Arsenic removal from groundwater through iron oxyhydroxide coated waste products
Bibliographic record
Abstract
The goal of this research was to remove arsenic from groundwater supplies via adsorption into media obtained from waste material generated as by-products from glass recycling programs and the seafood industry such as crushed glass and scallop shells. During the course of this research four new adsorbents were developed: ferric hydroxide coated crushed glass (FHCCG); ferric oxide coated crushed glass (FOCCG); ferric hydroxide coated scallop shells (FHCSS); and ferric oxide coated scallop shells (FOCSS). The adsorbents were characterized through evaluation of their structure, surface area, chemical composition, iron content, and coating stability. Efficiency of the adsorbents to remove arsenic from water was examined through batch kinetic and isotherm adsorption experiments. The adsorption capacity of the adsorbents was also evaluated by performing column experiments using real ground waters and a synthetic water. Arsenic removal to a concentration less than 10 μg/L was achieved with the FHCSS and more than 9000 bed volumes of water were treated before the breakthrough point was reached. The research results revealed that scallop shells coated with ferric hydroxideperformed better than crushed glass coated with ferric hydroxide. Both FOCCG and FOCSS had poor arsenic removal compared with FHCSS and granular ferric hydroxide (GFH). Ferric hydroxide coated scallop shells performed similarly to GFH.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".