Development of a Novel Mercury Cartridge for Mercury Analysis<sup>†</sup>
Bibliographic record
Abstract
Mercury cartridges, which are a key component of semicontinuous online mercury monitors, capture low-concentration mercury from the effluent streams of coal-fired power plants and subsequently release highly concentrated pulses of mercury for spectroscopic analysis. The most common sorbent used in mercury cartridges is gold-coated silica beads (Au/SiO 2 ), which form a reversible amalgam with elemental mercury. Ag/MC is a robust composite mercury sorbent, consisting of silver nanoparticles supported on the surface of natural chabazite, which can efficiently capture and release mercury from a real flue gas environment, making the material a potential alternative to Au/SiO 2 in mercury preconcentration cartridges. The performance of Au/SiO 2 - and Ag/MC-based mercury cartridges in capturing low-level mercury in Ar-, SO 2 -, and NO-containing gas streams was investigated systematically. Both SO 2 and NO were determined to be harmful to the performance of an Au/SiO 2 mercury cartridge. NO had limited impact on the performance of Ag/MC, but the presence of SO 2 led to reduced mercury recovery from the Ag/MC mercury cartridge. Soda lime was proven to be an effective, NO-tolerant SO 2 scrubber. Based on these results, a novel SO 2 - and NO-tolerant mercury cartridge was designed and fabricated using soda lime as a disposable SO 2 scrubber and Ag/MC as the reversible mercury sorbent.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".