Synthesis of ZnO-TiO2/activated carbon (ACαZnO/TiO2) nanoparticles and its application in adsorption of arsenic from aqueous media: study of isotherm and adsorption kinetics and optimization using response surface methodology-central composite design
Bibliographic record
Abstract
ABSTRACT Arsenic contaminated water is a serious threat to human health. Therefore, the aim of this study was to use a new method of stabilization of ZnO/TiO 2 on activated carbon (I ZnO/TiO 2 ) for the effective removal of arsenic from aqueous solutions. In this experimental study, a container with a useful volume of 3.14 L (height of 40 cm and diameter of 10 cm) was used. For this purpose, four main factors including pH (3–11), nanosorbent dose (1–3 g/L), initial arsenic concentration (1–10 mg/L), and reaction time (30–300 min) as effective factors in the arsenic removal efficiency. The results showed that arsenic adsorption increased with increasing contact time, adsorbent dose, and decreasing pH and arsenic concentration. A quadratic model was selected to estimate the removal of arsenic by the adsorption process with the modified adsorbent under study. The linear regression coefficient ( R 2 ) between experiments and different response values in the model for arsenic was >0.99. The optimal value for the studied variables was obtained for pH of 6.75, arsenic concentration of 9.76 mg/L, reaction time of 287.62 min, and nanosorbent dosage of 2.45 g/L. The maximum arsenic adsorption capacity under optimal conditions was predicted to be 4.53 mg/g. The results showed that the studied adsorbent for arsenic removal follows the Langmuir isotherm and quadratic kinetics ( R 2 > 0.99). The results of this study showed that the adsorption process using nano-photocatalytic adsorbents of TiO 2 and ZnO has relatively high efficiency in arsenic adsorption and can be used as a suitable complementary treatment method for water and wastewater containing carcinogenic heavy metals such as arsenic.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 teacher head, 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".