Performance Evaluation of Functionalized Biocarbon for Mercury Capture
Bibliographic record
Abstract
In this study, functionalized biocarbon (FBC) as a sorbent to capture mercury (Hg) from flue gas was developed and tested. The sorbent before and after Hg capture was characterized. The developed sorbent was tested for elemental Hg (Hg 0 ) capture efficiency in a (i) Hg pulse injection test, in an argon atmosphere, (ii) simulated flue gas of (a) 350 ppm of SO 2, 5% O 2, and balanced with N 2 or (b) 300 ppm of NO 2, 5% O 2, and balanced with N 2, and (iii) coupon test in a commercially operating coal-fired power plant. Hg 0 capture by FBC in Hg pulse injection tests, simulated flue gas with NO 2 and SO 2, and flue gas from a coal-fired power plant were very promising and comparable to a commercial sorbent. An average Hg capture efficiency of >96% by FBC was noted in all of the experiments. The Hg concentration in the leachate solutions was negligible and below the regulated toxicity limits by a significant factor. Urea-activated biocarbon, used in this study, showed technical parameters comparable to commercial activated carbons (ACs) (high surface area of 500–800 m 2 /g and high nitrogen content of 4 wt %, on a dry basis) and captured the same amount of mercury with a lower cost, which proved the robustness of FBC. The developed FBCs can be employed to capture Hg, where ACs are currently in demand and would offer a cheap alternate replacement with similar capture efficiency.
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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 | 0.001 | 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".