Mass Spectrometric Analysis of Chemical Warfare Agents and Their Degradation Products in Soil and Synthetic Samples
Bibliographic record
Abstract
A packed capillary liquid chromatography-electrospray ionization mass spectrometry (LC-ESI-MS) method was developed for the identification of chemical warfare agents, their degradation products and related compounds in synthetic tabun samples and in soil samples collected from a former mustard storage site. A number of organophosphorus and organosulfur compounds that had not been previously characterized were identified, based on acquired high-resolution ESI-MS data. At lower sampling cone voltages, the ESI mass spectra were dominated by protonated, sodiated and protonated acetonitrile adducts and/or their dimers that could be used to confirm the molecular mass of each compound. Structural information was obtained by inducing product ion formation in the ESI interface at higher sampling cone voltages. Representative ESI-MS mass spectra for previously uncharacterized compounds were incorporated into a database as part of an on-going effort in chemical warfare agent detection and identification. The same samples were also analyzed by capillary column gas chromatography (GC)-MS in order to compare an established method with LC-ESI-MS for chemical warfare agent identification. Analysis times and full-scanning sensitivities were similar for both methods, with differences being associated with sample matrix, ease of ionization and compound volatility. GC-MS would be preferred for organic extracts and must be used for the determination of mustard and relatively non-polar organosulfur degradation products, including 1,4- thioxane and 1,4-dithiane, as these compounds do not ionize during ESI-MS. Diols, formed following hydrolysis of mustard and longer-chain sulfur vesicants, may be analyzed using both methods with LC-ESI-MS providing improved chromatographic peak shape. Aqueous samples and extracts would, typically, be analyzed by LC-ESI-MS, since these analyses may be conducted directly without the need for additional sample handling and/or derivatization associated with GC-MS determinations. Organophosphorus compounds, including chemical warfare agents, related compounds and lower volatility hydrolysis products may all be determined during a single LC-ESI- MS analysis. Derivatization of chemical warfare agent hydrolysis products and other compounds with hydroxyl substitution would be required prior to GC-MS analysis, giving LC-ESI-MS a definite advantage over GC-MS for the analysis of samples containing chemical warfare agents and/or their hydrolysis products.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".