention time of the "non-retained" marker. Experimentally, a 4.6 mm × 150 mm RPLC-C column packed with 5 μm 100 Å High Strength Silica (HSS)-C particles is used and the retention times of these five compounds are measured. The volume fraction of acetonitrile in water increases from 20 to 90% generating a wide range of retention factors from 0.15 to 183 at T=300 K. The results demonstrate very good agreement between the MD-predicted surface excess adsorption data and measured retention factors (R> 0.985). A systematic error is observed as the proportionality constant is not exactly scaled to the retention time of uracil. This is most likely caused by the differences between the chemical and morphological features of the slit-pore model adopted in the MD simulations and those of the actual HSS-C particles: the average surface coverage with C chains, the geometry of the mesopores, and the pore size distribution. Specifically, the impact on RPLC retention of slight, local variations in surface chemistry (e.g., functional group density and uniformity) and how this aspect is affected by the pore space morphology (e.g., pore curvature and size) is worth investigating by future MD simulations.
提出了一种替代经典的半经验、统计或基于人工智能的模型拟合方法,用于预测液相色谱中表面过剩吸附和保留因子。该方法基于在本体液相和固体表面之间的界面处发生的分析物的液体到固体吸附的基本微观描述。在 T=300 K 下,在 100 Å 宽的狭缝孔模型(β-方石英-C 表面与乙腈/水流动相接触)中进行分子动力学(MD)模拟,以定量预测反相液相色谱(RPLC)中预期的小分子保留因子。尿嘧啶被选为参考“非保留”标记,而苄醇、苯乙酮、苯和乙苯则是四个选择的保留、中性化合物。MD 模拟允许确定这五种化合物的孔级密度分布,即分析物浓度随距离硅表面的变化。保留因子通过相对于尿嘧啶计算的各自表面过剩吸附来表示。根据定义,保留因子与表面过剩吸附成正比,比例常数直接与“非保留”标记的保留时间成正比。在实验中,使用 4.6 mm×150 mm 的 RPLC-C 柱,柱内填充 5 µm 100 Å 高强度硅胶(HSS)-C 颗粒,并测量这五种化合物的保留时间。水相中的乙腈体积分数从 20%增加到 90%,在 T=300 K 时产生了从 0.15 到 183 的广泛保留因子范围。结果表明,MD 预测的表面过剩吸附数据与测量的保留因子之间非常吻合(R>0.985)。观察到系统误差,因为比例常数与尿嘧啶的保留时间不完全成比例。这很可能是由于 MD 模拟中采用的狭缝孔模型的化学和形态特征与实际 HSS-C 颗粒的差异造成的:C 链的平均表面覆盖率、中孔的几何形状和孔径分布。具体而言,表面化学的细微局部变化(例如官能团密度和均匀性)以及该方面如何受到孔空间形态(例如孔曲率和尺寸)的影响,值得通过未来的 MD 模拟进行研究。