A Novel Approach for Modification of Biosorbent by Silane Functionalization and its Industrial Application for Single and Multi-Component Solute System
Bibliographic record
Abstract
Abstract The potential of an economically cheap raw material (rice husk) was evaluated in the present study to remove dyes including reactive yellow 15 (RY15) and reactive red 241 (RR241) in single and multi-component systems. The adsorbent was modified and functionalized chemically using glycidoxypropyltrimethoxysilane, sulfur and silane to enhance the removal efficiency of pollutants. The modified rice husk was evaluated by scanning electron microscope (SEM) and Fourier transform Infrared Spectroscopy (FTIR). Batch adsorption study showed that the modified rice husk with silane graft (RHSi) had highest removal efficiency of both dyes with 20% more removal compared to raw rice husk. The sorption correlated well with Langmuir, Freundlich, SIPS and Redlich-Peterson models for adsorption. Highest sorption was obtained at 10 mg L−1 of dye, 50 °C, 200 mg g−1 of adsorbent dose and pH 4. The mixture of two dyes poorly fit to the original Langmuir but fit best to the Langmuir-like model. This indicates that competitive Langmuir-like model considers that the capacities of adsorbents are equal. Results showed that the components compete for the available binding sites on adsorbent surface. It was also indicated that silane grafting can offer comparatively more binding sites compared to the raw rice husk and single-solute isotherm parameters cannot used for multi-component solute system.
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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".