Molecular framework for designing Fluoroclay with enhanced affinity for per- and polyfluoroalkyl substances
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
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.
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 | 0.002 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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