Comparison of four commercial solid-phase micro-extraction (SPME) fibres for the headspace characterisation and profiling of gunshot exhausts in spent cartridge casings
Bibliographic record
Abstract
Headspace solid-phase micro-extraction (SPME) is a promising technique for the characterisation and profiling of gunshot exhausts in spent cartridge casings, especially for health and environmental risk assessments, as well as forensic purposes. To date, however, no comprehensive investigation has been carried out to objectively assess the kinds of compound released during a discharge that can be recovered by this approach, the selectivity of the main commercially available fibres, and their relative performances for the analysis of gunshot exhausts and the discrimination of different ammunition types. This study aimed to fill this gap. Gunshot exhausts in spent cartridge casings from four different ammunition types were analysed by GC-MS, after extraction with four different commercial fibres: 100 μm polydimethylsiloxane (PDMS), 85 μm polyacrylate (PA), 65 μm polydimethylsiloxane/divinylbenzene (DVB), and 85 μm carboxen/polydimethylsiloxane (CAR). Results showed that, overall, a total of 120 analytes could be observed across the cartridges, but the different tested fibres also displayed distinct performances, which were, to some extent, complementary for the characterisation of gunshot exhausts. DVB, in particular, recovered the most compounds simultaneously. On the other hand, the observed variability between measurements was also high, making it a poor candidate for (semi-)quantitative applications (e.g. estimation of time since discharge and/or source profiling). In this regard, PA demonstrated the highest potential for broad use and implementation in multi-purpose methods.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".