Preparation and Evaluation of GAC-Based Iron-Containing Adsorbents for Arsenic Removal
Bibliographic record
Abstract
Granular activated carbon-based, iron-containing adsorbents (As-GAC) were developed for effective removal of arsenic from drinking water. Granular activated carbon (GAC) was used primarily as a supporting medium for ferric iron that was impregnated by ferrous chloride (FeCl2) treatment, followed by chemical oxidation. Sodium hypochlorite (NaClO) was the most effective oxidant, and carbons produced from steam activation of lignite were most suitable for iron impregnation and arsenic removal. Two As-GAC materials prepared by FeCl2 treatment (0.025 -0.40 M) of Dacro 20 x 50 and Dacro 20 x 40LI resulted in a maximum impregnated iron of 7.89% for Dacro 20 x 50 and 7.65% for Dacro 20 x 40Ll. Nitrogen adsorption-desorption analyses showed the BET specific surface area, total pore volume, porosity, and average mesoporous diameter all decreased with iron impregnation, indicating that some micropores were blocked. SEM studies with associated EDS indicated that the distribution of iron in the adsorbents was mainly on the edge of As-GAC in the low iron content (approximately 1% Fe) sample but extended to the center at the higher iron content (approximately 6% Fe). When the iron content was > approximately 7%, an iron ring formed at the edge of the GAC particles. No difference in X-ray diffraction patterns was observed between untreated GAC and the one with 4.12% iron, suggesting that the impregnated iron was predominantly in amorphous form. As-GAC could remove arsenic most efficiently when the iron content was approximately 6%; further increases of iron decreased arsenic adsorption. The removal of arsenate occurred in a wide range of pH as examined from 4.4 to 11, but efficiency was decreased when pH was higher than 9.0. The presence of phosphate and silicate could significantly decrease arsenate removal at pH > 8.5, while the effects of sulfate, chloride, and fluoride were minimal. Column studies showed that both As(V) and As(III) could be removed to below 10 microg/L within 6000 empty bed volume when the groundwater containing approximately 50 microg/L of arsenic was treated.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".