A comprehensive theory for vacuum-assisted headspace extraction of solid samples

Abstract

Vacuum-assisted headspace extraction (VA-HSE) has proven to be an efficient solution for the rapid and effective extraction of volatile and semi-volatile species. The research has been mainly focused on liquid samples by considering differences in Henry's constants, while the accelerating effect of vacuum is more significant and practically more important in solid samples with complex matrices. Nevertheless, the lack of a comprehensive theory for VA-HSE in solid samples, based on the adsorption/desorption phenomena, is quite evident. This research was done with the aim of modeling VA-HSE of solid samples from a thermodynamic point of view. To understand the impact and mechanism of reduced pressure, the sampling space was divided into three separate areas including the solid matrix (the surface and cavities of the solid sample), the headspace of the sample, and the extraction phase (the surface and cavities of the adsorbent). The effects of vacuum on the movement of analyte molecules in all three areas were investigated and included in a comprehensive equation.According to the theoretical model, when a solid sample is subjected to vacuum conditions, the enthalpy of the analytes in their free state decreases. As a result, the analytes become more volatile. Additionally, vacuum reduces the thickness of the boundary layer in solid samples. This facilitates the diffusion of analyte molecules into the cavities within the solid material and eventually into the headspace of the sample. A similar effect is observed on the extractive phase side when vacuum is applied. The reduction in boundary layer thickness promotes the adsorption of analytes onto the extractive phase, thereby facilitating a quicker equilibrium of analyte concentration in the extraction phase. The proposed model was validated by correlating it with the experimental data found in the literature. The results of this analysis have shown a robust correlation between the theoretical model and the experimental data, bolstering the reliability of the model, and highlighting its practical relevance.