An excerpt from LCGC's e-learning tutorial on column selection for RP-HPLC at
There are many factors that influence the performance of a high performance liquid chromatography (HPLC) stationary phase,
of which the chemical nature of the bonded phase ligand is important, but by no means all encompassing. Minor manufacturing
parameters such as the method of electropolishing the internal surface of the column can also have an effect on the selectivity
and efficiency produced by a particular column.
Few of us have time to study each individual parameter (of which there are hundreds) and assess their interactive effects
on the selectivity of our stationary phases. We need readily accessible measures of column performance to identify similar
or orthogonal chemistries to those we are currently using, or to gain an insight into which column types might work for particular
Several attempts have been made to produce a "definitive" set of chemical probes to best characterize the huge number of stationary
phases available (well over 1000 different types are currently available). As yet a harmonized set of test probes and methodologies
has not been identified, however three, independent, publicly available databases of HPLC columns exist today:
Figure 1: Schematic representations of the five interactions described by the hydrophobic subtraction model (adapted from
The PQRI database is the best populated, with 588 columns, and is a very useful tool to aid HPLC column selection. A description
of the Tanaka test probes is given below to help understand the various classifications, with the analogous PQRI test probes
indicated in parentheses. It should be noted that the PQRI classification uses different chemical probes to the Tanaka (now
ACD) database but the results describe a similar chemical behaviour.
- Retention factor, k
PB (not tested in the PQRI Classification), describes the hydrophobic retention demonstrated by the column measured using the
retention of pentylbenzene.
- Hydrophobic selectivity, αCH2 (H), the retention factor ratio (selectivity) between pentylbenzene and butylbenzene reflects the ability of the phase to separate
compounds that differ by only a single methylene group. Column hydrophobicity (H) increases with an increase in total carbon. Endcapping, because of its low (<10%) contribution to the overall carbon load
has little effect on retentivity.
- Shape selectivity, αT/0 (S*), describes the ability of the phase to discriminate between planar structures (triphenylene) and those with greater spatial
(hydrodynamic) volume (o-terphenyl). Column steric interactions increase as the bonded phase ligands move closer together on the silica surface (increased
bonded phase chain length or concentration of the bonded phase) and for packings with narrow pore sizes, and has a significant
effect on column selectivity, especially for molecules of different shapes.
- Hydrogen bonding capacity, αC/P (A and B), is a measure of the retention factor ratio (selectivity) between caffeine and phenol and describes the columns ability
to hydrogen bond with a solute. The PQRI database further characterizes hydrogen bonding capacity into hydrogen-bond acidity
(A), the ability for non-ionized silanols to interact with bases and hydrogen bond basicity (B), the ability for surface and bonded-phase species to further interact with acidic analyte features.
- Total ion-exchange capacity, αB/P pH 7.6 (C 7.6), is the selectivity between benzylamine and phenol at a mobile phase pH of 7.6 and reflects the total silanol activity
of the column, affecting peak shape and selectivity for polar and ionizable analytes.
- Acidic ion-exchange capacity, αB/P pH 2.7 (C 2.7), is measured using the retention factor ratio between benzylamine and phenol at pH 2.7 and reflects the likelihood of
peak tailing when analysing bases at low eluent pH. The magnitude of the difference between ion-exchange tests indicates the
ability to discriminate between polar analytes while maintaining good peak shape.
Most of these groups have used chemometric approaches to produce quantitative comparisons between column characteristics based
on principal component analysis (PCA) or tools to visualize the relative groupings of commercially available columns according
to their key descriptors.
References available in the on-line edition: