Ambient Mass Spectrometric Detection of Organometallic Compounds Using Direct Analysis in Real Time
Bibliographic record
Abstract
The present work describes the mass spectrometric detection of organometallic compounds following their atmospheric pressure ionization using a commercial direct analysis in real time (DART) ion source. Several organometallic compounds of As, Fe, Hg, Pb, Se, and Sn were examined, and their corresponding mass spectra as well as induced fragmentation patterns were recorded. Gas phase sampling of the pure organometallic compounds or their solutions prepared in toluene generated temporally stable signals. For the majority of the compounds, the molecular ion or protonated molecule was detected; noticeable exceptions are the tetra-substituted compounds for which their less-substituted species dominated. The organometallic species were used as model compounds for a systematic investigation of the impact of operating parameters of the DART source, including gas temperature and electrode voltages. In general, results have shown that powering the electrodes designed to remove ions from the DART gas stream results in a reduction in signal intensity for most of the compounds investigated, suggesting that charged species from the plasma play an important role in the ionization process of the test analytes.
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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 | 0.000 | 0.000 |
| 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) | 0.002 | 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".