Sugarcane Bagasse-Derived Cellulose as an Eco-Friendly Adsorbent for Azo Dye Removal
Bibliographic record
Abstract
Cellulose has been isolated from sugarcane bagasse and applied for azo dye adsorption.Hydrolysis process for cellulose isolation was performed with different concentrations of HCl and the best concentration was 20% (v/v).Fourier Transform Infrared Spectroscopy (FTIR), X-ray Diffraction (XRD), and Thermogravimetric Analysis (TGA).The increase in peak intensity at wavenumber 897 cm -1 was connected to cellulose stretching C-O and C-H, which resulted in greater cellulose content following hydrolysis.The thermal resistance of cellulose enhanced after treatments due to the significant intermolecular hydrogen bonding in the cellulose particles.Cellulose particles have been studied for their capacity to eliminate methylene blue (MB) from aqueous solutions.The analysis indicated that the OH, C=O, and C-O groups were particularly important in azo dye adsorption based on FTIR data.After 120 minutes of methylene blue adsorption and adsorption conditions at pH 10, a very high percent removal was obtained, precisely 99.7%.Based on adsorption kinetics, MB adsorption adopted a pseudo-second-order model (PSO) with an adsorption rate constant (k2) was 0.0276 g/mg.min.According to isotherm investigations, Freundlich isotherm model dependent adsorption (adjusted-R 2 =0.928), with Kf = 66.386 mg/g.
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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".