Biosorption properties of extracellular polymeric substances towards Zn(II) and Cu(II)
Bibliographic record
Abstract
The aim of this paper was to assess the biosorption properties of extracellular polymeric substances (G-EPS, P-EPS and W-EPS) extracted from three different activated sludges called AS-G, AS-P and AS-W. The compositions of the EPSs were determined. Sludge grown in lab had more EPS than those from a Sewage Treatment Plant, and that sludge fed on glucose had more EPS than if fed on peptone. The biosorption capacities of the EPSs with two metals Cu and Zn were examined successively. The maximal biosorption capacity of EPS is increased in the following order: G-EPS > W-EPS > P-EPS. All EPSs showed stronger binding properties for Zn than Cu, and this adsorption process was described well by Langmuir and Freundlich models, respectively. The excellent fit between pseudo second-order equations and adsorption process indicates that the chemisorption may be the rate limiting step. FTIR analyses revealed that the main chemical groups involved in the interactions between metals were apparently alcohol, carboxyl and amino. These groups were part of the EPS structural polymers, polysaccharides, proteins and hydrocarbon-like products.
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 | 0.000 |
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".