Using Hg2+ as a Precursor to Calibrate Gaseous Elemental Mercury from Pyrolysis of Coal
Bibliographic record
Abstract
Mercury is easily volatile and vaporizes in air. The monitor of mercury in gas phase provides insight for developing advanced Hg control technologies. However, the analytical method of gas mercury is rudimentary. In order to determine the gas mercury content, a reliable calibration method is needed. This study provides a method of calibrating gas mercury from pyrolysis of coal with Hg2+ as a precursor. The method is based on the principle that Hg2+ in aqueous solution can be quantitatively reduced by reductant into elemental mercury vapor to determine the calibration of mercury content in gas phase. Compared with the conventional calibration method based on the saturated vapor principle, this method eliminates the influence of the sampling temperature, the quantity of mercury during sampling can be easily controlled, and pollution of the gaseous mercury stored in the laboratory is avoided. To illustrate the accuracy of the calibration method, the quantity of elemental mercury emission during pyrolysis of four coals were determined. Results show that the method has good reproducibility and adaptability. Compared with the Canadian Ontario-Hydro method, the relative standard deviations of both methods are less than 3.0%, indicating a high reliability of this calibration method.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".