Review on the Removal of Metal Ions from Effluents Using Seaweeds, Alginate Derivatives and Other Sorbents
Bibliographic record
Abstract
Biosorbents, especially those derived from seaweed (macroscopic algae) and alginate derivatives, exhibit high affinity for many metal ions. Because biosorbents are widely abundant (usually biodegradable) and less expensive than industrial synthetic adsorbents, they hold great potential for the removal of toxic metals from industrial effluents. Various studies have demonstrated the efficiency of living and non-living micro-organisms, such as bacteria, yeasts, moulds, micro-algae, cyanobacteria and biomass from water treatment sewage to remove metals from solution. Several types of organic and inorganic biomass have also been used as sorbent materials. In addition, by-products from the forestry industry, as well as agriculture waste and natural sorbents, have also been studied. This paper reviews and summarizes some key recent developments in these areas and it describes and discusses some specific applications of selected natural sorbents.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".