Characterization of Mercury Binding onto a Novel Brominated Biomass Ash Sorbent by X-ray Absorption Spectroscopy
Bibliographic record
Abstract
Recent laboratory and field-scale experiments demonstrated the potential for brominated industrial solid waste from biomass combustion (Br-Ash) to be an efficient, cost-effective alternative to activated carbon for capturing mercury from coal-fired power plants. To develop this attractive alternative technology to a commercially sustainable level, a better understanding of mercury capture mechanisms by Br-Ash is required. For this purpose, X-ray absorption fine-structure (XAFS) spectra of Br-Ash were collected at the Hg L(III)-edge, Br K-edge and S K-edge, and analyzed to determine the local bonding environment of mercury atoms. The coordination environment of Hg was compared with that on a commercial brominated activated carbon. Our results indicate that the mercury was captured by chemisorption on both the commercial and biomass ash sorbents; however, the mercury binding environment was different for each sorbent. Mercury was found to bind to the reduced sulfur by the commercial brominated activated carbon, in contrast to mercury binding with carbon and bromine on the brominated biomass ash. Based on the results obtained, a mechanism of Hg capture involving oxidation of elemental Hg followed by binding of the oxidized mercury on the surface of the sorbent near Br was proposed for the brominated biomass ash.
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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) | 0.001 | 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".