Crop Residue Ashes as Adsorbents for Basic Dye (<scp>M</scp>ethylene Blue) Removal: Adsorption Kinetics and Dynamics
Bibliographic record
Abstract
In the quest for identifying low‐cost, locally available and effective adsorbents, pigeon pea (Cajanus cajan L.) residue ash (PRA) and sunflower (Helianthus annuus L.) residue ash (SRA) were evaluated as adsorbents for sequestering a basic dye, methylene blue (MB), from aqueous solution. Effects of contact time, initial solution pH and adsorbent dose on MB adsorption were investigated. MB adsorption onto PRA and SRA was dependent on initial solution pH, with adsorption being greater at higher pH. Percent MB removal increased with increase in adsorbent's dose. Adsorption followed pseudo‐second order kinetics. Intraparticle diffusion was not the sole rate‐controlling step. Both film diffusion and intraparticle diffusion controlled the adsorption process. The equilibrium data fitted well to Langmuir isotherm as compared to Freundlich isotherm, on the basis of coefficient of determination and error analysis. The maximum MB adsorption capacities of PRA and SRA were found as 58.8 and 62.5 mg g−1, respectively. The free energy change and dimensionless equilibrium parameter revealed spontaneous and favorable nature of MB adsorption onto crop residue ashes. Results demonstrate that the PRA and SRA have high adsorptive potential as compared to many previously reported ash based adsorbents, and hence can be used for treating water contaminated with cationic dyes.
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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".