Mn <sub>3‐</sub> <scp> <sub>x</sub> Cr <sub>x</sub> </scp> ‐ <scp>LDO</scp> , a new sorbent, for efficient elemental mercury removal from coal‐fired flue gas
Bibliographic record
Abstract
Abstract A series of Mn 3‐x Cr x ‐LDHs (layered double hydroxides) precursors were synthesized through the co‐precipitation method and subsequently calcined at varying temperatures to yield Mn 3‐x Cr x ‐LDO (layered double oxide) mercury removal sorbents. An exhaustive examination was conducted to determine how different calcination temperatures, molar ratios of Mn to Cr, and reaction temperatures influenced the removal efficiency of elemental mercury. Notably, when the molar ratio of Mn to Cr was held constant at 1:2, and the calcination temperature was set at 350°C, the sorbent achieved a remarkable Hg 0 removal efficiency exceeding 94.4% across a broad reaction temperature range of 100–250°C. In particular, it demonstrated an impressive efficiency of 99.82% at 150°C. A suite of characterization techniques was employed to systematically characterize the physicochemical properties, including scanning electron microscopy (SEM), N 2 adsorption–desorption, X‐ray diffraction (XRD), H 2 ‐temperature‐programmed reduction (H 2 ‐TPR), X‐ray photoelectron spectroscopy (XPS), and Hg 0 temperature‐programmed desorption (Hg 0 ‐TPD). The results revealed that Mn 1 Cr 2 ‐LDO retains some of the layered structure of LDHs, with a larger specific surface area and exhibiting superior redox properties. Additionally, the study explored the impact of O 2 , SO 2 , and NO on Hg 0 adsorption. It was found that SO 2 had an inhibitory effect due to competitive adsorption, while NO can counteract the inhibitory effect of SO 2 to a certain extent. The Hg 0 adsorption process on the Mn 1 Cr 2 ‐LDO sorbent follows the Mars–Maessen mechanism, where lattice oxygen oxidizes Hg 0 . This research underscores the potential of the LDHs precursor system in developing LDO sorbents for mercury removal, highlighting its promising applications in this field.
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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) | 0.000 | 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".