Peat and coconut fiber as biofilters for chromium adsorption from contaminated wastewaters
Bibliographic record
Abstract
Batch adsorption experiments were performed for the removal of chromium (III) and chromium (VI) ions from aqueous solutions using Canadian peat and coconut fiber. The Langmuir model was used to describe the adsorption isotherm. The maximum adsorption for peat reached 18.75 mg/g for Cr(III) and 8.02 mg/g for Cr(VI), whereas the value for fiber was slightly higher and reached 19.21 mg/g for Cr(III) and 9.54 mg/g for Cr(VI). Both chromium forms could be easily eluted from the materials. The adsorption of chromium forms to organic matter could be explained in terms of formation of donor-acceptor chemical covalent bound with hydroxyl groups as ligands and chromium as the central atom in the formed complex. The chromate-reducing activities were monitored with the use of electron paramagnetic resonance spectroscopy. The results showed that both adsorption and reduction occurred simultaneously and the maximum adsorption capacity of hexavalent chromium being equal to 95% for fiber and 92% for peat was obtained at pH 1.5. The reduction of Cr(VI) in wastewaters began immediately and disappeared after 20 h. Both materials contained yeast and fungi species which can be responsible for reduction of chromium compounds, due to their enzymatic activity (Chwastowski and Koloczek (Acta Biochim Pol 60: 829-834, 2013)). The reduction of Cr(VI) is a two-phase process, the first phase being rapid and based on chemical reaction and the second phase having biological features. After the recovery step, both types of organic materials can be used again for chromium adsorption without any loss in the metal uptake. Both of the materials could be used as biofilters in the wastewater treatment plants.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".