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Improvements in a kinetic based model to predict evaporation of gasoline

NCJ Number
301726
Journal
Forensic Chemistry Volume: 17 Dated: 2020
Author(s)
Date Published
2020
Annotation

The authors’ previous research identified the need to improve accuracy in predicting chromatograms corresponding to evaporated gasoline, so the current article addressed this need.

Abstract

Reference collections that contain chromatograms of ignitable liquids are widely used in fire-debris analysis to aid the identification of any liquid present in the debris. Most reference collections also include chromatograms of ignitable liquids evaporated to different levels to account for chemical changes as a result of evaporation during the fire. Previous work in the authors’ laboratory demonstrated the potential of a kinetic-based model to predict chromatograms corresponding to evaporated liquids, thereby eliminating the need for experimental evaporations. In the research reported in the current article, three gasoline samples were experimentally evaporated to 50 percent, 70 percent, and 90 percent by mass and analyzed by gas chromatography-mass spectrometry (GC–MS). Modifications to the instrument method, including elimination of the solvent delay and subtraction of background interferences, resulted in improved correlation between predicted and experimental chromatograms at each evaporation level. Modification of the method used to calculate the total fraction of the liquid remaining offered further improvement in predictive accuracy across all three evaporation levels. Application of the model to generate a reference collection of predicted chromatograms for gasoline and other ignitable liquids was also demonstrated, with maximum Pearson product-moment correlation (PPMC) coefficients ranging from 0.937 to 0.991 for comparisons of predicted and experimental gasoline. The model improvements reported here provide a method by which predicted chromatograms corresponding to any evaporation level of gasoline can be generated accurately, saving time and resources in generating reference collections for fire debris applications. (publisher abstract modified)

Date Published: January 1, 2020