A study on the kinetics of the gas–liquid reaction between nitric oxide and [Fe( <scp>II</scp> ) <scp>NTA</scp> ] <sup>−</sup>
Bibliographic record
Abstract
Abstract Iron nitrilotriacetic acid ([Fe(II)NTA] − ) solution is able to absorb NO from flue gases. In this study, the kinetics of the gas–liquid reaction between NO and [Fe(II)NTA] − has been determined using a double stirred cell. The study indicates that the reaction between NO and [Fe(II)NTA] − is turned into gas film controlling as the [Fe(II)NTA] − concentration is over 0.03 mol L −1 . The reaction rate between NO and [Fe(II)NTA] − is in proportion to the inlet nitric oxide concentration. 50°C is considered to be the best temperature for [Fe(II)NTA] − solution absorbing nitric oxide. The reaction rate between NO and [Fe(II)NTA] − decreases as pH drops below 5.5. The negative effect of pH may be reduced as the [Fe(II)NTA] − concentration increases. The reaction rate between NO and [Fe(II)NTA] − varies little in the pH range from 5.5 to 8.0. The kinetic equation for the gas–liquid reaction between NO and [Fe(II)NTA] − under the controlling of both liquid film and gas film has been obtained as follows: The activation energy E a for the gas–liquid reaction between NO and [Fe(II)NTA] − is 13.06 KJ/mol. stands for the rate of chemisorptions, mol/(m 2 · s); for the NO concentration in the interface, mol L −1 ; and for [Fe(II)NTA] − concentration in the liquid, mol L −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 | 0.001 |
| 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 | 0.001 |
| 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".