Fluorous Membrane Ion-Selective Electrodes for Perfluorinated Surfactants: Trace-Level Detection and in Situ Monitoring of Adsorption
Bibliographic record
Abstract
Ion-selective electrodes (ISEs) with fluorous anion-exchanger membranes for the potentiometric detection of perfluorooctanoate (PFO(-)) and perfluorooctanesulfonate (PFOS(-)) were developed. Use of an anion-exchanger membrane doped with the tetraalkylphosphonium derivative (Rf8(CH2)2)(Rf6(CH2)2)3P(+) and an optimized measurement protocol resulted in detection limits of 2.3 × 10(-9) M (1.0 ppb) for PFO(-) and 8.6 × 10(-10) M (0.43 ppb) for PFOS(-). With their higher selectivity for PFO(-) over OH(-), membranes containing the alternative anion exchanger (Rf6(CH2)3)3PN(+)P((CH2)3Rf6)3 with a bis(phosphoranylidene)ammonium group further improved the detection limit for PFO(-) to 1.7 × 10(-10) M (0.070 ppb). These values are comparable with results obtained using well-established techniques such as gas chromatography-mass spectrometry (GC-MS), liquid chromatography-mass spectrometry (LC-MS), and liquid chromatography-tandem mass spectrometry (LC-MS-MS), but the measurement with ISEs avoids lengthy sample preconcentration, can be performed in situ, and is less costly. Even when eventual spectrometric confirmation of analyte identity is required, prescreening of large numbers of samples or in situ monitoring with ISEs may be of substantial benefit. To demonstrate a real-life application of these electrodes, in situ measurements were performed of the adsorption of PFOS(-) onto Ottawa sand, which is a standard sample often used in environmental sciences. The results obtained are consistent with those from an earlier LC-MS study, validating the usefulness of these sensors for environmental studies. Moreover, PFOS(-) was successfully measured in a background of water from Carnegie Lake.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".