Regeneration of [Fe( <scp>II</scp> )‐ <scp>NTA</scp> ] <sup>−</sup> catalyzed by activated carbon in the simultaneous removal of sulphur dioxide and nitric oxide
Bibliographic record
Abstract
Abstract The combined control of NO and SO 2 can be finished with the [Fe(II)‐NTA] − solution because [Fe(II)‐NTA] − is capable of binding NO. However, the ability of [Fe(II)‐NTA] − to bind NO may be lost quickly due to the fast oxidation of [Fe(II)‐NTA] − to [Fe(III)‐NTA] by oxygen in the flue gases. To make it possible to put this technology into commercial application, efficient measures should be taken to regenerate [Fe(II)‐NTA] − to maintain the NO removal efficiency for a long time. The catalytic activity of activated carbon in the reproduction of [Fe(II)‐NTA] − has been investigated in a fixed‐bed reactor. The experiments indicate that [Fe(II)‐NTA] − reproduction increases with [Fe(III)‐NTA] and SO 3 2− concentrations as well as temperature. Fast flow and high pH are unfavourable for the reproduction of [Fe(II)‐NTA] − . An NO removal efficiency of 80.11%–89.78% is sustained for a long period of time with the [Fe(II)‐NTA] − reproduction catalyzed by activated carbon. The reaction orders with respect to [Fe(III)‐NTA] and SO 3 2− are 0.784 and 0.336, respectively. The apparent activation energy for this catalytic reaction is estimated to be 41.01 kJ mol −1 .
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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".