Sorbent biomaterials for cleaning up hydrocarbon spills on soil and bodies of water
Bibliographic record
Abstract
This study was aimed at identifying and evaluating natural organic materials which could be used as sorbents in clean-up operations following hydrocarbons spills on both soils and bodies of water. The sorption capacity of three materials (sugarcane fiber, coco fiber and water Eichornia crassipies was evaluated with three hydrocarbons (35°, 30° and 25°API) and two types of water (distilled and artificial marine water) adopting the ASTM F-726 standard and following the methodology suggested in the “Oil spill sorbents: testing protocol and certification listing programme” Canadian protocol. It was found that the three materials being evaluated had a sorption capacity equal to or greater than that of the commercial material to which they were compared. It was observed that sorption capacity results depended on some variables such as hydrocarbon viscosity, granulometry (particle size in Tyler sieve) and the structure of the material. Sugarcane fiber sorption in water showed the greatest hydrophobicity, different to Eichornia crassipies which is extremely hydrophilic. The materials’ sorption kinetics were determined and modelled with the three hydrocarbons (35°, 30° and 25°API). It was found that the materials became saturated in less than a minute, leading to a rapid alternative for cleaning-up and controlling hydrocarbon spills. Materials were also thermally treated for improving their hydrophobicity and behavior during spills on bodies of water. Sugarcane fiber was the material which presented the best results with the thermal treatment, followed by water Eichornia crassipies. Coco fiber did not present any significant change in its hydrophobicity.
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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".