Experimental p<i>K</i><sub>a</sub> Determination for Perfluorooctanoic Acid (PFOA) and the Potential Impact of p<i>K</i><sub>a</sub> Concentration Dependence on Laboratory-Measured Partitioning Phenomena and Environmental Modeling
Bibliographic record
Abstract
An accurately measured equilibrium acid dissociation constant (pKa) is essential for understanding and predicting the fate of perfluorocarboxylic acids (PFCAs) in the environment. The aqueous pKa of perfluorooctanoic acid (PFOA) has been determined potentiometrically using a standard water-methanol mixed solvent approach and was found to be 3.8 +/- 0.1. The acidity of PFOA is thus considerably weaker than its shorter-chain PFCA homologues. This was attributed to differences in molecular and electronic structure, coupled with solvation effects. The pKa of PFOA was suppressed to approximately 2.3 at higher concentrations because of the aggregation of perfluorooctanoate (PFO). Often, PFCA partion coefficients are determined at concentrations above those found in the environment. Thus, it was suggested that a pKa correction factor, which accounts for this concentration-dependent shift in acid/base equilibrium, should be applied to PFCA partition efficients before they are implemented in environmental fate models. A pKa of 3.8 +/- 0.1 suggests that a considerable concentration of the PFCA exists as the neutral species in the aqueous environment for example, in typical Ontario rainwater, it is approximately 17%. Transport, fate, and partitioning models have often ignored the presence this species completely. The environmental dissemination of PFCAs could, in part, be explained by considering the role of the neutral species.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".