Iron Oxide Coated Sand for Arsenic Removal: Investigation of Coating Parameters Using Factorial Design Approach
Bibliographic record
Abstract
Iron oxide-coated sand filtration is reported as one of the most viable technological options for arsenic removal from drinking water by the United States Environmental Protection Agency. For substantial utilization of the adsorption properties of iron oxide-coated sand for arsenic removal, it is important to understand the factors contributing to its adsorption capabilities. The effects of seven factors i.e., coating pH, temperature, iron concentration, number of coatings, aging, pH of the solution and mass of the adsorbent on arsenic (V) and arsenic (III) removal were investigated. Two sets of 27-4 fractional factorial design were adopted to identify the significant factors in the arsenic adsorption process. The results showed that coating pH, temperature, and solution pH had the most significant influence on As(V) removal and coating pH, temperature, solution pH and mass of adsorbent had the greatest effect on As(III) removal. The effects of other factors were relatively small on arsenic removal.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".