Advancing Hyper-Crosslinked Materials with High Efficiency and Reusability for Oil Spill Response
Bibliographic record
Abstract
Abstract Developing materials with high efficiency for recovering oil to mitigate the environmental impact of oil spills has always been a challenging task. A commercial melamine formaldehyde sponge was coated with an optimised superhydrophobic/superoleophilic hyper-crosslinked polymer and applied to the removal of crude oil from oil-in-water emulsions for the improvement of oil spill clean-up processes. The high surface area, porosity, hydrophobicity, and selectivity of oil over water made the HPCS ideal sorbent for efficient oil/water separation. The system was able to strip crude oil from water emulsions of 1000 ppm to a negligible level of 2 ppm oil with minimal amounts of the HPCS material. More importantly, the HPCS material could be reused via a simple mechanical compression process, and the uptake capacity was retained over ten cycles. For five cycles of oil adsorption/mechanical compression the HPCS was able to provide water filtrate with oil concentrations of under 15 ppm. This is an effective and economical recovery system, removing the need for consistent solvent washing and drying processes. These results suggest that the HPCS is a promising material for oil/water separation and recovery under challenging conditions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".