Characterization of Nine Isomers in Commercial Samples of Perfluoroethylcyclohexanesulfonate and of Some Minor Components Including PFOS Isomers
Bibliographic record
Abstract
Electrochemical fluorination of 4-ethylbenzenesulfonyl halides produces a mixture of compounds that has found extensive use as an erosion inhibitor in aircraft hydraulic fluids. This paper reports a study of the composition of commercial samples of this material from two industrial scale manufacturers in terms of the structures and relative concentrations of their components, the major of which is perfluoroethylcyclohexanesulfonate (PFECHS). Fractionation of one of these mixtures by column chromatography produced fractions in which all significant components were of sufficient purity to allow assignment of their structures by 19 F NMR spectroscopy. Assessment of the relevant signals in the 19 F NMR spectra of the commercial mixtures by integration revealed the presence of 14 constituents at levels ≥0.5% in terms of molar contributions. Ten of these involve five pairs of geometric isomers, including cis - and trans -PFECHS which accounted for between 55% and 60% of the components present. Three constituents were determined to be isomers of perfluorooctanesulfonate (PFOS), two branched and the other linear PFOS itself. The availability of samples of the various components also allowed us to identify the compounds responsible for the peaks observed when the commercial samples were analyzed by LC/MS using either C 18 or perfluorophenyl stationary phases.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".