Self-Aggregative Recognition and Extraction of Perfluorooctanesulfonate with Flexible Cationic Water-Soluble Deep Cavitands
Bibliographic record
Abstract
Cationic water-soluble deep cavitands enable hierarchical assembly-based recognition, optical detection, and extraction of perfluoroalkyl substances (PFAS) in aqueous solution. Recognition of PFAS occurs at the lower rim crown of the cavitand, which triggers self-aggregation of a PFAS-cavitand complex, allowing extraction from water. In addition, when paired with an indicator dye that can be bound in the cavity of the host molecule, the PFAS-cavitand association causes a significant (>20-fold at micromolar [PFAS]) enhancement of dye fluorescence due to conformational rearrangement of the fluxional cavitand AMI, allowing optical detection of PFAS. The cavitands are water-soluble, and the detection and recognition occur in purely aqueous solution. The association is most effective for long-chain sulfonate PFAS, and as such, selective optical detection of perfluorooctanesulfonate is possible, with a LOD = 130 nM in buffered water and 530 nM in real-world samples such as polluted canal water. By pairing the AMI host with multiple dyes in an array-based format, full discrimination of five other PFAS can be achieved at micromolar concentrations via differential sensing. In addition, the aggregation process allows extraction of PFAS from solution, and a 99% reduction of PFOS concentration in water is possible with a single treatment of an equimolar concentration of AMI cavitand. The hierarchical nature of the cavitand recognition system allows both selective, sensitive optical detection and extraction of PFAS from water with a single scaffold.
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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) | 0.000 | 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 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".