Wax-wetting sponges for oil droplets recovery from frigid waters
Bibliographic record
Abstract
Energy-efficient recovery of oil droplets from ice-cold water, such as oil sands tailings, marine, and arctic oil spills, is challenging. In particular, due to paraffin wax crystallization at low temperatures, the crude oil exhibits high viscosity, making it difficult to collect using simple solutions like sponges. Here, we report a wax-wetting sponge designed by conforming to the thermoresponsive microstructure of crude oil droplets. To address paraffin wax crystallization, we designed the sponge by coating a polyester polyurethane substrate with nanosilicon functionalized with paraffin-like octadecyl ligands. The wax-wetting sponge can adsorb oil droplets from wastewater between 5° and 40°C with 90 to 99% removal efficacy for 10 cycles. Also, upon rinsing with heptol, the adsorbed oil is released within seconds. The proposed approach of sponges designed to conform with the temperature-dependent microstructure of the crude oils could enable cold water technologies and improve circular economy metrics in the oil industry.
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 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".