Use of biomass-derived adsorbents for the removal of petroleum pollutants from water: a mini-review
Bibliographic record
Abstract
Over the past decades, a large amount of petroleum pollutants has been released into the environment resulting from various activities related to petrochemicals. The discharge of wastewater with petrochemicals can pose considerable risk of harm to the human health and the environment. The use of adsorbents has received much consideration across the environmental field as an effective approach for organic pollutant removal. There is a particular interest in the use of biomass adsorbent as a promising environmentally-friendly and low-cost option for removing pollutants. In this article, we present a review of biomass-derived adsorbents for the removal of petroleum pollutants from water. The features of different adsorbents such as algae, fungi, and bacteria biomasses are summarized, as is the process of removing oil and PAHs using biomass-derived adsorbents. Finally, recommendations for future study are proposed.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".