Prediction of Thermal Modulated Comprehensive Two‐Dimensional Gas Chromatographic Separation Using a Modular, Graph‐Based Simulation Platform

TL;DR

This paper introduces a simulation tool for gas chromatography that predicts separation results and helps improve method development.

Contribution

A modular, graph-based simulation platform for thermal modulated GC×GC with open-source implementation and validation.

Findings

The simulation model achieved less than 1% error in first-dimension retention times.

Peak width predictions showed larger deviations, up to 40% in the first dimension.

The framework supports automated method development and system diagnostics in multidimensional gas chromatography.

Abstract

Comprehensive two‐dimensional gas chromatography (GC×GC) offers exceptional separation performance, but method development remains time‐consuming and sensitive to numerous system parameters. In this study, we present a modular simulation framework for GC×GC systems with thermal modulation, implemented in the open‐source Julia package GasChromatographySystems.jl. The simulation is based on a graph‐based abstraction of the GC system and models solute migration through column and modulator modules using previously established retention models. A simplified but effective model for thermal modulation enables the generation of realistic two‐dimensional retention times and peak widths. Simulation results were validated against experimental measurements from a GC×GC‐ToF‐MS system using different modulation periods and temperature programs. Systematic deviations between simulated and measured retention times could be explained and corrected by adjusting parameters such as the actual modulation period and modulator shift. The final model achieved root mean squared error (rmse) of below 15 s (less than 1%) for first‐dimension retention times and 55 ms (8%) for the second dimension. Peak width predictions were less accurate, with deviations of up to 3 s (40%) in the first dimension and up to 40 ms (60%) in the second. This modular and adaptable simulation framework provides a robust foundation for future applications in automated method development and system diagnostics in multidimensional gas chromatography.

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