Mechanism of mercury speciation transformation based on combined removal at medium-to-low temperature
Bibliographic record
Abstract
Combined removal with other coal fired pollutants was an effective method to control mercury emission during coal combustion.Accelerating the transformation of Hg0 to Hg2+ could enhance the performance of air pollution control devices in mercury emission control.Therefore,it is necessary to understand the influence factors of mercury speciation transformation.A bench scale test rig was built to simulate the process of mercury oxidation by gas components by using the Ontario Hydro Method for mercury detection at medium-to-low temperature.It was found that 90% Hg0 could be oxidized by Cl2 at the concentration of 10 μl·L-1.The process of Hg0 oxidation was sensitive within a range of SO2 concentration,out of which SO2 concentration had little influence on Hg0 oxidation.HCl,as the reaction product of H2O and Cl2,could also oxidize mercury from Hg0 to Hg2+,but its oxidation ability was weaker than Cl2,which generally made H2O an inhibitor of Hg0 oxidation.The process of NO2 decomposition would release free oxygen atoms which could oxidize mercury from Hg0 to Hg2+,so mercury oxidation by NO2 should be considered in a comprehensive kinetics model.
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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 | 0.001 |
| 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".