Rapid extraction of trace benzene by a crown-ether-based metal-organic framework
Bibliographic record
Abstract
the energy-intensive extractive distillation method. Their adsorptive separation by porous materials is hampered by their similar dimensions. Metal-organic frameworks (MOFs) with versatile pore environments are capable of molecular discrimination, but the separation of trace substrates in liquid-phase remains extremely challenging. Herein, we report a robust MOF (NKU-300) with triangular channels decorated with crown ether that can discriminate trace benzene from cyclohexane, exhibiting an unprecedented selectivity of 8615(10) for the mixture of benzene/cyclohexane (v/v = 1/1000). Remarkably, NKU-300 demonstrates exceptional selectivities for the extraction of benzene from cyclohexane over a wide range of concentrations of 0.1%-50% with ultrafast sorption kinetics and excellent stability. Single-crystal X-ray diffraction and computational modelling reveal that multiple supramolecular interactions cooperatively immobilise benzene molecules in the triangular channel, enabling superior separation performance. This study will promote the application of advanced sorbents with tailored binding sites for challenging industrial separations.
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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".