Studies on Removal of Cr (VI) from Aqueous Solutions Using Powder of Mosambi Fruit Peelings (PMFP) As a Low Cost Sorbent
Bibliographic record
Abstract
The powder of mosambi fruit peelings (PMFP) was used as an adsorbent for the removal of heavy metal like Cr (VI) from aqueous solutions was studied using batch tests. The influence of physico‐chemical key parameters such as the initial metal ion concentration, pH, agitation time, adsorbent dosage, and the particle size of adsorbent has been considered in batch tests. Sorbent ability to adsorb Cr (VI) ions was examined and the mechanism involved in the process investigated. The optimum results were determined at an initial metal ion concentration was 10 mg/lit, pH=2, agitation time – 60 min, an adsorbent dose (150 mg/50 ml) and the particle size (0.6 mm). The % adsorption, Langmuir constants [Q0=7.51(mg/g) and b=1.69(mg/lit)] Freundlich constant(Kf=2.94), Lagergren rate constants (Kad (min-1)=5.75 x 10-2) for [Cr(VI)] 10 mg/lit were determined for the adsorption system as a function of sorbate concentration. The equilibrium data obtained were tested using Langmuir, Freundlich adsorption isotherm models, and the kinetic data obtained were fitted to pseudo first order model.
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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) | 0.001 | 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 | 0.001 | 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".