Ultra Pressure Liquid Chromatography–Negative Electrospray Ionization Mass Spectrometry Determination of Twelve Halobenzoquinones at ng/L Levels in Drinking Water
Bibliographic record
Abstract
We report here the characterization of twelve halobenzoquinones (HBQs) using electrospray ionization (ESI) high resolution quadrupole time-of-flight mass spectrometry. The high resolution negative ESI spectra of the twelve HBQs formed two parent ions, [M + H(+) + 2e(-)], and the radical M(-•). The intensities of these two parent ions are dependent on their chemical structures and on instrumental parameters such as the source temperature and flow rate. The characteristic ions of the HBQs were used to develop an ultra pressure liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) method. At the UPLC flow rate (400 μL/min) and under the optimized ESI conditions, eleven HBQs showed the stable and abundant transitions [M + H(+) + 2e(-)] → X(-) (X(-) representing Cl(-), Br(-), or I(-)), while dibromo-dimethyl-benzoquinone (DBDMBQ) showed only the transition of M(-•) → Br(-). The UPLC efficiently separates all HBQs including some HBQ isomers, while the MS/MS offers exquisite limits of detection (LODs) at subng/mL levels for all HBQs except DBDMBQ. Combined with solid phase extraction (SPE), the method LOD is down to ng/L. The results from analysis of authentic samples demonstrated that the SPE-UPLC-MS/MS method is reliable, fast, and sensitive for the identification and quantification of the twelve HBQs in drinking water.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".