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Likelihood of total resolution in liquid chromatography: Evaluation of one-dimensional, comprehensive two-dimensional, and selective comprehensive two-dimensional liquid chromatography

机译:液相色谱法中总分离度的可能性:一维,全面二维和选择性全面二维液相色谱法的评估

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Computer simulations of three methods of liquid chromatography (LC) are developed to understand better the conditions under which each method is superior to the others. The methods are one-dimensional LC (1D-LC), comprehensive two-dimensional LC (LC x LC), and selective comprehensive two-dimensional LC (sLC x LC). The criterion by which superiority is measured in this case is the probability that all peaks in a given sample are separated by a resolution equaling or exceeding unity. A point-process model is developed for the simulation of sLC x LC to complement existing models for 1D-LC and LC x LC. In the sLC x LC model, first-dimension singlet peaks remain in that dimension, and first-dimension multiplets, or clusters of overlapping peaks, are transferred to the second dimension for further separation during the interval of time between successive multiplets. Criteria are developed for the success or failure of multiplet transfer. The three LC methods are simulated for peak numbers ranging from 2 to 50 and analysis times ranging from 10 to 1200 s, using peak capacities that reflect the performance of modern instrumentation. The probability computations predict the experimental finding that LC × LC is superior to 1D-LC at long times (over 210 or so seconds) but is inferior at shorter times due to the broadening of first-dimension peaks by sampling. In general, sLC × LC is predicted to be superior to LC × LC for samples with less than 40 peaks separated using three samples or less per multiplet. Conversely, LC × LC is predicted to be superior to sLC × LC for samples containing more than 40 peaks and when sLC × LC separations are carried out with six samples per multiplet. We find that the analysis time required to attain a 50% probability of total resolution is always predicted to be shorter for sLC × LC than for 1D-LC, and 30-75% shorter than for LC × LC when 20 or so peaks are separated. Finally, in light of the substantial predicted time savings for sLC × LC analyses the computations are interpreted relative to practical concerns, e.g., retention-time shifts, to establish good working conditions (e.g., the number of samples per multiplet) for future experimental studies of sLC × LC.
机译:开发了三种液相色谱(LC)方法的计算机模拟,以更好地了解每种方法优于其他方法的条件。这些方法是一维LC(1D-LC),全面二维LC(LC x LC)和选择性全面二维LC(sLC x LC)。在这种情况下,测量优劣的标准是给定样本中所有峰被等于或超过1的分辨率分开的概率。开发了用于模拟sLC x LC的点过程模型,以补充现有的1D-LC和LC x LC模型。在sLC x LC模型中,第一维多重峰保留在该维中,并且第一维多重峰或重叠峰的簇被转移到第二维,以便在连续多重之间的时间间隔内进一步分离。为多重传输成功或失败制定了标准。使用反映现代仪器性能的峰容量,对三种液相色谱方法进行了仿真,其峰数范围为2至50,分析时间为10至1200 s。概率计算可预测实验结果,即LC×LC在较长时间(超过210秒钟左右)上优于一维LC,但在较短时间上却较差,这是由于采样导致的一维峰变宽。通常,对于每3份以3个或更少的样品分离的少于40个峰的样品,sLC×LC预计将优于LC×LC。相反,对于包含40个以上峰的样品,以及每多峰分析六个样品进行sLC×LC分离时,预计LC×LC优于sLC×LC。我们发现,总的分离率达到50%的概率所需的分析时间总是被预测为sLC×LC比1D-LC短,而当分离20个左右的峰时,其分析时间比LC×LC短30-75%。 。最后,鉴于sLC×LC分析预计可节省大量时间,因此将计算相对于实际问题(例如保留时间偏移)进行解释,以建立良好的工作条件(例如每多峰样品数量)以供将来的实验研究sLC×LC。

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