Thin-Layer Chromatography Gradient Optimization Strategy for Wet Load Adsorption Flash Chromatography

色谱法 化学 吸附 洗脱 模拟移动床 二维色谱法 柱色谱法 梯度洗脱 分辨率(逻辑) 高效液相色谱法 计算机科学 人工智能 有机化学
作者
Paweł Kręcisz,Kamila Czarnecka,Paweł Szymański
出处
期刊:Journal of Chromatographic Science [Oxford University Press]
卷期号:60 (5): 472-477
标识
DOI:10.1093/chromsci/bmab097
摘要

Chromatography is one of the most popular methods for the separation of compounds in modern pharmaceutical industry and science. Despite the extensive use of the reversed phase chromatography in analytical and preparative applications, the normal phase adsorption chromatography has a special place in purifying post-reaction mixtures or the separation of natural extracts, especially in wet load mode, because of simplicity and high velocity of preparation. Complex mixtures, more difficult to separate, require gradient methods to obtain better results of separations. These methods can be developed by external software, but the automatic methods are often not very accurate and the negative impact of wet load application on separation quality is considerable in them. Therefore, we present the thin-layer chromatography (TLC) gradient optimization strategy for wet load separations to obtain repeatable results of separations for different compounds without worrying about negative impact of wet loading on separation quality. The strategy provides information about an elution model of desired compound, which is used to develop the gradient method. The strategy also allows to standardize the separation length, because gradient methods performed by the TLC gradient optimization strategy have a very similar duration time in column volumes. The method can also be simply scaled because of using the column volume as a base unit in calculations.
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