|Abstract Title:||Strategies to Optimize Throughput in 2D-LC|
|Presenter Name:||Dr Monika Dittmann|
|Co-authors:||Dr Konstantin Shoykhet|
Dr Stephan Buckenmaier
|Session Choice:||High Throughput versus High Efficiency Separations (CS)|
Abstract Information :
Two-dimensional liquid chromatography (2D-LC) is becoming a routine technology as more and more instrument solutions are becoming commercially available from instrument vendors.
Although 2D-LC is inherently a high-resolution technique, it is still worthwhile to consider possibilities to optimize the analysis time required to achieve a given separation goal. This optimization should mostly aim at increasing the throughput of the 2nd dimension analysis as this determines the total separation time.
The main operation modes in 2D-LC are full-comprehensive 2D-LC for very complex samples, heart cutting and multiple heart cutting (selective comprehensive) for samples of low to medium complexity.
The optimization strategy for full-comprehensive operation aims mostly at achieving a desired peak capacity in the shortest time possible. In this context, the gradient kinetic plot model (KPM) can be employed to find the best column dimensions and operating conditions for the 2nd dimension (particle size, column length and ID, flow rate, temperature etc.). The model takes limiting factors such as maximum operating pressure or flow rate into account. An extended version of the KPM also includes the impact of external band-broadening that limits the use of very short, narrow columns.
Another means to improve peak capacity per times the use of shifted gradients, i.e. adjustment of 2nd D gradient start and end conditions depending on the sampling point within the 1stD gradient.
If the sample is only of limited complexity or if only certain parts of the 1D chromatogram are of interest, selective comprehensive 2D-LC can be used to achieve the desired separation in a shorter time by analyzing only the areas of interest. In this case the optimization might aim more at achieving certain selectivities (through selection of stationary phases, 2D eluent, gradient range and slope) than aiming exclusively at optimizing peak capacity. Here a combination of kinetic optimization and retention modeling can be useful.
The authors will discuss the different optimization strategies and the factors limiting speed in multidimensional analysis.