High-Throughput Trace Analysis of Explosives in Water by Laser Diode Thermal Desorption/Atmospheric Pressure Chemical Ionization-Tandem Mass Spectrometry
Bibliographic record
Abstract
Harmful explosives can accumulate in natural waters in the long term during their testing, usage, storage, and dumping and can pose a health risk to humans and the environment. For the first time, attachment of small anions to neutral molecules in laser diode thermal desorption/atmospheric pressure chemical ionization was systematically investigated for the direct determination of trace nitroaromatics, nitrate esters, and nitramine explosives in water. Using ammonium chloride as an additive improved the instrument response for all the explosives tested and promoted the formation of several characteristic adduct ions. The method performs well achieving good linearity over at least 2 orders of magnitude, with coefficients of determination greater than 0.995. The resulting limits of detection are in the range of 0.009-0.092 μg/L. River water samples were successfully analyzed by the proposed method with accuracy in the range of 96-98% and a response time of 15 s, without any further pretreatment or chromatographic separation.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".