Fabrication of Triblock Elastomer Foams for Oil Absorption Applications: Effects of Crosslinking, Composition, and Rheology Factors
Bibliographic record
Abstract
Oil spill accidents and oil-contaminated wastewater release from industries cause severe environmental pollution, resource waste, and economic loss. Thus, there is a dire need for an efficient oil/water separation approach to mitigate the challenge. Among the various oil/water separation technologies, employing oil-absorbing materials is one of the most effective strategies. In this work, a highly effective elastomer foam based on styrene–ethylene–butylene–styrene (SEBS) and ethylene propylene diene monomer (EPDM) blend was developed. Dicumyl peroxide was employed as a radical initiator for the crosslinking of the elastomer blend. The crosslinking of the blend significantly increased the melt strength of the SEBS/EPDM blend and therefore allowed them to expand extensively (up to 1200 vol %) with substantial volume increase (expansion ratio 12.8–13.1) and created pores with well-defined structures. As a result, the material exhibited outstanding oil absorption (up to 1030 wt %) due to an enlarged surface area and blend constituents. The incorporation of EPDM and radical-mediated crosslinking prevented the dissolution of SEBS in the oil and maintained the structural integrity of the foam in the oil, paving the way for recyclability. Also, the inherent hydrophobicity of employed polymers led to poor interaction with water and, thus, outstanding oil/water separation ability of the developed elastomer foams. Additionally, the foaming of triblock polymers, including SEBS, remained largely unexplored. This study has elucidated the impact of crosslinking and rheological factors on the successful foaming of SEBS.
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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".