Removal of Direct Orange 26 azo dye from water using natural carbonaceous materials
Bibliographic record
Abstract
The aim of the study was to assess the possibility of using natural carbonaceous materials such aspeat, lignite, and hard coal as low-cost sorbents for the removal of Direct Orange 26 azo dye from an aqueous solution. The adsorption kinetics and the influence of experimental conditions were investigated. The following materials were used in the research: azo dye Direct Orange 26, Spill-Sorb “Fison” peat (Alberta, Canada), lignite (Bełchatów, Poland), and hard coal (“Zofiówka” mine, Poland). The morphology and porous structure of the absorbents were tested. Dye sorption was carried out under static conditions, with different doses of sorbents, pH of the solution, and ionic strength. It was observed that the adsorption of Direct Orange 26 dye on all three adsorbents was strongly dependent on the pH of the solution, while the ionic strength of the solution did not affect the adsorption efficiency. The adsorption kinetics were consistent with the pseudo-second-order reaction model. The stage which determines the rate of adsorption is the diffusion of the dye in the near-surface layer. The process of equilibrium adsorption of Direct Orange 26 dye on all tested adsorbents is best described by the Langmuir isotherm. The maximum adsorption capacity for peat, brown coal and hard coal was 17.7, 15.1 and 13.8 mg/g, respectively. The results indicate that peat, lignite, and hard coal can be considered as alternative adsorbents for removing azo dyes from aqueous solutions.
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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) | 0.001 | 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".