Metal-organic framework materials (MOFs) are a class of microporous and crystalline materials with great potential for adsorption-based separations. Harnessing the separation ability of these materials in high-performance liquid chromatography (HPLC) requires the use of small and uniform particles in order to achieve good column packing. The well-known MOF material [Cu 3 (BTC) 2 ] is, however, typically synthesized as a polydisperse mixture due to its nucleation

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The present contribution addresses the problem of the accurate determination of the extra-column band-broadening dispersion by applying the method of moments (MOM) on pulse response experiments. The MOM provides the only mathematically rigorous way to determine the variance of a pulse response, but suffers from the fact that the obtained variance values usually depend very strongly and unpredictably on the selected integration boundaries. In the present study, it is investigated whether the MOM cannot be made more accurate by repeating the pulse response experiment a number of times, then add all measured peaks (after retention time alignment) and subsequently perform the integration on this summed peak. Testing this approach for a number of different integration boundary detection methods consistently more accurate results are obtained than when the different repeat response pulses are integrated individually and the variances are averaged afterwards. It was also found that adding five repeats already leads to a significant improvement of the variance estimate, whereas the addition of 10-20 repeats is needed before the variance estimate converges to a steady value.

ABSTRACT:We report on the possibilities of a new method development (MD) algorithm that searches the chromatographic parameter space by systematically shifting and stretching the elution window over different parts of the time-axis. In this way, the search automatically focuses on the most promising areas of the solution space. Since only the retention properties of the first and last eluting compounds of the sample need to be (approximately) known, the algorithm can be directly applied to samples with unknown composition, and the proposed solutions are not sensitive to any modeling errors. The search efficiency of the algorithm has been evaluated on an extensive set of random-generated in silico samples covering a broad range of different retention properties. Compared to a pure grid-based search, the algorithm could reduce the number of missed components by 50% and more. The algorithm has also been applied to solve three different real-world separation problems from the pharmaceutical industry. All problems could be successfully solved in a very short time (order of 12 h of instrument time).O ne of the most time-consuming tasks in analytical liquid chromatography is method development (MD). This is the search for the chromatographic operating conditions (type of organic modifier and stationary phase, temperature, gradient profile, pH, ionic strength, etc.) leading to the complete resolution of the sample in all its individual constituents. 1−9 Because of the high probability for peak overlap 10 and the sensitive dependency of the retention time of the individual analytes on, for example, the employed gradient slope, the MD process still involves a lot of trial and error and can take up to several weeks of work.To speed up this work, fully or semiautomated MD software has been developed over the years. 10−16 Roughly spoken, the automated MD strategies described in literature are either search-based (e.g., using the Simplex method) 17 or modelbased (e.g., Drylab, 18 Chromsword). 19 In the present study, the properties of a hybrid method, further referred to as the predictive elution window stretching and shifting method (PEWS 2 ), were investigated. This method explores the chromatographic parameter space in a pure search-mode but also uses the information on the retention properties of some of the peaks (e.g., the first and the last peak of the chromatogram and/or the first and last peaks of its most problematic zone). This information is used to make a model-based prediction of the gradient slope (expressed here as βt 0 = (ϕ e − ϕ 0 )·t 0 /t G ) and initial gradient composition ϕ 0 values that should be imposed to shift and stretch the elution window of the sample in a controlled manner over different parts of the time-axis. Hence, instead of optimizing the gradient by searching in the (ϕ 0 ,βt 0 )-space, the PEWS 2 -method searches directly in the (k first ,k last )-space. Since the values of k first and k last directly determine how wide the elution window is and whether the components elute early or late, t...