As an analytical scientist working on HPLC we often focus on selecting the right stationary phase which plays a crucial role in HPLC method development but in this article we will try to explore the importance of selecting the appropriate column dimensions.
Key parameters to consider include column length, pore size, particle size, internal diameter, and carbon loading. This article provides a detailed guide to selecting these parameters based on the nature of the analyte and the specific requirements of the analysis.
1) Particle size:
Particle size is the average particle size of the packing in the HPLC column.The standard particle size for HPLC columns was 5 µm for a long time, until the mid-1990s, when 3.5 µm became popular for method development. More recently, as higher speed and/or higher resolution is required, chromatographers have turned to packings with sub-2-3 µm, including 1.8 µm.
The relationship between particle size and resolution is inversely proportional. Smaller particle sizes lead to higher resolution but also increase the column's back pressure.
Now we know the smaller the particle size of the HPLC column higher the resolution but why is it so?
This is just because of the below scientific principles of chromatography.
A) Increased Surface Area
Greater Surface Area: Smaller particles have a larger surface area per unit volume compared to larger particles. This increased surface area allows for more interactions between the stationary phase and the analytes, enhancing the theoretical plate counts and resolution.
B) Reduced Eddy Diffusion
Eddy Diffusion: If you know about the Van Demeter equation you must know about Eddy diffusion. Eddy diffusion, a component of band broadening, is caused by the multiple flow paths that analyte molecules can take through the packed bed of particles. Smaller particles reduce the variation in these flow paths and reduce band broadening due to eddy diffusion and helps in improving resolution.
2) Column length:
Doubling the column length generally doubles the plate number and analysis time, enhancing resolution. However, longer columns also increase back pressure linearly.
If column length doubles, the plate number and analysis time also double. As column length increases, back pressure increases linearly. For example, a 2.1 x 100 mm column packed with 3.5 µm particles generates about 12,000-14,000 theoretical plates, an efficiency that can provide adequate separation for many samples. By reducing the particle size from 3.5 µm to 1.8 µm, the efficiency of the same 2.1 x 100 mm column is doubled to about 24,000 theoretical plates. However, this column generates a back pressure that is four times greater than the pressure of the same size column filled with 3.5 µm particles. Very often, an efficiency of 24,000 plates is not required, so the column length can be halved to 50 mm, with an expected efficiency of 12,000 plates.
So the conclusion here is that the shorter column reduces analysis time and solvent consumption but lowers the resolution as compared to longer columns on the opposite side longer columns provide higher resolution and theoretical plates as compared to shorter columns but with increased back pressure.
C) Internal diameter:
Internal diameter of column directly affect the solvent consumption and sensitivity. Reducing the column dimensions often results in high sensitivity and low solvent consumption. Narrower the internal diameter of column (2.1 to 3.0) mm often offer higher sensitivity faster analysis time but low sample loading capacity in the contrary wider internal diameter (4.6 to 10.0)mm provide higher sample loading capacity but may reduce sensitivity and resolution.
Even smaller columns are often less expensive to buy. In some cases, if the column diameter is reduced by half, sensitivity increases by four to five times (assuming the injection mass is kept constant). For example, when the same amount of the same sample is injected onto a 2.1 mm id column, the peaks are about three to five times higher than on an optimized LC than when the same amount of sample is injected onto a 4.6 mm id column.
3) Pore size:
Pore size is the average size of a pore in a porous packing. Its value is typically expressed in angstroms. The pore size determines whether a molecule can diffuse into and out of the packing. Therefore, the pore size of the packing material in your HPLC column plays an important role, since the molecules must 'fit' into the porous structure in order to interact with the stationary phase. Smaller pore size packings (pore size 80 to 120Å) are best for small molecules with molecular weights up to 2000. For larger molecules with MW over 2000, wider pore packings are required; for example, a popular pore size for proteins is 300Å. For polypeptides and many proteins, choose 200-450 Å, and choose 1,000Å and 4,000Å for very high molecular weight proteins and vaccines.
4) Carbon loading:
Carbon Load refers to the % carbon content of the silica bonded stationary phase. Generally speaking, a high carbon load (example 18-25%) results in a more hydrophobic surface. The surface is also more resistant to high pH.
High carbon loading in the column means high hydrophobic and increased retention of the non polar analytes therefore the columns with high carbon loading are good for the separation of nonpolar and mid polar compound while the column with low carbon loading retained polar components and good for separation of polar analytes.
Typical examples of such columns with carbon loading are as below
- Zorbax C18 about 24%
- X bridge schied RP C18 about 17%
- X bridge Schied BEH about 18%
Conclusion:
By understanding and optimizing these parameters—particle size, pore size, column length and carbon loading—analytical scientists can tailor their HPLC methods to achieve the desired balance between resolution, efficiency, and analysis time. Whether working with complex mixtures or routine analyses, the careful selection of column characteristics ensures robust, reproducible, and high-quality chromatographic separations.
Ultimately, the successful application of HPLC in various fields, from pharmaceuticals to environmental analysis, relies on making informed decisions about column selection based on a thorough understanding of these critical factors.
