Identification of Novel Fluorinated Surfactants in Aqueous Film Forming Foams and Commercial Surfactant Concentrates
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Recent studies comparing the results of total organofluorine-combustion ion chromatography (TOF-CIC) to targeted analysis of perfluoroalkyl and polyfluoroalkyl substances (PFASs) by liquid chromatography tandem mass spectrometry (LC-MS/MS) have shown that a significant yet variable portion of the total organofluorine in environmental and biological samples is in the form of unknown PFASs. A portion of this unknown organofluorine likely originates in proprietary fluorinated surfactants not included in LC-MS/MS analyses and not fully characterized by the environmental science community, which may enter the environment through use in aqueous film forming foams (AFFFs) for firefighting. Contamination of water, biota, and soils with various PFASs due to AFFF deployment has been documented. Ten fluorinated AFFF concentrates, 9 of which were obtained from fire sites in Ontario, Canada, and two commercial fluorinated surfactant concentrates were characterized in order to identify novel fluorinated surfactants. Mixed-mode ion exchange solid phase extraction (SPE) fractionated fluorinated surfactants based on ionic character. High resolution mass spectrometry assigned molecular formulas to fluorinated surfactant ions, while collision induced dissociation (CID) spectra assisted structural elucidation. LC-MS/MS detected isomers and low abundance fluorinated chain lengths. In total, 12 novel and 10 infrequently reported PFAS classes were identified in fluorinated chain lengths from C3 to C15 for a total of 103 compounds. Further research should examine the environmental fate and toxicology of these PFASs, especially their potential as perfluoroalkyl acid (PFAA) precursors.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Science and technology studies | 0.000 | 0.004 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 it