On the use, and reuse, of polymers for the treatment of hydrocarbon contaminated water via a solid–liquid partitioning bioreactor
Bibliographic record
Abstract
Aqueous environments contaminated with diesel components pose a threat to the native biota due to the intrinsically toxic nature of the many hydrocarbon compounds present. In the event of diesel being released into an aqueous environment it is imperative that the contaminant is recovered in a rapid manner to ensure the safety of aquatic organisms as well as to maintain desired water quality. The research presented in this study investigates the potential of polymeric sorbents to recover diesel from a contaminated aqueous source. Thermoplastic materials, such as styrene butadiene derived polymers, were shown to substantially reduce diesel levels in excess of 98% with 90% of this recoverable fraction being removed in less than 30 min. Recyclable materials, such as used automobile tires, were shown to obtain similar results with added potential benefit including lower cost and reuse of a waste material. The polymeric sorbents were also biologically regenerated and this was accomplished in a solid-liquid two-phase partitioning bioreactor, in which 65% of the initial diesel contamination was degraded within a 9 day period. The result of this work was the demonstration of a low cost, reusable remediation technology for the recovery, and destruction of diesel from aqueous environments.
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 | 0.001 |
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".