Effect of first-dimension undersampling on effective peak capacity in comprehensive two-dimensional separations.

Area of Science:

• Analytical Chemistry
• Separation Science

Background:

• Comprehensive two-dimensional (2D) separations offer high peak capacity but can be limited by undersampling in the first dimension.
• Undersampling can lead to peak broadening and inaccurate theoretical peak capacity calculations.

Purpose of the Study:

• To develop a method for correcting the theoretical maximum peak capacity in 2D separations.
• To quantify the impact of undersampling the first dimension on peak width.
• To establish a predictive model for peak broadening in 2D separations.

Main Methods:

• Simulations of comprehensive 2D separations with randomly distributed sample constituents.
• Application of 2D statistical overlap theory to determine effective first-dimension peak width.
• Using the number of observed peaks as a performance metric for effective peak width determination.

Main Results:

• A simple function, = sqrt(1+0.21(t(s)/1sigma)^2), was derived to describe the ratio of effective first-dimension peak width after sampling to its original width.
• This model is valid for 2D separations with random or weakly correlated retention times (0.2 <= t(s)/1sigma <= 16).
• The derived expression predicts up to 35% more first-dimension peak broadening compared to previous models.

Conclusions:

• The developed expression provides a realistic correction for theoretical 2D peak capacities.
• This work offers a valuable tool for optimizing comprehensive 2D separation methods.
• Accurate estimation of undersampling effects is crucial for maximizing the performance of 2D separations.