Assessment of azo-based dyes biosorption capacity by <i>Ganoderma lucidum</i> from textile industries: mechanism and adsorption equilibrium
Bibliographic record
Abstract
The fungus Ganoderma lucidum demonstrates significant potential for the adsorption of azo dyes from textile effluents. Among seven assessed azo dyes – Acid Blue 161, Congo Red, Reactive Blue SS222, Reactive Red 43, Remazol Black GF, Direct Blue 21, and Reactive Yellow ME145 – the highest and lowest adsorption capacities, recorded at 500 mg/L, were 65.83% for Acid Blue 161 and 27.77% for Reactive Red 43, respectively. A comparison of dried and wet G. lucidum biomass revealed that wet biomass exhibited 29% higher biosorption. Furthermore, live biomass achieved 26.19% greater removal of Acid Blue 161 compared to dead biomass. Pretreatment of fungal biomass with 10% acetic acid enhanced dye biosorption by 28%. The desorption process was examined using hydrochloric acid (0.01 M) and sodium hydroxide (0.01 M), resulting in the lowest and highest desorption rates of 4.7% and 63%, respectively. The decrease in biosorption efficiency with a temperature increase from 30 °C to 50 °C confirmed the exothermic nature of the adsorption process. The maximum biosorption capacity, as determined by the Langmuir model, was 232.55 mg/g. Scanning electron microscopy (SEM) and Fourier-transform infrared spectroscopy (FTIR) analyses revealed the critical role of amine groups in the fungal cell wall structure during the biosorption process. This study highlights the environmental benefits of G. lucidum and its efficacy in detoxifying azo dyes from textile industrial effluents, making it a promising solution for sustainable wastewater treatment.
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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) | 0.000 | 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".