Balancing Pore Accessibility and Hydrophobicity for Enhanced Perfluorooctanoic Acid Removal: A Case Study on NU-1000
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Perfluorooctanoic acid (PFOA), a persistent pollutant from the PFCA class, poses significant environmental and health risks due to its resistance to degradation. Metal–organic frameworks (MOFs) offer a promising solution for removing PFOA from water; however, the mechanisms underlying PFOA uptake and the optimization of MOFs for this application remain unclear. In this study, we explore the adsorption mechanism of PFOA on NU-1000, a zirconium-based MOF, using molecular dynamics (MD) simulations conducted in an explicit water environment to replicate realistic conditions. Our findings, consistent with experimental observations, provide atomistic insights into PFOA adsorption, identifying hydrophobic interactions as key drivers of its removal. Leveraging these insights, we propose two functionalization strategies using the SALI method. Among these, NU-F, the MOF functionalized with fluorobenzoate ligands, exhibits superior PFOA uptake across all tested concentrations, achieving a higher removal efficiency and faster adsorption kinetics. The enhanced performance of NU-F is attributed to its optimal balance of hydrophobic interactions and efficient pore utilization. This study underscores the importance of rational functionalization in MOF design for environmental remediation and offers a promising pathway for developing advanced adsorbents targeting PFOA and other PFCAs pollutants.
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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 | 0.001 | 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".