Desorption electrospray ionization mass spectrometric analysis of organophosphorus chemical warfare agents using ion mobility and tandem mass spectrometry
Bibliographic record
Abstract
Desorption electrospray ionization mass spectrometry (DESI-MS) has been applied to the direct analysis of sample media for target chemicals, including chemical warfare agents (CWA), without the need for additional sample handling. During the present study, solid-phase microextraction (SPME) fibers were used to sample the headspace above five organophosphorus CWA, O-isopropyl methylphosphonofluoridate (sarin, GB), O-pinacolyl methylphosphonofluoridate (soman, GD), O-ethyl N,N-dimethyl phosphoramidocyanidate (tabun, GA), O-cyclohexyl methylphosphonofluoridate (cyclohexyl sarin, GF) and O-ethyl S-2-diisopropylaminoethyl methyl phosphonothiolate (VX) spiked into glass headspace sampling vials. Following sampling, the SPME fibers were introduced directly into a modified ESI source, enabling rapid and safe DESI of the toxic compounds. A SYNAPT HDMS instrument was used to acquire time-aligned parallel (TAP) fragmentation data, which provided both ion mobility and MS(n) (n = 2 or 3) data useful for the confirmation of CWA. Unique ion mobility profiles were acquired for each compound and characteristic product ions of the ion mobility separated ions were produced in the Triwave transfer collision region. Up to six full scanning MS(n) spectra, containing the [M + H](+) ion and up to seven diagnostic product ions, were acquired for each CWA during SPME fiber analysis. A rapid screening approach, based on the developed methodology, was applied to several typical forensic media, including Dacron sampling swabs spiked with 5 microg of CWA. Background interference was minimal and the spiked CWA were readily identified within one minute on the basis of the acquired ion mobility and mass spectrometric data.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".