ArticleForensic chemistry (Amsterdam, Netherlands)2025
Differentiated factor expression by chemical class in the concentration of ignitable liquid residue by dynamic vapor microextraction.
Article in Forensic chemistry (Amsterdam, Netherlands), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
- Interpol review of forensic fire debris analysis and fire investigation, 2023-2025.Forensic science international. Synergy · 2026Review
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Authors and funding
3 authors.
Funding
Abstract
Ignitable liquids (IL) are complex mixtures whose chromatographic profiles may vary considerably across chemical compound classes. Dynamic vapor microextraction (DVME) is an emerging technique with potential application for extracting and concentrating IL from fire debris. A previous study assessed the effects of 11 instrumental and debris factors on DVME performance by collecting chromatographic data from a designed experiment, but did not investigate whether factor effects had differential expression within individual chemical classes. In this study, that experimental data is reanalyzed to assess factor effects individually in each of five chemical classes relevant to IL identification (alkanes, cycloalkanes & alkenes, aromatic-alkylbenzenes, indanes, and polynuclear aromatics), and effects are compared to the results previously reported from the original analysis. Two new instrument settings (capillary vapor trap coating and temperature) are found to have significant class-specific effects, providing additional avenues of improving DVME performance, while the effect of collection volume is found to be discordant in one class (alkane) compared to all others. Effects from uncontrollable debris factors on DVME performance are also found to be partially mitigated in certain classes through optimal instrument settings, though "optimal" may depend on the sample being analyzed. This analysis offers new insight into DVME optimization and robustness, and provides a discussion on how experimental conclusions drawn from aggregate chromatographic data, without consideration of class-specific effects, may be incomplete.
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