Silica-Silver Nanocomposites as Regenerable Sorbents for Hg<sup>0</sup>Removal from Flue Gases
Bibliographic record
Abstract
Silica-silver nanocomposites (Ag-SBA-15) are a novel class of multifunctional materials with potential applications as sorbents, catalysts, sensors, and disinfectants. In this work, an innovative yet simple and robust method of depositing silver nanoparticles on a mesoporous silica (SBA-15) was developed. The synthesized Ag-SBA-15 was found to achieve a complete capture of Hg 0 at temperatures up to 200 °C. Silver nanoparticles on the SBA-15 were shown to be the critical active sites for the capture of Hg 0 by the Ag–Hg 0 amalgamation mechanism. An Hg 0 capture capacity as high as 13.2 mg·g –1 was achieved by Ag(10)-SBA-15, which is much higher than that achievable by existing Ag-based sorbents and comparable with that achieved by commercial activated carbon. Even after exposure to more complex simulated flue gas flow for 1 h, the Ag(10)-SBA-15 could still achieve an Hg 0 removal efficiency as high as 91.6% with a Hg 0 capture capacity of 457.3 μg·g –1 . More importantly, the spent sorbent could be effectively regenerated and reused without noticeable performance degradation over five cycles. The excellent Hg 0 removal efficiency combined with a simple synthesis procedure, strong tolerance to complex flue gas environment, great thermal stability, and outstanding regeneration capability make the Ag-SBA-15 a promising sorbent for practical applications to Hg 0 capture from coal-fired flue gases.
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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".