In situ heterogeneous oxidative degradation of adsorbed cellulose-reactive anionic dye
Bibliographic record
Abstract
The release of industrial wastewater containing textile organic dyes without treatment affects human health and the environment. In this study, a two-step process to treat water contaminated with cellulose-reactive anionic dye is presented. First, the dye was adsorbed onto an inexpensive water hyacinth root powder (WHRP) bioadsorbent. Then, the dye-loaded WHRP was treated with Fenton reagents, allowing for in situ heterogeneous oxidation of the dye. The adsorption was performed from a solution containing between 100 and 300 mg dye/L, consistent with the effluent from textile processing plants. The corresponding loading on the WHRP was between 16.40 and 41.80 mg dye/g adsorbent. A short treatment time (less than 25 min) was achieved, which is more practical than previous reports that took several hours. To fully characterize the process, we studied the conditions affecting the in situ oxidation of adsorbed dye and developed a kinetic model for the reaction to find the optimal operating conditions necessary to achieve a high dye degradation percentage in a short timeframe. A first-order dependence between the reaction rate and the dye concentration was found, coupled with catalyst deactivation at a constant rate. However, increasing either the catalyst or the oxidant concentration above a certain threshold resulted in the inhibition of the dye degradation reaction. In this study, optimal dye degradation could be achieved using up to 31.60 mg dye/g adsorbent, with 0.9 M H2O2 and 3.6 mM Fe2+ at pH 3, resulting in more than 99 % dye degradation and nearly complete mineralization in less than 20 min. Thus, a relatively simple in situ dye oxidation process, well-suited for scale-up and continuous operation, was achieved.
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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".