This project is about finding the optimal configuration in two-dimensional liquid chromatography systems. What does it mean? If you are in the bussiness of HPLCxHPLC, you probably have wondered about what should be the optimal value of the column lengths (in the first and second dimension), modulation times, column diameters, particle sizes, flow-rates, etc. Which combination of these parameters is giving the best performance?
The aim is to find the right configuration. Note that it is a “system optimization” method. This means that we “don’t care” about the retention of the compounds (we don’t know the compounds), but we set up the system configuration attending to maximise the peak capacity and minimize the analysis time. As we are contemplating two objectives (peak capacity and time), we can optimize it using different methods. In a first approximation, we treated the problem starting from the so-called “Poppe-plots” [17a] (a methodology honoring prof. Hans Poppe, from University of Amsterdam). Later we improved the method and approached the problem via Pareto optimization [32a]. In this project, we could be aiming for a three-objective optimisation, namely (i) (maximum) peak capacity, (ii) (minimum) analysis time and (iii) (minimum) dilution. This is one of the key slides (which looks like a Poppe-plot, but it is in fact a 3D Pareto front):
One of the highlights of this research was the finding that the optimal modulation time corresponds to having the peaks in the first dimension modulated only 1.5 or 2 times (opposed to the accepted value of 4 modulations per peak). This is in consonance to what other researches had found.
A side aspect of the project was to apply a simmilar methodology to the optimization of monolithic columns (in collaboration with the Free University of Brussels and Thermo) [19a]. A later project covered the aspects of gradient optimization considering the time spent in second-dimension column recovery [31a]. Recently, the methodology was extended to the Pareto optimization of two-dimensional spatial chromatography [36a] and to the optimization of three-dimensional spatial chromatography [37a]. The idea behind these last two pieces of research was to investigate the potential of spatial chromatography (2D and 3D) in terms of their speed and the peak capacity they generate. Spatial n-dimensional chromatography proved to be superior to time n-dimensional chromatography.
This project was developed at the University of Amsterdam. Several people were involved. See authors of the publications for more details about authorship.
DSM, University of Amsterdam.
“TwoDimensionalSO” is a software module performing pareto optimization on HPLCxHPLC systems (basically, it does what is described in ref. [32a]).
To download the software instructions:
To install the software in your computer. First you have to download and install the Matlab common runtime component (MCR):
Second, you download and execute the software component:
An example file is provided here below: