Biosorption of Heavy Metals from Acid Mine Drainages onto Pig Bristles, Poultry Feathers and Crustacean Shells Industrial Biowastes
Bibliographic record
Abstract
The removal of metals ions from aqueous solutions plays an important role in water pollution control. In this study, a biosorption process for the bioremediation of heavy metal-contaminated acid mine drainages, located in Western Spain, has been developed. The process is based on the physico-chemical properties for the adsorption, ion exchange, and complexation of metal ions by biopolymers keratin and chitin from different industrial biowastes such as pig bristles, poultry feathers and crustacean shells. The selectivity for metals, the first order kinetics and yields of the corresponding biosorption processes of uranium and other metals polluting such acid mine drainages by such biosorbents are described. The biowaste rich in keratin (pig bristles) seems to show a higher biosorption capacity than that of bioresidues rich in chitin (crustacean shells). Moreover, factors such as the lower contamination by metals of acid waters, the lower influent water volume/biosorbent mass ratio, the configuration of the packed-bed reactor and the partial hydrolysis of keratin increase both the capacity and the rate of the process of metal biosorption onto the biosorbent.
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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) | 0.000 | 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".