Predicting peak shape in capillary zone electrophoresis: a generic approach to parametrizing peaks using the Haarhoff-Van der Linde (HVL) function

Citation
Gl. Erny et al., Predicting peak shape in capillary zone electrophoresis: a generic approach to parametrizing peaks using the Haarhoff-Van der Linde (HVL) function, ANALYT CHEM, 73(20), 2001, pp. 4862-4872
Citations number
31
Categorie Soggetti
Chemistry & Analysis","Spectroscopy /Instrumentation/Analytical Sciences
Journal title
ANALYTICAL CHEMISTRY
ISSN journal
00032700 → ACNP
Volume
73
Issue
20
Year of publication
2001
Pages
4862 - 4872
Database
ISI
SICI code
0003-2700(20011015)73:20<4862:PPSICZ>2.0.ZU;2-O
Abstract
We have found that the Haarhoff-Van der linde (HVL) peak function provides excellent fitting to the shapes of CZE peaks. Initially designed for overlo aded peaks in gas chromatography, this function describes a Gaussian peak w hen there is no peak distortion, and a triangular peak when there is no dif fusional peak broadening. As such, it is ideal for CZE peaks distorted by e lectromigration dispersion (EMD). Fitting peaks with this function gives fo ur parameters: three of them can be related to the Gaussian peak that would have been obtained in case of no EMD; the last one is a measure of the pea k distortion. Using moving boundary theory, this peak distortion parameter may readily be expressed in terms of analyte and background electrolyte mob ilities and concentrations, electric field, and sample injection length. Th e variance of an HVL peak is shown to be described by a universal function, and a master equation is presented. The region where EMD adds less than 10 % to the Gaussian variance is shown to be very narrowly spread around the m obility matching condition. Under typical CZE operating conditions with an analyte at 1% of the BGE concentration, significant peak distortion is alwa ys present. Because the total peak variance is not an addition of the Gauss ian and triangular contributions, the HVL model and the methodology introdu ced here should always be used to correctly combine variances.