Thermodynamic interpretation of the drift and noise of gradient baselines in reversed-phase liquid chromatography using mobile phase additives

Rebirth of recycling liquid chromatography with modern chromatographic columns : Extension to gradient elution

Twin column recycling semi-preparative liquid chromatography (TCRLC) is revived to prepare small amount (∼ 1 mg) of a pure targeted compound, which cannot be isolated by conventional preparative liquid chromatography. In this work, TCRLC is extended to gradient elution. The first step of this modified process consists of a gradient step, which eliminates both early and late impurities. If not discarded, some late impurities could echo during the second isocratic recycling step of the process and compromise the purity level required for the targeted compound. Additionally, the entire gradient TCRLC (GTCRLC) process is automated regarding the eluent composition programmed and the actuation times of two valves: one two-position four-port divert valve enables to shave the targeted compound from early and late impurities during the initial gradient step. The second two-position six-port recycling valve ensures the complete baseline resolution between the band of the targeted compound and those of the closest impurities, which are not fully eliminated after the initial gradient step. The automation of the whole GTCRLC process is achieved by running four preliminary scouting gradient runs (at four different relative gradient times, t_gt₀= 2, 6, 18, and 54, where t₀ is the hold-up column time) for the accurate determination of the thermodynamics (lnk versus φ plots of the retention factor as a function of the mobile phase composition) of the first impurity, the targeted compound(s), and of the last impurity. The automated GTCRLC process was successfully applied for the isolation of a polycyclic aromatic hydrocarbon (PAH), chrysene, from a complex mixture of PAHs containing two nearly co-eluting impurities (benzo[a]anthracene and triphenylene) and nine other early/late impurities (sample volume injected: 1 mL, 7.8 mm × 150 mm Sunfire-C₁₈ column, acetonitrile/water eluent mixtures, T= 55 ^∘C, 20 cycles, baseline separation in less than two hours). Additionally, the GTCRLC process is advantageously used to isolate and baseline separate the vitamins D₂ and D₃ initially present in a milk extract mixture (0.3 mL sample injection volume, 7.8 mm × 150 mm Sunfire-C₁₈ column, methanol/water eluent mixtures, T= 65 ^∘C, 14 cycles needed in 1.5 hours). These results open promising avenues toward an effective preparation of unknown targeted compounds before further physico-chemical characterization and unambiguous identification.

Theoretical framework for mixer design for noise reduction and gradient fidelity

The mixing of two or more solvent streams to deliver a stable and accurate solvent composition is crucial to the performance, repeatability and reproducibility of a liquid chromatographic separation. We provide a theoretical treatment of axial mixing of a sequence of solvent packets with the framework of continuous stirred tank reactors (CSTRs) in series and investigate the tradeoffs presented between the primary goal of mixers (noise reduction) and it's necessary side-effects of gradient deformation and asymmetry. An experimental setup to mimic CSTR conditions was created using a stop-flow setup where the fluid flow was periodically paused and sonicated within pods of a certain volume. The effects of mixer volume relative to the volume of pump strokes and gradient volumes were investigated and discussed. A total mixer volume that is six-fold the pump stroke volume was found to be necessary to achieve sufficient (95%) noise reduction necessary for certain applications. A series of two or more CSTR elements was found to outperform a single CSTR element for larger mixer-to-pump stroke volume ratio in dampening baseline noise. For linear gradients, a gradient volume that is ten times larger than the mixer volume was found to sufficiently maintain gradient fidelity. For very small gradient volumes relative to the mixer volume, deformation of linear gradients was found to be significantly greater than predicted by the analytical solution. Furthermore, the nature of the solvent gradient deformation was asymmetric, with the latter half of the solvent gradient deforming significantly more than the first half. Combining analytically and numerically derived solutions for multiple CSTRs connected in series with experimental data, several suggestions can be made on mixer dimensions and design for a certain pump system and method transfer, given a pump stroke volume and gradient time.

Perspective on the Future Approaches to Predict Retention in Liquid Chromatography

The demand for rapid column screening, computer-assisted method development and method transfer, and unambiguous compound identification by LC/MS analyses has pushed analysts to adopt experimental protocols and software for the accurate prediction of the retention time in liquid chromatography (LC). This Perspective discusses the classical approaches used to predict retention times in LC over the last three decades and proposes future requirements to increase their accuracy. First, inverse methods for retention prediction are essentially applied during screening and gradient method optimization: a minimum number of experiments or design of experiments (DoE) is run to train and calibrate a model (either purely statistical or based on the principles and fundamentals of liquid chromatography) by a mere fitting process. They do not require the accurate knowledge of the true column hold-up volume V₀, system dwell volume V_dwell (in gradient elution), and the retention behavior (k versus the content of strong solvent φ, temperature T, pH, and ionic strength I) of the analytes. Their relative accuracy is often excellent below a few percent. Statistical methods are expected to be the most attractive to handle very complex retention behavior such as in mixed-mode chromatography (MMC). Fundamentally correct retention models accounting for the simultaneous impact of φ, I, pH, and T in MMC are needed for method development based on chromatography principles. Second, direct methods for retention prediction are ideally suited for accurate method transfer from one column/system configuration to another: these quality by design (QbD) methods are based on the fundamentals and principles of solid-liquid adsorption and gradient chromatography. No model calibration is necessary; however, they require universal conventions for the accurate determination of true retention factors (for 1 < k < 30) as a function of the experimental variables (φ, T, pH, and I) and of the true column/system parameters (V₀, V_dwell, dispersion volume, σ, and relaxation volume, τ, of the programmed gradient profile at the column inlet and gradient distortion at the column outlet). Finally, when the molecular structure of the analytes is either known or assumed, retention prediction has essentially been made on the basis of statistical approaches such as the linear solvation energy relationships (LSERs) and the quantitative structure retention relationships (QSRRs): their ability to accurately predict the retention remains limited within 10-30%. They have been combined with molecular similarity approaches (where the retention model is calibrated with compounds having structures similar to that of the targeted analytes) and artificial intelligence algorithms to further improve their accuracy below 10%. In this Perspective, it is proposed to adopt a more rigorous and fundamental approach by considering the very details of the solid-liquid adsorption process: Monte Carlo (MC) or molecular dynamics (MD) simulations are promising tools to explain and interpret retention data that are too complex to be described by either empirical or statistical retention models.

Surfing on Mobile Phase, Part I: Origins of Mobile-Phase Composition Waves and Their Effects on Detector Baselines

The most commonly used designs for modern liquid chromatography (LC) pumps produce mobile-phase streams with small short-term variations in mobile phase composition. Understanding the origin of these variations and their effects on chromatographic performance can help us develop high-performing methods. In this installment, we focus on the effect of these mobile-phase composition “waves” on detector baselines.