Characterization & Batch Sorption Study for Chromium (VI) Removal fromAqueous Solutions by Activated Carbon Adsorbent Prepared from Indigenous Sugarcane Bagasse
Bibliographic record
Abstract
Chromium being a carcinogenic element present in drinking water in the less developed areas in the poor countries contributes to many infectious diseases. The removal of chromium traces from water needs to have an easy and efficient way for poor countries. Concerning this, a low-cost industrial bio-adsorbent based on bagasse (the sugar industry waste) is prepared and characterized for Cr (VI) removal from aqueous solutions. Preparation of the absorbent is performed by carbonization and steam activation of sugarcane bagasse (SCB). The FTIR spectra and the morphology of the adsorbent before and after Cr (VI) removal was studied using FTIR and SEM. All the experiments were carried out in a batch process with laboratory-prepared samples to study the effects of pH, adsorbent dose, adsorbate concentration, shaking time and shaking speed. It was observed that the highest removal efficiency was achieved at pH=2, adsorbent dose=0.75 g, adsorbate concentration=60 mg/L, shaking speed=150 rpm, and shaking time=20 minutes. These results suggest that this bio-adsorbent can provide a simple, effective, and cheap method for removing Cr (VI) ions from effluents and water resources.
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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 | 0.001 |
| 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".