Exploring Applicability of Direct Analysis in Real Time with Mass Spectrometry (DART-MS) to Identify Homemade Explosive Residues Post-Blast
Bibliographic record
Abstract
Application of Direct-Analysis-in-Real-Time (DART) ionization with mass spectrometry (DART-MS) to identify explosives from post-blast residues is presented.Explosives of interest represent real current threats encountered in criminal investigations in North America and Europe: homemade organic peroxides, binary explosives and smokeless powder.A series of simulated improvised explosive devices (IEDs) were manufactured using triacetone triperoxide (TATP), hexamethylene triperoxide diamine (HMTD), methyl ethyl ketone peroxide (MEKP), homemade binary explosives (composed of a fuel-oxidizer) and single and double-base smokeless powders.Each IED was configured to yield bomb fragments representative of actual materials recovered from bombing investigations.The goal of this study was to demonstrate the validity of DART-MS for identification of homemade explosives using real world samples (i.e.not laboratory simulations) and develop a quality assured method for use in accredited forensic laboratory settings.Smokeless powder was of specific interest as there is currently no reported method to identify nitrocellulose (NC) post-blast, unless unconsumed material is recovered.Therefore, this study aimed to demonstrate the validity of DART-MS to characterize thermal breakdown products of NC.All recovered fragments were analyzed directly and in directly (i.e.cotton swabs and solvent extraction methods) using full scan high resolution mass spectrometry (HRMS).This work demonstrates the forensic validity of DART-MS to provide rapid and quality assured identification of explosive residues from real post-blast IED fragments.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".