Simultaneous and Practical Difluoromethylation of Triclosan, 2,4,6-Trichlorophenol and Pentachlorophenol in Soils for their Qualitative Detection by Electron Ionization GC-MS
Bibliographic record
Abstract
The extremely rapid (30 seconds) and practical derivatization of three environmentally relevant chlorinated phenols (CPs): triclosan, 2,4,6-trichlorophenol and pentachlorophenol for their qualitative detection by GC-MS is presented. The method involves the use of the eco-friendly difluoromethylating agent diethyl (bromodifluoromethyl) phosphonate (DBDFP) and results in the efficient tagging of the hydroxyl group in the phenols with the difluoromethyl (CF2H) moiety. Moreover, the protocol is carried out at ambient temperature and thus eliminates the heating involved with more conventional methods such as silylation. Another important facet of the derivatization is its biphasic nature, allowing for the specific tagging of phenolic species that are present in water or in an organic matrix. The derivatization yields difluoromethylated ether versions of the phenols with enhanced detectability by EI-GC-MS and as a way of demonstrating the robustness of the protocol, the three CPs were derivatized and unequivocally identified when spiked in three different types of soils: Virginia type A soil, Ottawa Sand and Nebraska EPA soil at a 1 μg g-1 concentration each. The protocol offers a fast way of derivatizing these types of phenols since no prior, separate sample preparation or extraction steps are needed.
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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 | 0.000 |
| 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) | 0.001 | 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".