Photochemical Formation of Trifluoroacetic Acid: Mechanistic Insights into a Fluoxetine-Related Aryl-CF <sub>3</sub> Compound
Bibliographic record
Abstract
Trifluoroacetic acid (TFA) is a ubiquitous environmental contaminant; however, its sources are poorly constrained. One understudied source is from the photochemical reactions of aromatic compounds containing −CF 3 moieties (aryl-CF 3 ) including many pharmaceuticals and agrochemicals. Here, we studied the aqueous photochemistry of 4-(trifluoromethyl)phenol (4-TFMP), a known transformation product of the pharmaceutical fluoxetine. When exposed to lamps centered at UV-B, 4-TFMP formed up to 9.2% TFA at a steady state under acidic conditions and 1.3% under alkaline conditions. TFA yields of fluoxetine were similar to 4-TFMP for acidic and neutral pH, but higher at alkaline pH, suggesting that fluoxetine may have a mechanism of TFA formation in addition to via the 4-TFMP intermediate. Use of an 13 CF 3 isotopologue of 4-TFMP allowed for the tracking of TFA formation, which formed via multiple oxidative additions prior to oxidative ring cleavage. The oxidation is mediated by reactive oxygen species (ROS) generated through self-sensitized photolysis, with singlet oxygen and hydroxyl radicals as the key ROS. In addition to the TFA formation mechanism, other photochemical reactions of 4-TFMP resulted in defluorination and dimerization. Overall, this work expands our understanding of how TFA forms from aryl-CF 3 compounds to better understand the total global burden of TFA.
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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) | 0.002 | 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".