Performance of a diatomite-based sorbent in removing mercury from aqueous and oil matrices
Bibliographic record
Abstract
Performance of diatomite based mercury sorbent (Chromosorb® QSR) powder and pellets was studied. Test results indicated that this sorbent was highly effective in removing various mercury species from aqueous and oil matrices. More than 99% removal was achieved after 30 min treatment with the diatomite sorbent powder at 1 g/L sorbent loading for an initial ionic mercury concentration of 9700 ppb. The diatomite sorbent was also stable when stored at oxidizing condition or at high temperature up to 200 °C. For continuous mercury removal process, the diatomite sorbent powder can be used as filter aid in pressure filters and the diatomite sorbent pellets can be used in fixed bed columns. A number of case studies presented on actual industrial wastewater and crude oil samples further indicate that this new mercury sorbent could provide a practical solution to mercury contamination problems in many industries.
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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 | 0.001 | 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".