Rapid Analysis of Chemical Warfare Agents and Their Hydrolysis Products by Desorption Electrospray Ionization Mass Spectrometry (DESI-MS)
Bibliographic record
Abstract
Abstract : A dip and shoot analytical method was developed for the rapid sampling and desorption electrospray ionization mass spectrometric (DESI-MS) analysis of chemical warfare agents and their hydrolysis products in liquid samples. Sampling was performed by simply dipping fused silica, stainless steel or SPME tips into the organic or aqueous samples. Replicate analyses were completed within several minutes under ambient conditions with no sample pre-treatment, resulting in a significant increase in sample throughput over conventional solid phase microextraction (SPME) sampling and DESI-MS analysis or liquid chromatographic electrospray ionization mass spectrometric (LC-ESI-MS) analysis. The developed sample handling and analysis method was applied to the determination of chemical warfare agent content in samples containing unknown chemical and/or biological warfare agents. Ottawa sand was spiked with mustard, extracted with water and autoclaved to ensure sterility. Mustard was completely hydrolysed during the extraction/autoclave step and thiodiglycol was identified by DESI-MS, with analyses generally being completed within one minute using the dip and shoot method developed for the rapid analysis of liquid samples.
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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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".