Fast, Cost-effective and Energy Efficient Mercury Removal-Recycling Technology
Bibliographic record
Abstract
Abstract We herein present a novel and sustainable technology for mercury recycling, with the maximum observed uptake capacity. Facile synthesis of the most efficient (~1.9 gg−1) nano-trap, made of montmorillonite-Fe-iron oxides, was performed to instantaneously remove mercury(II) ions from water. Elemental Hg was recovered from the adduct, by employing Fe granules, at ambient conditions. Varied pHs and elevated temperatures further enhanced this already highly efficient recycling process. The reduction of Hg(II) to Hg(I) by the nano trap and Hg(I) to Hg(0) by Fe granules are the main driving forces behind the recycling process. Facile sustainable recycling of the nano-trap and Fe granules require no additional energy. We have further developed a recyclable model for Hg nano-trap, which is inexpensive (<$5 CAD), and can remove mercury in a few seconds. This technology has multiple applications, including in the communities exposed to mercury contamination.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".