Study oil/water separation property of PE foam and its improvement by <i>in situ</i> synthesis of zeolitic–imidazolate framework (ZIF‐8)
Bibliographic record
Abstract
Herein, the oil/water separation property of four polyethylene (PE) foams with two different volume expansion ratios including 15 and 30 times was studied. We found that the PE‐30‐1 foam was the best absorbent materials among these four samples, which was also superior to the commercially available products. It demonstrated high oil absorption capacities, as well as high oil/water selectivity, which was due to its small average pore size and high porosity. In addition, this work also reports two rapid and straightforward methods to fabricate PE‐30‐1‐ZIF‐8 composite with enhanced compressive property, an encouraging absorption capacity for different organic solvents, reaching 59 times of its own weight for chloroform, as well as increased oil–water selectivity of the system. Comparing with the PE foam, the organic solvent absorption capacities for PE‐ZIF‐8‐sono composite foams were improved by 6.4%, 29%, 9.1%, and 12% for chloroform, acetone, toluene and oleic acid, respectively, while the corresponding improvement for PE‐ZIF‐8‐Me(OH) was about 7.5%, 28%, 14%, and 15%, respectively. These PE‐ZIF‐8 foams could reach the requirement for oil spill cleanup, suggesting its great potential for this type of application. POLYM. ENG. SCI., 59:1354–1361 2019. © 2019 Society of Plastics Engineers
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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".