Study of adsorption kinetic, mechanism, isotherm, thermodynamic, and design models for Cu(II) ions on sulfuric acid-modified Eucalyptus seeds: temperature effect
Bibliographic record
Abstract
The present research was investigated to remove the Cu(II) ions from aqueous solution by adsorption technology using surface-modified Eucalyptus seeds (SMES). Adsorption kinetics, mechanism, isotherms, and thermodynamic parameters were estimated. It was found that the adsorption of Cu(II) ions onto SMES follows pseudo-second-order kinetics. Adsorption mechanism was well explained with intraparticle diffusion and Boyd kinetic models. Diffusivity values of the Cu(II) ions to the SMES were estimated at different temperatures. Effective diffusivity values were estimated at 30°C: 1.9297 × 10−11, 2.1446 × 10−11, 2.0165 × 10−11, 2.2440 × 10−11, and 2.7434 × 10−11 m2 s−1 for an initial Cu(II) ions concentration of 20–100 mg L−1, respectively. Freundlich adsorption isotherm model agreed with the experimental data to a greater extent, showing the multilayer adsorption of Cu(II) ions onto SMES. The maximum monolayer adsorption capacity of SMES for Cu(II) ions was found to be 76.94 mg of Cu(II) ions g−1 of SMES at 30°C. The determinations from the thermodynamic study show that the process was feasible, spontaneous, and exothermic in nature. A single-stage batch adsorber was designed using Freundlich isotherm model, to estimate the amount of adsorbent that was needed to treat the known volume of the effluent.
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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) | 0.000 | 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".