Novel Regenerable Sorbent for Mercury Capture from Flue Gases of Coal-Fired Power Plant
Bibliographic record
Abstract
A natural chabazite-based silver nanocomposite (AgMC) was synthesized to capture mercury from flue gases of coal-fired power plants. Silver nanoparticles were engineered on zeolite through ion-exchange of sodium ions with silver ions, followed by thermal annealing. Mercury sorption test using AgMC was performed at various temperatures by exposing it to either pulse injection of mercury or continuous mercury flow. A complete capture of mercury by AgMC was achieved up to a capture temperature of 250 degrees C. Nano silver particles were shown to be the main active component for mercury capture by amalgamation mechanism. Compared with activated carbon-based sorbents, the sorbent prepared in this study showed a much higher mercury capture capacity and upper temperature limit for mercury capture. More importantly, the mercury captured by the spent AgMC could be easily released for safe disposal and the sorbent regenerated by simple heating at 400 degrees C. Mercury capture tests performed in real flue gas environment showed a much higher level of mercury capture by AgMC than by other potential mercury sorbents tested. In our mercury capture tests, the AgMC exposed to real flue gases showed an increased mercury capture efficiency than the fresh AgMC.
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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) | 0.000 | 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".