Magnesium Oxide Nanoparticles for the Adsorption of Pentavalent Arsenic from Water: Effects of Calcination
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Bibliographic record
Abstract
The occurrence of heavy metal ions in water is intractable, and it has currently become a serious environmental issue to deal with. The effects of calcining magnesium oxide at 650 °C and the impacts on the adsorption of pentavalent arsenic from water are reported in this paper. The pore nature of a material has a direct impact on its ability to function as an adsorbent for its respective pollutant. Calcining magnesium oxide is not only beneficial in enhancing its purity but has also been proven to increase the pore size distribution. Magnesium oxide, as an exceptionally important inorganic material, has been widely studied in view of its unique surface properties, but the correlation between its surface structure and physicochemical performance is still scarce. In this paper, magnesium oxide nanoparticles calcined at 650 °C are assessed to remove the negatively charged arsenate ions from an aqueous solution. The increased pore size distribution was able to give an experimental maximum adsorption capacity of 115.27 mg/g with an adsorbent dosage of 0.5 g/L. Non-linear kinetics and isotherm models were studied to identify the adsorption process of ions onto the calcined nanoparticles. From the adsorption kinetics study, the non-linear pseudo-first order showed an effective adsorption mechanism, and the most suitable adsorption isotherm was the non-linear Freundlich isotherm. The resulting R2 values of other kinetic models, namely Webber-Morris and Elovich, were still below those of the non-linear pseudo-first-order model. The regeneration of magnesium oxide in the adsorption of negatively charged ions was determined by making comparisons between fresh and recycled adsorbent that has been treated with a 1 M NaOH solution.
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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 | 0.000 | 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 it