Insight into the Self-Assembly Behaviors of Per- and Polyfluoroalkyl Substances Using a “Computational Microscope”
Bibliographic record
Abstract
Aqueous film-forming foams (AFFFs) have been extensively used for firefighting, contributing to environmental contamination with per- and polyfluoroalkyl substances (PFAS). Most PFAS in AFFFs are fluorosurfactants, known to self-assemble into large supramolecular assemblies in the field of physical chemistry; however, the application of this phenomenon to understanding environmental fate has not been studied. We hypothesize that self-assembled PFAS likely enhance the long-term retention of PFAS in subsurface environments, acting as a continuous source of dissolved PFAS. Thus, characterizing these self-assemblies and understanding their aggregation dynamics are crucial for assessing the fate and transport of PFAS. Despite the utility of molecular dynamics (MD) simulation in studying surfactant behaviors, fluorosurfactants have been underexplored due to the lack of force field parameters. In this study, we developed coarse-grained (CG) force field parameters for fluorosurfactants based on the Martini 3 model and performed CG-MD simulations. These “computational microscope” simulations reveal the self-assembly behavior of selected PFAS, aligning with experimental cryo-transmission electron microscopy observations and providing mechanistic insights. Our work sheds light on the evolution of solvated PFAS self-assemblies over time and space. The CG-MD simulation can particularly address the knowledge gaps for new PFAS that are difficult to explore experimentally due to the lack of chemical standards.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".