Preparation of <scp>ZIF</scp> ‐8—Pebax 1657 nanocomposite membranes for <i>n</i> ‐hexane/nitrogen separation as a representative of gasoline vapour recovery
Bibliographic record
Abstract
Abstract Gasoline vapour emission is hazardous to both human health and the ecosystem and also results in capital loss, altogether revealing the necessity of its recovery. Some ZIF‐8–Pebax flat nanocomposite membranes were fabricated by the method of solution casting and used for gasoline vapour recovery as represented by n ‐hexane vapour/nitrogen separation. Microporous ZIF‐8 nanoparticles were synthesized and characterized by Fourier transform infrared (FTIR) and Brunauer–Emmett–Teller (BET) analysis. BET results revealed specific surface area, total volume, and average pore diameter of 940.8 m 2 · g −1 , 0.36 cm 3 · g −1 , and 1.54 nm, respectively. Pure nitrogen and n ‐hexane vapour/nitrogen gas mixture permeabilities were measured through the membranes. There was a decline in both permeation rate and selectivity up to 5.0 wt.% of ZIF‐8 loading and the next increment at their higher loadings to considerably more values that the pristine membrane. The maximum n ‐hexane vapour permeability and selectivity at 10.0 wt.% loading of ZIF‐8 nanoparticles, the feed flow rate of 173 mL · min −1 , and permeate side pressure of −200 mbar were observed as 280.1 Barrer and 106.7, respectively, revealing 60.0% and 36.9% improvements compared with those of the pristine Pebax membrane. Observed 86%–92% n ‐hexane vapour recovery approves the successful application of the ZIF‐8–Pebax nanocomposite membranes for n ‐hexane/nitrogen separation. The long‐term separation performance of 5.0 wt.% ZIF‐8 loaded nanocomposite membrane was improved by 76.5% compared with that of the pristine Pebax membrane.
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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".