Effect of Activated Carbon Supports on Removing H<sub>2</sub>S from Coal-Based Gases using Mn-Based Sorbents
Bibliographic record
Abstract
A series of MnO x /AC sorbents were prepared by a sub- or supercritical water impregnation method at different temperatures using three types of active carbons (ACs) as supports. The desulfurization activities of the MnO x /AC sorbents were studied, and the physicochemical properties of fresh and used sorbents were characterized by X-ray diffraction analysis, X-ray photoelectron spectroscopy, scanning electron microscopy, and X-ray absorption near edge structure. The results indicate that the AC composition and preparing temperature affect the desulfurization activity of prepared sorbent by changing the form and dispersion of the active component. MnO x /AC sorbent prepared at 350 °C and active carbon AC(Z) from walnut shell as support presents the best desulfurization activity, holding above 22 h under the desulfurization level of nearly entire removing H 2 S in hot coal-based gas with breakthrough sulfur capacity of 7.24 g of S/100 g of sorbent. It is found that the valence of Mn loaded on the different AC supports was various, which related directly to the activity and capacity of the H 2 S removal from hot coal-based gases. The species, content, and the dispersion of the manganese oxide on the surface of the supports were dominated by AC properties. In addition, Mn 3 O 4 is the main active component for the H 2 S removal over MnO x /ACs sorbents.
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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".