Adsorption of copper on glass beads coated with chitosan: Stirred batch and fixed bed analysis
Bibliographic record
Abstract
Chitosan is a natural polymer largely studied for heavy metal adsorption applications, since the amino and hydroxyl groups present in its structure can act as chelation sites. However, this material presents some drawbacks as an adsorbent due to its low mechanical strength. Studies have shown that the use of immobilized chitosan on glass beads can improve the mechanical strength of adsorbent and enhance mass transfer properties. For this reason, stirred batch experiments of copper adsorption on chitosan immobilized onto glass beads were performed to estimate the surface diffusion coefficient and the chitosan film thickness, considering an inert solid glass core. The kinetic data were modelled by a surface diffusion model incorporating the external film mass transfer resistance. Column experiments were also performed for copper solution at different flow rates and a film‐surface diffusion model was used to describe the breakthrough adsorption experiments, using the chitosan film thickness estimated from the batch experiment (φ = 2.5 µm). The input parameters for this model were determined by batch experiments or estimated from correlations available in the open literature. The surface diffusion coefficients (0.98–1.72 × 10−10 cm2 · min−1) of copper in the chitosan film for different flow rates were estimated. The experimental data and the model agreed, indicating that the film thickness and the mass transfer parameters were well predicted.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".