Mercury Removal from Flue Gases by Novel Regenerable Magnetic Nanocomposite Sorbents
Bibliographic record
Abstract
Magnetic zeolite composites with supported silver nanoparicles are a new class of multifunctional materials with potential applications as recyclable catalysts, disinfectants, and sorbents. This study evaluated the suitability of the magnetic composites as sorbents for the removal of elemental mercury vapor from flue gases of coal-fired power plants. The sorbents were found to completely capture mercury at temperatures up to 200 degrees C, and the mercury capacity of the sorbents was found to be affected by the state, content, and size of the silver particles in the composite. Cumulative or extended thermal treatments at 400 degrees C were found to improve the mercury capture capacity, allowing the sorbent to be regenerated and recycled multiple times without performance degradation. The magnetic sorbent was readily separated from fly ash by magnetic separation, leaving the fly ash essentially free of sorbent contamination. In initial in-plant tests, the sorbents were able to capture mercury from the flue gases of an operational, full-scale, coal-fired power plant The combination of mercury capacity, ease of separation and regeneration, and recyclability makes these multifunctional magnetic composites excellent candidate sorbentsforthe control of mercury emissions from coal-fired power plants.
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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 | 0.000 | 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 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".