Adsorption of copper from water using TiO2-modified activated carbon derived from orange peels and date seeds: Response surface methodology optimization
Bibliographic record
Abstract
This study evaluated the application and efficiency of modified activated carbon in the removal of copper (Cu) from synthetic aquatic samples. The surface of activated carbon derived from orange peel (AC-OP) and date seeds (AC-DS) have been modified by Titanium dioxide nanoparticles (TiO 2 NPs) (1:10 wt% mixing ratio) and used in a series of experiments designed by Response Surface Methodology (RSM) incorporating Central Composite Design (CCD). The Brunauer-Emmett-Teller (BET) test demonstrated that the modification has increased the surface area of AC-OP from 2.40 to 6.06 m 2 g −1 and AC-DS from 51.10 to 81.37 m 2 g −1 . Effects of pH (1–7), ion initial concentration (10–60 mg L −1 ), adsorbent dose (0.5–8 g L −1 ), and contact time (0.4–6 h) have been investigated. The results showed that the optimum conditions for TiO 2 -modified AC-OP (OP–TiO 2 ) are pH 5, initial concentration of 24.6 mg L −1 , adsorbent dose of 4.9 g L −1 , and contact time of 3.6 h. The optimum conditions for TiO 2 -modified AC-DS (DS-TiO 2 ) are pH 6.4, initial concentration of 21.2 mg L −1 , adsorbent dose of 5 g L −1 , and contact time of 3.0 h. The modified quadratic models represented the results well with regression coefficients of 0.91 and 0.99 for OP-TiO 2 and DS-TiO 2 , respectively. The maximum Cu removal for OP-TiO 2 and DS-TiO 2 were 99.90 % and 97.40 %, and the maximum adsorption capacity was found to be 13.34 and 13.96 mg g −1 , respectively. Kinetic data have been fitted to pseudo first-order, pseudo second-order, intra-particle diffusion, and Elovich models. The pseudo second-order showed a better fit to the experimental data (R 2 > 98 %). This study demonstrates the successful development of modified activated carbon derived from orange peels and date seeds, modified by TiO 2 nanoparticles, for efficient adsorption of copper ions from water. The findings contribute to understanding the adsorption mechanism and provide valuable insights for designing environmentally friendly adsorbents.
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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) | 0.001 | 0.001 |
| 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 | 0.001 | 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".