Preparation of pretreated sugarcane bagasse and chitosan bio-composite for the adsorption of phosphate from aqueous media
Bibliographic record
Abstract
In this work, we report on the biosorbent bio-composite consisting of pretreated sugarcane bagasse, chitosan, and epichlorohydrin, which was prepared using the blending method for the adsorption of phosphate from aqueous media. The effect of solvent concentration was investigated, and it was observed that the high solvent concentration significantly enhanced the extraction of cellulose. Moreover, pretreatment enhances the compatibility between the extracted cellulose and chitosan. Furthermore, the addition of epichlorohydrin significantly enhanced the adsorption capacity as well as the stability of the synthesized biosorbent bio-composite. Fourier transform infrared, X-ray photoelectron spectroscopy, scanning electron microscope, X-ray diffraction, and thermogravimetric analysis were used to characterize the prepared bio-composite. Batch studies were conducted to investigate the effectiveness of the bio-composite. The mechanism of adsorption between the biosorbent bio-composite and phosphate was through chemisorption as the data best fitted to the pseudo-second-order kinetic model. The isotherm study showed that the data best fitted to the Freundlich adsorption isotherm model, and the Langmuir adsorption isotherm model showed that the maximum adsorption capacity was 15.26 mg/g. The biosorbent was found to be pH dependent, selective, and recyclable.
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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) | 0.001 | 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 | 0.001 |
| 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".