Basic rules of NO oxidation by Fe<sup>2+</sup>/H<sub>2</sub>O<sub>2</sub>/AA directional decomposition system
Bibliographic record
Abstract
Abstract Basic rules of NO oxidation by a Fe2+/H2O2/AA directional decomposition system were researched based on the technical background of flue gas NOx removal. Effects of gas‐liquid interfacial area, main gas, and solution parameters on NO oxidation efficiency (η) were analyzed. The results showed that adequate contact area was the precondition for high η by a Fe2+/H2O2/AA system. η decreased with the increase in NO concentration, which illustrated that this method would be efficient in oxidizing NO at a low concentration. η tended to decrease linearly with the growth in gas flow, however, the NO oxidation rate (v) rose with the increase in NO concentration and gas flow. η increased with the initial concentrations of H2O2 and Fe2+, but the amplitude decreased. Controlling the initial concentrations of H2O2 and Fe2+ to achieve reasonable synergies between generation rate and consumption rate of ·OH could weaken the invalid consumption of reactants. η increased with the increase in temperature in the range 30–60 °C, but it nearly did not change with temperature after 60 °C. This oxidation technology and the traditional wet flue gas desulphurization technology exhibited temperature synergy. Under typical pH of wet desulphurization, η and H2O2 consumption rate did not change obviously.
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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".