Removal of methylene blue and safranin orange pollutants from liquid effluents by soy residue
Bibliographic record
Abstract
Abstract Methylene blue and safranin orange dyes, which are used in the textile and pharmaceutical industries, can severely damage the environment and human health. This study investigated the use of okara residue as an alternative biosorbent for the removal of methylene blue and safranin orange dyes. Substantial amounts of okara residue are generated daily during the processing of soy milk in the agro‐industrial sector. Dye adsorption was not affected by pH. An adsorption study identified the optimal experimental conditions as: 298 K, 0.03 g of adsorbent in 30 mL of dye solution at a concentration of 50 mg L −1 , and a contact time of 300 min for methylene blue dye, and 298 K, 0.02 g of adsorbent in 30 mL of dye solution at a concentration of 50 mg L −1 , and a contact time of 200 min for safranin orange dye; the maximum adsorption capacities were 93.201 ± 0.01 and 184 592 ± 0.02 mg g −1 , respectively. Okara has considerable advantages over other natural materials as an alternative for the treatment of industrial effluents. Because it is easily obtained and does not require any physicochemical treatment, adsorption does not require any specific operation temperature. In addition, okara exhibited a high adsorption capacity compared to other natural materials that require chemical and physical processing for adsorbent preparation.
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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".