Ultrahigh-Pressure Reversed-Phase Liquid Chromatography in Packed Capillary Columns

MacNair, John E.; Lewis, Kenneth C.; Jorgenson, James W.

doi:10.1021/ac961094r

Cited by 564 publications

(409 citation statements)

References 17 publications

(21 reference statements)

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“…The isocratic retention of a solute (ln k') is assumed to be a linear function of the volume fraction of the organic modifier (ϕ): (12) where S is the slope of ln k' versus ϕ and ln k' w is the extrapolated solute retention in pure water. Using LSST theory, the retention time (t R ) in linear gradient elution is given as: 24 (13) where t 0 is the column dead time, t D is the system dwell time, k' 0 is the isocratic retention factor in the initial eluent of the gradient. The gradient steepness (b) is defined as: (14) where F is the flow rate (mL/min), and t G is the gradient time, V m is the column dead volume (mL) and ϕinitial and ϕfinal are the initial and final eluent compositions of the gradient, respectively.…”

Section: Prediction Of Retention Timesmentioning

confidence: 99%

Peak Capacity Optimization of Peptide Separations in Reversed-Phase Gradient Elution Chromatography: Fixed Column Format

et al. 2006

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The optimization of peak capacity in gradient elution RPLC is essential for the separation of multicomponent samples such as those encountered in proteomic research. In this work we study the effect of gradient time (t G ), flow rate (F), temperature (T) and final eluent strength (ϕ final ) on the peak capacity of separations of peptides that are representative of the range in peptides found in a tryptic digest. We find that there are very strong interactions between the individual variables (e.g. flow rate and gradient time) which make the optimization quite complicated. On a given column, one should first set the gradient time to the longest tolerable and then set the temperature to the highest achievable with the instrument. Next, the flow rate should be optimized using a reasonable but arbitrary value of ϕ final . Last, the final eluent strength should be adjusted so that the last solute elutes as close as possible to the gradient time. We also develop an easily implemented, highly efficient and effective Monte Carlo search strategy to simultaneously optimize all the variables. We find that gradient steepness is an important parameter that influences peak capacity and an optimum range of gradient steepness exists in which the peak capacity is maximized.

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Section: Prediction Of Retention Timesmentioning

confidence: 99%

Peak Capacity Optimization of Peptide Separations in Reversed-Phase Gradient Elution Chromatography: Fixed Column Format

et al. 2006

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“…The heat generation, or power dissipation, is the product of pressure drop (DP) and flow rate (F) as reported in Eq. (1.6) (MacNair et al, 1997).…”

Section: The Changes In Solvent Properties With Pressurementioning

confidence: 99%

“…The first remarkable separation performed at a pressure up to 4100 bar in a fused-silica capillary column (52 cm length, 30 mm I.D., 1.5 mm nonporous particles) was described in 1997 by Jorgenson et al For an analysis time of less than 10 min, a number of plates in the range of 140,000e190,000 were obtained with small molecules (MacNair et al, 1997). In 2003, the upper pressure limit of their system was extended and the particle size decreased to obtain outstanding chromatographic performance.…”

Section: Figure 13mentioning

confidence: 99%

Theory and Practice of UHPLC and UHPLC–MS

Guillarme

Veuthey

2017

Handbook of Advanced Chromatography /Mass Spectrometry Techniques

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“…32,33 The group achieved a peak capacity of ∼300 over a 30 minute gradient by operating a 30 µm i.d. fused-silica capillary packed with 1 µm nonporous silica particles at 130 kpsi.…”

Section: Emerging Technologiesmentioning

confidence: 99%

Multidimensional LC Separations in Shotgun Proteomics

2008

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Two modes of separation coupled with MS enable researchers to study complicated biological structures.Proteins are the molecular products of genes and play a central role in many biological processes. Protein expression occurs as a function of cellular and environmental conditions, and consequently proteins are expressed at different times and under different conditions. Insight is thus garnered by determining how protein expression has changed in a biological context.Over the past two decades, MS has become an important tool for the analysis of proteins. 1,2 One current method for the analysis of protein mixtures is proteolytic digestion followed by LC/MS/ MS. Once hard-to-handle proteins are converted into chemically well-behaved peptides, the approach overcomes many difficulties associated with protein mixture analysis. 3 Tandem MS has been particularly effective because the data can be directly used to identify peptides and subsequently infer which proteins (digested or undigested) are in the mixture. 4 This type of approach for the analysis of protein mixtures is often referred to as "shotgun" proteomics ( Figure 1).Because increasingly complicated biological structures are studied by tandem MS, the need for more powerful and highly resolving separation methods has grown. Consequently, the development and use of multidimensional LC (MDLC) in proteomics has thrived over the past few years. MDLC combines two or more forms of LC to increase the peak capacity, and thus the resolving power, of separations to better fractionate peptides prior to entering the mass spectrometer.High-resolution separation serves two functions in combination with MS. It better resolves peptides differing in charge and hydrophobicity to minimize ion suppression and improve ionization efficiency, and it simplifies the complexity of peptide ions entering the mass spectrometer to minimize undersampling. This last aspect is important because the tandem MS process is driven by data-dependent data acquisition and has a finite cycle time. A higher peak capacity and better resolving power improve the acquisition of data and can lead to a better representation of the proteins in the mixture. Improved resolution also results in a larger dynamic range.Although MDLC is by no means a new concept and has a long history, it has been enjoying a renaissance in proteomics. 5 In this article, we will provide an overview of the background, important applications, and future prospects for MDLC; in particular, we will focus on applications involving peptides. (To listen to a podcast about this feature, please go to the Analytical Chemistry website at pubs.acs.org/ac.)

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Ultrahigh-Pressure Reversed-Phase Liquid Chromatography in Packed Capillary Columns

Cited by 564 publications

References 17 publications

Peak Capacity Optimization of Peptide Separations in Reversed-Phase Gradient Elution Chromatography: Fixed Column Format

Peak Capacity Optimization of Peptide Separations in Reversed-Phase Gradient Elution Chromatography: Fixed Column Format

Theory and Practice of UHPLC and UHPLC–MS

Multidimensional LC Separations in Shotgun Proteomics

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