The Use of Modified Beetroot Fibers by Sodium Dodecyl Sulfate (SDS) Cleaning Water Contaminated by Organic and Inorganic Compounds
Bibliographic record
Abstract
The beetroot fibers were used to decontaminate water polluted by methylene blue dye (MB), to remove heavy metals from wastewater and to soften hard water. In order to improve the adsorbent performance and to determine the optimum conditions of industrial wastewater cleaning, the effect of fiber particle sizes, initial concentrations of pollutants, pH of aqueous solutions and effluent rates on the cleaning procedure were investigated. Data showed that the efficiency of cleaning increased when fiber particle size decreased (from mm to µm scale). Optimum pH value for adsorption was 6 to 6.5. Maximum metal cations retention or hardness of modified fibers by sodium dodecyl sulfate (SDS) was estimated at 70 mg per gram of fiber; while the maximum retention of methylene blue was found to be 300 mg of dye per gram of fiber. Chemical modification of fibers by an anionic surfactant such as sodium dodecyl sulfate increased the efficiency of the dye elimination by 2-fold when compared to unmodified fibers. The adsorption parameters were determined using Langmuir and Freundlich isotherms.
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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".