Column studies for biosorption of dyes from aqueous solutions on immobilised <i>Aspergillus niger</i> fungal biomass
Bibliographic record
Abstract
Biosorption is becoming a promising alternative to replace or supplement the present dye removal processes from dye wastewaters. Based on the results of batch studies on biosorption of the dyes on powdered fungal biomass, Aspergillus niger, an immobilised fungal biomass was used in column studies for removal of four dyes, Acid Blue 29, Basic Blue 9, Congo Red and Disperse Red 1 from aqueous solutions. For each dye, the effectively pretreated powdered fungal biomass was immobilised in a polysulphone matrix in the form of spherical beads. In column studies, adsorption and elution tests were conducted for each dye and the regeneration and reuse for Acid Blue 29 were carried out. The breakthrough data from column studies could be described by the Thomas model. Results of t-tests indicated that the Thomas model constants were statistically significant at 95% confidence level for Acid Blue 29 and Basic Blue 9, but not for Congo Red and Disperse Red 1. The beads had adsorption capacities of 64.7 mg/g for Acid Blue 29, 8.3 mg/g for Basic Blue 9, 1.1 mg/g for Congo Red, and 0.1 mg/g for Disperse Red 1, respectively. In the elution tests, Acid Blue 29 and Basic Blue 9 were easily desorbed from the beads, but Congo Red and Disperse Red 1 were minimally desorbed. The beads in the column retained a high adsorption capacity (91%) for Acid Blue 29 in the second cycle, which suggested that the system using A. niger biomass can be developed for the removal of certain dyes. WaterSA Vol.29(4) 2003: 465-472
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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 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".