A comprehensive review on the role of biosurfactants in remediation of heavy metals from contaminated environment
Bibliographic record
Abstract
The hazardous nature of heavy metals (HMs) in the ecosystem has drawn global attention due to its emergence as a major environmental and public health concern. As toxic metals are non-degradable, they affect not only animals but also human beings and vegetation. These HMs are long-lasting pollutants that travel vast distances from their site of origin in the environment and bioaccumulate in humans and other organisms through the food chain. These pollutants can cause extensive pollution since they are frequently produced by industrial activities and inappropriate disposal procedures. Biosurfactants’ (BSs) versatility makes them an appealing category that plays an essential role in various biotechnological applications for environmental remediation. BSs have a variety of characteristics, including metal binding, solubilization, and emulsification. They remediate the HMs through processes like complexation, ion exchange, and metal solubilization. Furthermore, owing to their amphiphilic nature, they improve the sorption and solubility of hydrophobic contaminants and reduce the surface area and interfacial tension of immiscible liquids. As a result, BS-based remediation plays an important part in the heavy metal removal process in multiple ways. This review aims to provide information on the function of BSs in eliminating HMs through bioremediation processes for environmental sustainability.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".