Hybrid sol–gel silica adsorbent material based on grape stalk applied to cationic dye removal
Bibliographic record
Abstract
Abstract The removal of azo‐dyes, recalcitrant compounds found in several industrial effluents, was investigated by an environmentally friendly hybrid adsorbent material based on grape stalk encapsulated by the sol–gel method. The winemaking process produces a large amount of grape stalk. This solid waste represents an environmental problem for wineries. The hybrid adsorbent based on grape stalk was synthetized by the acid catalyzed sol–gel route and characterized by different techniques. According to the zeta potential values, the resulting hybrid material is negatively charged at a wide pH range (2.0–12.0). The adsorption of the cationic azo‐dye Basic Blue 41 onto the hybrid material was evaluated in aqueous solution. The studies of pH (3.0–12.0), adsorbent concentration (0.5–5.0 g L−1), kinetic and adsorption equilibrium were investigated at 303 K. The kinetic data were best fitted to the pseudo‐second order kinetic model. Equilibrium studies showed that the hybrid material achieved a maximum adsorption capacity of 205.3 mg g−1. The large amount of solid waste generated from wine production represents a serious impasse for wineries regarding its storage and disposal, from both ecological and economical points of view. Alternatively, this agricultural solid waste may represent a source of raw material to create new products, increasing the life cycle and adding value to the previously discarded material. In this work, grape stalk was employed as adsorbent for dye removal, promoting the reduction of environmental contamination. The results showed that it is a promising material to use as efficient eco‐friendly adsorbent for the treatment of wastewater.
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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".