High-sensitivity elemental ionization for quantitative detection of halogenated compounds
Bibliographic record
Abstract
The rising importance of organohalogens in environmental, pharmaceutical, and biological applications has drawn attention to analysis of these compounds in recent years. Elemental mass spectrometry (MS) is particularly advantageous in this regard because of its ability to quantify without compound-specific standards. However, low sensitivity of conventional elemental MS for halogens has hampered applications of this powerful method in organohalogen analyses. To this end, we have developed a high-sensitivity elemental ion source compatible with widely available atmospheric-sampling mass spectrometers. We utilize a helium-oxygen plasma for atomization followed by negative ion formation in plasma afterglow, a configuration termed as plasma-assisted reaction chemical ionization (PARCI). The effect of oxygen on in-plasma and afterglow reactions is investigated, leading to fundamental understanding of ion generation processes as well as optimized operating conditions. Coupled to a gas chromatograph, PARCI shows constant ionization efficiency for F, Cl, and Br regardless of the chemical structure of the compounds. Negative ionization in the afterglow improves halide ion formation efficiency and eliminates isobaric interferences, offering sub-picogram elemental detection for F, Cl, and Br using low-resolution MS. Notably, the detection limit for F is about one order of magnitude better than other elemental MS techniques. The high sensitivity and facile adoptability of PARCI pave the way for combined elemental-molecular characterization, a comprehensive analytical scheme for rapid identification and quantification of organohalogens.
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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 | 0.001 | 0.001 |
| 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".