Bibliographic record
Abstract
Abstract Biosorption is becoming a promising alternative to replace or supplement the present dye removal processes from dye wastewater. In this study, removal of a dye, Basic Blue 9, from an aqueous solution by biosorption on the dead fungal biomass Aspergillus niger was studied. The effective pretreatment method for increasing the biosorption capacity of A. niger was investigated. Autoclaving was found to effectively enhance the biosorption capacity of A. niger to 18.54 mg/g compared with 1.17 mg/g of living cells for Basic Blue 9. Batch pH, kinetic and isotherm studies were conducted to evaluate the biosorption capacity of the pretreated (dead) biomass. The initial pH of the dye solution strongly affected the biosorption capacity and rate. The effective initial pH was between 4 and 6. The equilibrium time varied with the initial pH of the dye solution and was set at 30 h because no significant dye removals occurred after that time of contact. The Lagergren and Ho et al. models can be used to describe the kinetics of Basic Blue 9 biosorption on A. niger successfully for different initial pH values, except for pH 4. At initial pH 4, biosorption of Basic Blue 9 fitted the Langmuir equation well; at initial pH 10, the Langmuir and Freundlich isotherm models both fitted biosorption well. The results of this study indicated that fungal biomass of A. niger can be used for removing Basic Blue 9 from an aqueous solution.
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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".