Comprehensive two-dimensional gas chromatography thermodynamic modeling and selectivity evaluation for the separation of polychlorinated dibenzo-p-dioxins and dibenzofurans in fish tissue matrix

Relation between characteristic temperature and elution temperature in temperature programmed gas chromatography - Part II: Influence of column properties

The method development process in gas chromatography can be accelerated by suitable computer simulation tools using knowledge about the solute-column interactions described by thermodynamic retention parameters. Since retention parameters usually are determined under isothermal conditions, the presented work offers a step to estimate one of the most important retention parameters, the characteristic temperature Tchar by less laborious temperature programmed measurements. In the first part an empirical multivariate model was introduced describing the correlation between the elution temperature Telu of a solute and its characteristic temperature Tchar. Now in the second part a simulation model of GC and available retention data from a retention database was used to investigate the correlation between Telu and Tchar for an expanded range of heating rates and initial temperatures. In addition to part I, the simulation is used to investigate the influences of different properties of the separation column such as different phase ratios and column geometries like length and diameter or various stationary phases including SLB-5 ms, SPB-50, Stabilwax, Rtx-Dioxin2, Rxi-17Sil MS, Rxi-5Sil MS, ZB-PAH-CT, DB-5 ms, Rxi-5 ms, Rtx5 and FS5ms. The fit model is valid for all investigated stationary phases. The influence of the phase ratio to the correlation could be determined. Therefore, the model was expanded to this parameter. The expanded range of heating rates and the normalization for the system independent dimensionless heating rate required a further modification of the previously presented correlation model. The model now fits also under isothermal conditions. The results were used for estimation of the Tchar of an analyte from the elution temperature in the temperature program. The prediction performance was investigated and evaluated for 20 different temperature program conditions and at two phase ratios (β=125 and β=250). Under best conditions the estimated and the measured Tchar values show relative differences <0.5 %. With this novel model estimations for Tchar are possible at 20 °C above the initial temperature, which expands the prediction range even for low and medium retained analytes compared to earlier approaches.

Generalized flow calculation of the gas flow in a network of capillaries used in gas chromatography

A general method for the calculation of the flow and pressure of a gas in a network of cylindrical capillaries is presented. This method is used specifically for gas chromatographic systems in this work. With this approach, it is possible to easily calculate flow and pressures in complex gas chromatographic systems, like flow-modulated or thermal-modulated multidimensional gas chromatographic systems, or systems with multiple outlets at different pressures. A mathematic abstraction using graph theory is used to represent the system of capillaries. With this graph, the flow balance equations at the connections of the capillaries can easily be set up. Using a computer algebra system, the system of flow balance equations can be solved for the pressures at the connection points. For simple systems, this approach is presented, and calculated flows, pressures, and hold-up times are compared with measured values. In addition, two complex systems (4-Way-Splitter, Deans Switch system) of capillaries are presented with calculations only. For these systems, certain conditions were formulated, that is, a certain difference in hold-up times and a defined split ratio between different paths of these systems. Using a numeric non-linear solver, configurations of these systems were found, that fulfill these conditions.

Theoretical modeling and machine learning-based data processing workflows in comprehensive two-dimensional gas chromatography-A review

In recent years, comprehensive two-dimensional gas chromatography (GC × GC) has been gradually gaining prominence as a preferred method for the analysis of complex samples due to its higher peak capacity and resolution power compared to conventional gas chromatography (GC). Nonetheless, to fully benefit from the capabilities of GC × GC, a holistic approach to method development and data processing is essential for a successful and informative analysis. Method development enables the fine-tuning of the chromatographic separation, resulting in high-quality data. While generating such data is pivotal, it does not necessarily guarantee that meaningful information will be extracted from it. To this end, the first part of this manuscript reviews the importance of theoretical modeling in achieving good optimization of the separation conditions, ultimately improving the quality of the chromatographic separation. Multiple theoretical modeling approaches are discussed, with a special focus on thermodynamic-based modeling. The second part of this review highlights the importance of establishing robust data processing workflows, with a special emphasis on the use of advanced data processing tools such as, Machine Learning (ML) algorithms. Three widely used ML algorithms are discussed: Random Forest (RF), Support Vector Machine (SVM), and Partial Least Square-Discriminate Analysis (PLS-DA), highlighting their role in discovery-based analysis.

Retention Database for Prediction, Simulation, and Optimization of GC Separations

This work presents an open source database with suitable retention parameters for prediction and simulation of GC separations and gives a short introduction to three common retention models. Useful computer simulations play an important role to save resources and time in method development in GC. Thermodynamic retention parameters for the ABC model and the K-centric model are determined by isothermal measurements. This standardized procedure of measurements and calculations, presented in this work, have a useful benefit for all chromatographers, analytical chemists, and method developers because it can be used in their own laboratories to simplify the method development. The main benefits as simulations of temperature-programed GC separations are demonstrated and compared to measurements. The observed deviations of predicted retention times are in most cases less than 1%. The database includes more than 900 entries with a large range of compounds such as VOCs, PAHs, FAMEs, PCBs, or allergenic fragrances over 20 different GC columns.

Zr-MOF-Induced Smart Accumulation Enables Surface-Enhanced Raman Spectroscopic Detection of Dioxin at ppt Level in Food Samples

The fast and economical detection of trace polychlorinated dibenzo-p-dioxins (PCDDs) in food samples by current mass spectrum-based methods is hindered by tedious sample preparation and bulky & expensive analytical instruments. Surface-enhanced Raman spectroscopy (SERS) successfully detects many organic pollutants in foods but not dioxins because the employed metal nanoparticles weakly adsorb hydrophobic PCDDs. Herein, we report the detection of PCDDs in milk with SERS for the first time using a bifunctional substrate consisting of Au nanoparticles embedded in a zirconium-based metal-organic framework shell (AuNP/Zr-MOF). 2,3,7,8-Tetrachlorodibenzo-p-dioxin (TCDD), as the most toxic PCDD, is detected as low as 1.2 parts per trillion (ppt) in real milk samples with massive interfering substances in 30 min, which is the lowest among all reported methods. The aromatic rings of Zr-MOF promote the smart accumulation of TCDD through π-π interactions, and Au-Cl interactions drive TCDD onto Au surfaces. Zr-MOF shells with pore sizes of 12.7 and 20 Å block the accessibility of larger interfering molecules. A one-step apparatus and protocol are established to be superior to traditional methods in terms of time and cost. This work provides new insight into a rational screening method for the detection of persistent organic pollutants in a real sample matrix.

Estimation of retention parameters from temperature programmed gas chromatography

A fast and reliable method is presented to evaluate retention parameters of the distribution-centric 3-parameter model from temperature programed gas chromatographic measurements. Based on a fully differentiable model of the migration of solutes in a gas chromatographic (GC) system, Newton's method with a trust region is used to determine the three parameters, respectively the three parameters and the column diameter, of several solutes as the minima of the difference between measured and calculated retention times. The determined retention parameters can then be used in method development, using the simulation of GC separation. The results of the retention parameters are compared to the parameters determined using isothermal GC measurements and show good agreement, with deviations of less than 0.5% (1.8 K) for the most important parameter of characteristic temperature T_char. Using the estimated retention parameters, additional GC separations are simulated and compared with measurements. Retention times in additional temperature programmed measurements could be predicted with less than 0.7% deviation. Four to five different temperature programs are enough to determine reliable retention parameters. Unless the column diameter and the column length are exactly known, it is preferable to also estimate the diameter (more precisely the L/d-ratio) together with the retention parameters.