Molecular framework for designing Fluoroclay with enhanced affinity for per- and polyfluoroalkyl substances
Bibliographic record
Abstract
Motivated by the need for enhancing sorbent affinity for per- and polyfluoroalkyl substances (PFAS), we demonstrate the possibility of rationally designing clay-based material (FluoroClay) with a pre-selected intercalant and predicting sorbent performance using all-atom molecular dynamics simulation coupled with density functional theory-based computation. Perfluorohexyldodecane quaternary ammonium (F6H12A) as the selected intercalant revealed significant enhancement in adsorption affinity for hard-to-remove compounds, including perfluorobutane sulfonate (PFBS) and polyfluoroalkylethers (GenX and ADONA). The adsorption is thermodynamically entropy-driven and dominated by the hydrophobic effect. The incorporation of fluorine atoms into clay intercalants gave rise to a hydrophobic and fluorophilic "cavity" structure for targeted PFAS. The self-assembly of intercalant-PFAS under the negative electric field of clay sheets created a unique configuration that significantly enlarged the contact surface area between PFAS and F6H12A and was quantitatively driven by their intermolecular interactions, e.g., CF chain-CH chain, CF chain-CF chain, and charge-CH chain interactions. Collectively, our work demonstrated a new approach to select fluorinated functionality for designing a new adsorbent and estimating its performance via molecular simulation. It also provided an in-depth understanding of the underlying fundamental physics and chemistry in the adsorption of PFAS, suggesting a new strategy for PFAS removal, particularly for short-chain PFAS and new chemical alternatives.
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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 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 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 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".