Accelerating UHPLC/HPLC Method Development and Maximizing

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In all types of modern laboratories, it is increasingly important to be able to carry out accurate and reproducible HPLC analyses with excellent turnaround time and throughput. For those laboratories that must develop, validate, and use methods over a significant period of time, or those that must transfer methods to other laboratories around the world, it is a practical business advantage to be able to develop robust and rugged methods more quickly. For both of these situations, it can be beneficial to have a diverse group of column chemistries from which one can choose and use to explore chromatographic selectivity.

Two fundamental requirements for obtaining accurate, repeatable, and reproducible results in HPLC and UHPLC are excellent peak shape and adequate resolution between the analytes of interest. Good symmetrical peak shape in reversed-phase liquid chromatography (RPLC) comes from an appropriate choice of column, mobile phase, and a variety of other parameters including sample solvent, injection volume, buffer type and strength, etc. A high-quality column made from ultrapure, low-metal-content silica can play a key role in minimizing secondary interactions of acidic and basic analytes with the stationary phase, which can lead to tailing and fronting peaks. Combining a series of stable, unique bonding chemistries with ultrapure, low acidity silica particles results in a family of RPLC columns, making the method development process faster, more comprehensive, and more effective. Moreover, by choosing shorter column geometries with smaller particle sizes such as 1.7, 2, and 3 µm particles, comprehensive method development and evaluation of the ‘chromatographic selectivity space’ for the analytes can be accomplished faster and more efficiently, thus producing robust separations using optimum combinations of organic modifier, mobile phase pH, buffer choice, and column temperature.

The Resolution Equation and Selectivity: The Most Powerful ParameterThe well-known form of the resolution equation (Equation 1) describe the relationships between column efficiency (N), retention (k), and selectivity (α, or relative retention) and their effects on resolution between peak pairs. Representing this equation as a graph, it can be simply demonstrated that a change in selectivity has the most dramatic effect on analyte resolution (Figure 1).

Equation 1. The Resolution Equation, Involving Efficiency, Retention,

and Selectivity

Where N1 and N2 are the theoretical plate counts for peaks 1 and 2, k1 and k2 are the retention factors for peaks 1 and 2, and α = k2/k1.

In Figure 1, it is clearly shown that resolution versus selectivity is nearly linear in the region between 1.00 < α < 1.25, and that for α values greater than 1.05, the effect of selectivity on resolution dominates the effects of efficiency (N) and retention (k), which show diminishing impacts at N values above 5,000 plates and k values above 5.

Figure 1. The resolution equation and the relative contributions of α,

N, and k on resolution (RS)

Accelerating UHPLC/HPLC Method Development and Maximizing Chromatographic Selectivity with Novel Stationary Phase Chemistries

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The influence of each of the three variables in the resolution equation can also be demonstrated using simulated chromatograms. Consider a 50 mm, 5 µm column that can deliver approximately 4,200 theoretical plates. Figure 2 shows the initial separation of two analytes that elute with k values of 3.0 and 3.3, respectively. These k values correspond to an α value of 1.1 and a resolution of 1.2. Subsequent figures show the results of changing k, N (consistent with a change from 5 µm to 3 µm particles for the same column geometry), and α, each by 70.8% and how those changes affect the resolution values calculated.

Figure 3 shows a simulated chromatogram that results from increasing k for both analytes by 70.8%, which produces an increase in resolution of only 8.3%.

If we now take the original separation in Figure 2 and increase N (theoretical plates) by 70.8%, we observe a 31% increase in resolution to 1.5 (Figure 4).

Finally, taking the original separation in Figure 2 and increasing selectivity, α, by 70.8% provides a dramatic increase in resolution of 575% (Figure 5), which is a significant resolution increase compared to the other values demonstrated thus far.

In conclusion, from these simulated results and for method development and analysis in general, chromatographic selectivity is the most powerful term in the resolution equation and much more effective than either retention or efficiency for maximizing analyte resolution.

Figure 2. Simulated chromatogram from a 50 mm, 5 µm column run at

1 mL/min with analytes eluting at k = 3.0 and 3.3

Figure 3. Simulated chromatogram resulting from a 70.8% increase in

retention (k)

Figure 4. Simulated chromatogram resulting from a 70.8% increase in

efficiency (N)

Figure 5. Simulated chromatogram resulting from 70.8% increase in

selectivity (α)

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LC Parameters That Impact SelectivityThere are a number of parameters that affect RPLC selectivity, and these are shown in Table 1. For isocratic separations, stationary phase, organic modifier choice, mobile phase pH (for ionizable analytes), and organic modifier percentage has the greatest effect on selectivity. Parameters that have less impact on selectivity include column temperature, choice of buffer, buffer concentration, and mobile phase additive choice and concentration. For gradient separations, all these parameters affect selectivity, in addition to gradient slope (∆% organic/min) and instrumental gradient delay volume (aka dwell volume).

Snyder and co-workers1 proposed a relative ranking for a number of these parameters in terms of their ability to change selectivity. From that research and other publications and experimentation, it’s clear that applying columns with different modes of retention/mechanisms of analyte interaction is one of the most powerful approaches for optimizing resolution in reversed-phase LC.

ACE Novel Stationary Phases: Designed for Unique Selectivity and StabilityAnalyte interactions with modern C18 stationary phases are dominated by hydrophobic interactions as the main mechanism for retention and separation. C18 is the most popular phase used across many different application areas. Other types of stationary phases with different chemical moieties such as aromatic, amide, polarizable groups, etc., have the possibility for different mechanisms of interaction such as π-π interactions,

hydrogen bonding, dipole-dipole interactions, shape selectivity, and so on. These other phases typically offer different selectivity from C18 stationary phases. By combining the hydrophobic interactions and stability of C18 phases with additional retention mechanisms from other stationary phase chemistries, there are significant advantages to the method developer. Now, it is possible to maximize chromatographic selectivity with multiple mechanisms of interaction between the analyte and stationary phase (i.e., not just hydrophobic interactions from a standard C18 phase).

Additionally, many commercially available columns such as phenyl, pentafluorophenyl, and cyano phases contain short alkyl chain spacer ligands (e.g., ethyl, propyl, and others) as part of the silane structure. These short-alkyl-chain phases exhibit reduced hydrophobicity and demonstrate increased bleed with both LC-UV and LC-MS analyses at low pH, at which RPLC method development is typically performed. With a desire to develop new stationary phase chemistries to maximize selectivity and minimize undesirable characteristics such as phase bleed, a new family of unique ACE stationary phases was designed and commercialized. These new ACE phases were engineered to maximize selectivity through multiple mechanisms of interaction (e.g., the mechanisms associated with phenyl, PFP, embedded-amide, or CN phases), but with the stability and low-bleed characteristics of a C18 phase. The first two ACE phases that were introduced were the ACE C18-AR and C18-PFP in 2009 and 2010 respectively. These two phases are single-ligand, endcapped phases, which

PARAMETER OPTIONS

Stationary phase C18/C8, Phenyl, Polar-embedded/Amide, Cyano, PFP

pH pH 1.5 to pH 7 for most columns; 1.5–11.0 for base-stable phases such as ACE SuperC18

Organic modifier CH3CN, CH3OH, CH3CN/CH3OH blend (1:1), 2-propanol, ethanol

% Organic modifier Most phases from 5–10% up to 95%; Wettable phases 0–95%

Column temperature 10 to 60 °C for most columns at low pH, typically 40 °C max for mid or high pH

Buffer choice Phosphate, formate, acetate, trifluoroacetic acid (TFA), formic acid, acetic acid

Buffer concentration Typically from 5 to 50 mM

Additive concentration For ion-pairing reagents, concentration can vary from 2 mM up to 100 mM

Gradient steepness The rate of % organic/min can be varied to adjust selectivity and resolution

Instrument delay volumeGradient delay volume can sometimes affect both retention and selectivity, especially for early-eluting components

Table 1. Parameters that can affect RPLC selectivity

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have stable, long-chain alkyl groups with either phenyl (C18-AR) or pentafluorophenyl (C18-PFP) functionalities bonded to the terminal end of the octadecyl chain. More recently, the ACE C18-Amide (embedded amide group), ACE CN-ES (cyano phase with extended alkyl spacer), and ACE SuperC18 (enhanced hydrophobicity and high pH stability) have all been introduced to further extend the ACE portfolio. All of the unique ACE phases are described along with their specifications in Table 2. Using various characterization data, it is possible to understand the relative weighting of the different mechanisms of

interaction for each ACE stationary phase (Table 3). It is clear from the data that the ACE chemistries have been designed to be complementary to each other in their mechanisms of interaction, making them ideal for exploring chromatographic selectivity during RPLC method development.

PHASE FUNCTIONAL GROUPPARTICLE

SIZES (µm)

CARBON LOAD (%)

ENDCAPPEDPH

RANGE100% AQUEOUS COMPATIBILITY*

USPCODES

C18 Octadecyl1.7, 2, 3,

5, 1015.5 Yes 2–8 No L1

C18-ARProprietary octadecyl with

terminal phenyl1.7, 2, 3,

5, 1015.5 Yes 2–8 Yes L1

C18-PFPProprietary octadecyl with

terminal PFP1.7, 2, 3,

5, 1014.3 Yes 2–8 Yes L1

C18-Amide

Polar embedded amide1.7, 2, 3,

5, 1016.4 Yes 2–8 Yes L1/L60

SuperC18Octadecyl with proprietary

encapsulation1.7, 2, 3,

5, 1014.8 Encapsulated

1.5–11.5

No L1

CN-ESCyano with extended alkyl

spacer1.7, 2, 3,

5, 1012.6 Yes 2–8 Yes L10

Table 2. Specifications for novel ACE phases

*Under isocratic conditions and gradient conditions

SEPARATION MECHANISM AND RELATIVE STRENGTH OF INTERACTION

ACE BONDED PHASE

HYDROPHOBIC INTERACTIONS

∏-∏ INTERACTIONS

DIPOLE-DIPOLE

HYDROGEN BONDING

SHAPE SELECTIVITY

ACE C18 **** – – * **

ACE SuperC18 **** – – – **

ACE C18-AR **** *** (donor) * ** ***

ACE C18-PFP **** *** (acceptor) **** *** ****

ACE C18-Amide **** – ** **** **/***

ACE CN-ES *** * *** ** *

Table 3. Separation mechanisms and relative interaction strengths for ACE phases*

*NOTE: The strength of the interactions are shown as relative strength, with four * indicating very strong, three * indicating strong, two and

one * being moderate to low, and a hyphen (–) indicating no contribution to interactions.

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ACE Phase StructuresSchematic structural diagrams of the six ACE phases are shown in Figure 6. The novel phases (C18-AR, C18-PFP, C18-Amide, and CN-ES) have the low-bleed and stability characteristics of the ACE C18 phase, but possess multiple mechanisms of interactions from the C18 chain and the additional functional group (to maximize selectivity) in a single ligand phase.

Figure 6. Schematic structural diagrams of the six novel ACE phases

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Assessing Selectivity Differences Among the Six ACE Phases

Hydrophobic Subtraction ModelHPLC stationary phases have been characterized and compared in a variety of ways, but one of the most successful and widely used approaches for phase characterization has been the Hydrophobic Subtraction Model (HSM) approach of Snyder, Dolan, and Carr2, 3. This is based upon a multi-parameter empirical model that uses a diverse set of representative analytes to characterize column selectivity. The model assumes that one can describe column selectivity based on five parameters: hydrophobicity (H), steric resistance (S), hydrogen-bond acidity (A), hydrogen-bond basicity (B), and cation-exchange capacity (C, at pH 2.8 and pH 7.0). Using a procedure beyond the scope of this white paper, a set of 16–18 test probe molecules are used to assign values for each of these parameters—including two for cation-exchange capacity (at pH 2.8 and 7.0) for each column. Using these demonstrably repeatable and reproducible parameters, it is possible to calculate the geometric distance in this 5-parameter space between any two columns, FS (Equation 2). These HSM values have been obtained for over 600 reversed phase columns, and are now maintained on an online website, www.hplccolumns.org. Table 4 shows the HSM parameters for the six ACE novel phases and their FS values relative to ACE C18. Also included are weighting factors using Equation 2 for the five parameters that are used to calculate the FS selectivity distance factors relative to ACE C18 in Table 4:

Equation 2. Selectivity function, FS, as function of five parameters in

hydrophobic subtraction model

It has been observed that columns having FS values greater than approximately 8–10 often provide useful differences in selectivity, thereby confirming the orthogonal selectivity provided by these six ACE phases.

Neue Selectivity Approach4

Another approach that has been used to assess the degree of orthogonality of selectivity between stationary phases (and also between conditions such as different organic modifiers, different pHs, etc.) uses linear gradient runs carried out using two stationary phases (same column geometry and conditions) using a diverse set of analytes. Such analyses were carried out using the six ACE phases, and the gradient retention times for a set of 41 diverse analytes obtained for each column were plotted against each other. Linear regression analyses from the plots of those respective retention times allow the calculation of the coefficient of determination (R2, a measure of the scatter about the best-fitting line between the points). From the R2 value (see example in Figure 7), one can calculate the selectivity factor, S, which is a measure of the orthogonality of the two stationary phases (or the orthogonality of two different organic modifiers or mobile phase pHs, etc., for the same stationary phase (Equation 3). In Figure 7, two different example plots

FS

BONDED PHASE

H S* A B C (pH 2.8) C (pH 7.0)EB

RETENTION FACTOR*

USP TYPE

0 ACE C18 1.000 0.02 -0.09 0.00 0.14 0.090 7.90 L1

9.9 ACE C18-AR 0.880 -0.05 -0.20 -0.01 0.07 0.150 5.70 L1

10.2ACE

SuperC180.997 -0 -0.2 -0.01 0.030 0.009 9.92 L1

17.6 Ace C18-PFP 0.899 -0.02 -0.25 -0.08 -0.001 -0.995 5.90 L1

19.3 ACE CN-ES 0.811 -0.01 -0.43 -0.01 -0.052 0.153 3.74 L10

35.2ACE C18-

Amide0.791 0.023 -0.49 0.199 -0.06 0.176 5.86 L1/L60

Table 4. Hydrophobic subtraction model parameters and FS values for six ACE columns

*Retention factor for the non-polar reference solute, ethylbenzene, which allows comparison

of overall retentivity of the various stationary phases

http://www.hplccolumns.org

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are shown: the first for two columns with very similar selectivity (e.g., C18 vs. another novel C18) and the second for a C18 phase versus a phase with a non-C18 stationary phase (e.g., phenyl, cyano, polar-embedded amide, etc.). Note that many more analytes are used for such orthogonality experiments than only four as depicted in Figure 7.

Figure 7. Example plots of gradient retention times from two

different columns to calculate selectivity factor, S

Equation 3. Neue selectivity factor, S

As an example, when the retention times of 41 analytes obtained using column A were plotted versus retention times obtained using column B, a coefficient of determination (R2) was calculated to be 0.95. The S value for that correlation and comparison of phases would be 100 x √(1 – 0.95) = 22.4. Typically, an S value for a C18 phase versus a C8 phase would be quite small and less than ~3.

The following two tables show the S values5 derived from gradient retention times obtained using acetonitrile (Table 5) and methanol (Table 6) as organic modifiers for the six ACE phases. Low S values of approximately 6–8, denote a minor, but measurable difference in selectivity. The majority of S values determined for the ACE phases are significantly greater than this—again confirming that the six ACE phases offer significant and useful differences in selectivity. Furthermore, while the magnitude of the S values are substantial for changes in stationary phase alone, significant differences in selectivity are also observed when using either methanol or acetonitrile as the organic modifier.

Further analysis of the S value data (see reference 5) has confirmed this and demonstrates that acetonitrile and

ACETONITRILE

C18 C18-AR C18-PFP CN-ES C18-Amide SuperC18

C18 X 13 11 15 20 9

C18-AR X 8 17 26 11

C18-PFP X 13 23 9

CN-ES X 17 16

C18-Amide X 24

SuperC18 X

Table 5. S values for the six ACE phases using acetonitrile as organic modifier

METHANOL

C18 C18-AR C18-PFP CN-ES C18-Amide SuperC18

C18 X 19 15 25 18 7

C18-AR X 15 27 28 18

C18-PFP X 20 19 15

CN-ES X 19 23

C18-Amide X 20

SuperC18 X

Table 6. S values for the six ACE phases using methanol as organic modifier

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methanol provide significantly different selectivity for all six ACE phases. It is therefore highly recommended that the effects of both column stationary phase and organic modifier on selectivity be investigated during method development.

Use of Different Column Chemistries to Maximize Selectivity for Method Development and AnalysesThere are many different approaches that can be taken when developing a new HPLC or UHPLC separation, which are usually dictated by the complexity of the sample to be analyzed, and by how often, and for how long it is expected that the resulting method will be used (Figure 8).

Figure 8. Method development strategy depends on sample

complexity

For a relatively simple sample that contains six or fewer analytes, it is usually possible to perform a series of isocratic or gradient experiments using a single stationary phase with a single organic modifier and a low pH aqueous eluent to obtain a satisfactory result with adequate resolution and peak shape. If peak shape, retention, or resolution is inadequate, one can make systematic changes to one or more conditions; for example, to evaluate a different stationary phase chemistry (e.g., an amide or aromatic phase), different eluent pH (low or mid pH), or a different organic modifier (e.g., CH3OH vs. CH3CN). Such an approach may not be sufficient for more complex samples, or, if a more thorough, systematic stationary phase and mobile phase conditions screen were needed (e.g., for examples in Figure 9 and Figure 10). In this example, a triple API + related substances mixture (17 analytes in total) have been systematically screened using the six column chemistries plus two solvents systematic approach. The different conditions provided useful information on analyte retention behavior and elution orders. While no combination of chemistry and solvent resolved all 17 analytes from each other using the screen, various options (e.g., C18 in CH3OH, C18-AR in CH3OH, and

C18-Amide in CH3OH or CH3CN) provided resolution of up to 11 of 14 impurity peaks. With further optimization of the gradient and as a worked example, it has been shown that full separation of all 17 analytes in the mixture is possible using the C18-Amide in MeOH6.

1. 2-Aminophenol, 2. Hydroquinone, 3. Theobromine, 4. Paracetamol, 5. Theophylline, 6. Paraxanthine, 7. 4-Hydroxybenzoic acid, 8. Caffeine, 9. 2-Acetamidophenol, 10. 2-Hydroxybenzoic acid, 11. Phenol, 12. Aspirin, 13. 4-Nitrophenol, 14. 4-Chloroacetanilide, 15. 2-Nitrophenol, 16. Acetylsalicylic acid, 17. Salsalate

Figure 9. Comparison of chromatograms

obtained using six novel ACE phases with acetonitrile

as organic modifier in a column screening experiment

1. 2-Aminophenol, 2. Hydroquinone, 3. Theobromine, 4. Paracetamol, 5. Theophylline, 6. Paraxanthine, 7. 4-Hydroxybenzoic acid, 8. Caffeine, 9. 2-Acetamidophenol, 10. 2-Hydroxybenzoic acid, 11. Phenol, 12. Aspirin, 13. 4-Nitrophenol, 14. 4-Chloroacetanilide, 15. 2-Nitrophenol, 16. Acetylsalicylic acid, 17. Salsalate

Figure 10. Comparison of chromatograms obtained using six novel

ACE phases with methanol as organic modifier in a column screening

experiment

Conditions: Column: ACE Excel 2 μm, 100 x 3.0 mm Flow rate: 1.2 mL/min. Mobile Phase A1: 10 mM ammonium formate, pH 3.0Mobile Phase B1: 10 mM ammonium formate, pH 3.0 in MeCN:H2O 9:1 (v/v) Mobile Phase B2: 10 mM ammonium formate, pH 3.0 in MeOH:H2O 9:1 (v/v) Gradient: 5-95% B in 5 mins.

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With more complex samples that contain 10–25 or more analytes (e.g., pharmaceutical related substance methods, clinical methods, or environmental methods), a prudent approach may involve screening multiple parameters. Such a systematic approach may include the comparison of stationary phases having different retention mechanisms, as well as an evaluation of the impact of organic modifier choice, aqueous component pH, and choice of buffer used to control eluent pH values.

This type of systematic strategy is portrayed by the two rightmost boxes in Figure 8, in which multiple stationary phases, two or more pHs, and two organic modifiers are used to explore the selectivity space for a complex separation during method development.

As an additional worked example, Figures 11–14 show the results from gradient screening experiments performed for a mixture of 15 acidic, neutral, and basic analytes (Table 7) using the six ACE phases (using 2.1 x 50 mm, 2 μm columns, 4–80% organic in 8 min, 0.5 mL/min, 35 °C), both CH3CN and CH3OH as organic modifiers, and for two different mobile phase pH values (pH 2.75 and 4.75) using potassium phosphate and ammonium acetate respectively. Note that the number of peaks observed for each combination of

stationary phase, organic modifier, and pH is highlighted for each chromatogram.

Each chromatogram is annotated with the number of visible peaks. Some considerations for pursuing further method development and optimization might include, but is not limited to:

• Total number of peaks• USP tailing factors for all or most important analytes• Mean or median resolution for all peaks • Limiting resolution (minimum) between poorest

separated peak pair

The most promising combinations for further method development and optimization were:

• C18-Amide, C18-AR, and SuperC18 with CH3CN at pH 2.75

• SuperC18 and C18-PFP with CH3OH at pH 2.75• C18-PFP, C18-AR, and C18-Amide with CH3CN at pH 4.75• SuperC18 and C18-AR with CH3OH at pH 4.75

From these results, it is clear that the use of a diverse set of stationary phases (with their different mechanisms of interaction), along with different organic modifiers

ANALYTE LOG P PKA TYPE

lomefloxacin -0.39 5.64 Base

ranitidine 0.98 2.7, 8.2 Base

acebutolol 1.53 9.2 Base

alprenolol 2.69 9.5 Base

lidocaine 2.84 7.8 Base

pyrilamine maleate 3.04 9 Base

trazodone 3.13 N.A. Base

diphenhydramine 3.65 9 Base

acetylsalicylic acid 1.24 3.5 Acid

coumaric acid 1.83 4 Acid

naproxen sodium 2.99 4.15 Acid

hesperidin -0.31 10 Neutral

naringin -0.16 > 10 Neutral

prednisolone 1.27 12.6 Neutral

prednisone 1.66 N.A. Neutral

Table 7. Mixture of 15 acidic, basic, and neutral analytes

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Figure 11. Six ACE

phases compared

using CH3CN at

pH 2.75

Figure 12. Six ACE

phases compared

using CH3OH at

pH 2.75

Screening Conditions for 2 pHs and 2 Organic Modifiers for Figures 11-14: Columns:

ACE 2.1 x 50 mm, 2 μm

Mobile Phase A1: 20 mM

KH2PO4, pH 2.75

Mobile Phase A2: 20 mM

NH4OAc, pH 4.75

Mobile Phase B1: CH3CN

Mobile Phase B2: CH3OH

Flow Rate: 0.5 mL/min.

Temperature: 35 °C

Gradient: 4-80% in 8 mins.

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Figure 13. Six ACE

phases compared

using CH3CN at

pH 4.75

Figure 14. Six ACE

phases compared

using CH3OH at

pH 4.75

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and aqueous mobile phase buffers (set at two different pH values) may be helpful to explore selectivity. Such a systematic approach allows a careful examination and comparison of the various combinations, which can allow the selection of one or more combinations that can be optimized further.

Faster Stationary Phase and Mobile Phase Screening for Method Development

With the availability of these unique phase chemistries in both 1.7 and 2 µm particle sizes, it is possible to screen various combinations of stationary phase, organic modifier, and mobile phase pH much faster—even without automated method development instrumentation and software. For example, using the six phases in a 2.1 x 50 mm, 1.7 µm, or 2 µm ACE Excel column geometry, with both acetonitrile and methanol modifiers, at one temperature and one pH, it would take approximately 7.4 hours to screen those columns and conditions. It is assumed that a flow rate of 0.5 mL/min, an 8-minute gradient time from 2% to 95% organic modifier (k* = 6, Equation 4), five column volumes, and two delay volumes of equilibration, and duplicate injections would be carried out (Table 8 and Table 9). By taking a different, blocked approach with a single stationary phase, two different organic modifiers and two different pH values, a single stationary phase screening could be completed in less than three hours in an automated fashion (Table 10).

Equation 4. Gradient retention factor, k*

Where tG is gradient time in min, F is flow rate in mL/min, VM is the column volume in mL, ∆ɸ is the organic modifier gradient range (%B final – %B initial), expressed as a fraction, and S is a constant for a given analyte under a given set of conditions. S is a function of the analyte molecular weight and is also the slope of the plot of ln k versus ɸ for that analyte.

S 6

POROSITY 0.6

LENGTH 50 mm

ID 2.1 mm

VM 0.10 mL

VD 0.50 mL

FLOW RATE 0.50 mL/min

tG 8.00 min

K* 6.00

∆ɸ 93

EQUILIB TIME 3.04 min

TIME %B

0.00 2

8.00 95

9.04 95

9.24 2

12.28 2

Table 8. Gradient analysis conditions for screening six ACE phases

(2.1 x 50 mm, 1.7 µm, or 2 µm columns)

Note: VD is delay volume, VM is column void volume

SummaryThe six unique ACE stationary phase chemistries make it convenient and effective to compare various combinations of stationary phase, organic modifier, and pH as part of a comprehensive method development strategy. Moreover, these six stationary phases make it practical to substitute one phase for another under any given set of conditions to help determine whether there are analytes present that had not been separated using a single stationary phase. All ACE phases are made using the same ultrahigh purity, low-acidity silica particles for which ACE columns have long been known and sought. ACE columns have a long history of delivering excellent peak shape, dependable reproducibility, and enduring quality and performance. These six phases are available in 1.7, 2, 3, and 5 µm particle sizes in hardware that can be used for HPLC or UHPLC up to a pressure limit of 1,000 bar (14,500 psi). Superior peak shape, excellent batch-to-batch reproducibility, novel chemistries, and high efficiency performance make ACE columns an excellent choice for your LC analyses and method development needed.

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Table 10. Example instrument injection sequence for screening one stationary phase, two organic modifiers, and two mobile phase pHs

Table 9. Example instrument injection sequence for screening six ACE phases, two organic modifiers, and a single mobile phase pH

SEQ. LINE

METHOD NAME # INJECTIONS TIME PER INJ. TOTAL TIME ELAPSED

TIME (MIN)ELAPSED

TIME (HRS)

1 Blank Gradient ACN_pH_2.8 1 12.3 12.3 12.3 0.2

2 C18_ACN_pH_2.8 2 12.3 24.6 36.8 0.6


4 C18_AR_ACN_2.8 2 12.3 24.6 73.7 1.2


6 C18-PFP_ACN_2.8 2 12.3 24.6 110.5 1.8


8 SuperC18_ACN_2.8 2 12.3 24.6 147.3 2.5


10 C18_Amide_ACN_2.8 2 12.3 24.6 184.1 3.1


12 CN-ES_ACN_2.8 2 12.3 24.6 221 3.7

13 Blank Gradient MeOH_pH_2.8 1 12.3 12.3 233.2 3.9

14 C18_MeOH_pH_2.8 2 12.3 24.6 257.8 4.3


16 C18-AR_MeOH_2.8 2 12.3 24.6 294.6 4.9


18 C18-PFP_MeOH_2.8 2 12.3 24.6 331.4 5.5


20 SuperC18_MeOH_2.8 2 12.3 24.6 368.3 6.1


22 C18-Amide_MeOH_2.8 2 12.3 24.6 405.1 6.8


24 CN-ES_MeOH_2.8 2 12.3 24.6 441.9 7.4

SEQ. LINE

METHOD NAME # INJECTIONS TIME PER INJ. TOTAL TIME ELAPSED

TIME (MIN)ELAPSED

TIME (HRS)


2 C18_ACN_pH_2.8 2 12.3 24.6 36.8 0.6


3 C18_MeOH_pH_2.8 2 12.3 24.6 73.7 1.2

3 Blank Gradient CAN_pH_3.8 1 12.3 12.3 85.9 1.4

4 C18_ACN_pH_3.8 2 12.3 24.6 110.5 1.8


5 C18_MeOH_pH_3.8 2 12.3 24.6 147.3 2.5

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References1. “Orthogonal” separations for reversed-phase liquid

chromatography; Journal of Chromatography A, 1101 (2006) 122–135; J. Pellett, P. Lukulay, Y. Mao, W. Bowen, R. Reed, M. Ma, R.C. Munger, J.W. Dolan, L. Wrisley, K. Medwid, N.P. Toltl, C.C. Chan, M. Skibic, K. Biswas, K.A. Wells, L.R. Snyder.

2. A fast, convenient and rugged procedure for characterizing the selectivity of alkyl-silica columns; Journal of Chromatography A, 1057 (2004) 49–57; L.R. Snyder, A. Maule, A. Heebsh, R. Cuellar, S. Paulson, J. Carrano, L. Wrisley, C.C. Chan, N. Pearson, J.W. Dolan, J.J. Gilroy.

3. Column selectivity in reversed-phase liquid chromatography I. A general quantitative relationship; Journal of Chromatography A, 961 (2002) 171–193; N.S. Wilson, M.D. Nelson, J.W. Dolan, L.R. Snyder , R.G. Wolcott, P.W. Carr.

4. Selectivity in reversed-phase separations, Influence of the stationary phase; Journal of Chromatography A, 1127 (2006) 161–174. U.D. Neue, J.E. O’Gara, A. Méndez.

5. Maximization of Selectivity in Reversed-Phase Liquid Chromatographic Method Development Strategies; LCGC Europe, January (2016) 8–21. M.R. Euerby, M. Fever, J. Hulse, M. James, P. Petersson, C. Pipe.

6. Streamlined RPLC Method Development Using a Combined Column Screening and Software Optimisation Approach, presented as a webinar by Alan P McKeown, https://uk.vwr.com/store/content/externalContentPage.jsp?path=/uk.vwr.com/en_GB/webinar4_software_modelling.jsp

Documents

Accelerating UHPLC/HPLC Method Development and Maximizing