Metal-Impregnated Petroleum Coke-Derived Activated Carbon for the Adsorption of Arsenic in Acidic Waters
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide The efficacy of metal-impregnated petroleum coke (PC) activated carbon for the adsorption of arsenite and arsenate in acidic waters is investigated in this study. Unmodified PC activated carbon, FeCl 3 -loaded activated carbon, KMnO 4 -loaded activated carbon, and a mixed FeCl 3 –KMnO 4 -loaded activated carbon were used for evaluation. The surface characteristics of the activated carbons before and after arsenic adsorption were analyzed by X-ray photoelectron spectroscopy (XPS). Arsenate adsorption was significantly improved by the addition of an iron–manganese-loaded activated carbon, increasing adsorption from 8.12 to 50.93%. Oxidation–reduction reactions are proposed based on the observed arsenic 2p 3/2, iron 2p 3/2, and manganese 2p 3/2 XPS peaks. While iron in the iron-loaded activated carbon is not acting as the reducing agent, it is acting as a conductor for the flow of electrons from the activated carbon to the arsenic for reduction to take place prior to the physisorption of the arsenic. In the manganese-loaded activated carbon, manganese acts as the reducing agent for arsenic prior to arsenic adsorption to the surface through physisorption. XPS of the post-arsenic(V) exposure samples showed that the Fe 2 O 3 species were reduced from 32.18 to 1.66% in the FeMn-loaded sample, while the FeOOH species were increased from 53.16 to 81.71%. Similarly, MnO in the FeMn-loaded activated carbon dropped from 26.82 to 15.40%, while MnOOH and MnO 2 increased from 39.98 and 33.20 to 43.96 and 40.64%, respectively. This is consistent with the proposed mechanism. The adsorption of arsenite was also evaluated to show that the modification of the activated carbon adsorbed not only the arsenic(V) species but also the more toxic arsenic(III) species without the need for oxidation of the arsenic prior to adsorption.
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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 | 0.000 | 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 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".