Computational investigation of the nitrosation mechanism of piperazine in CO2 capture
Bibliographic record
Abstract
Quantum chemistry calculations and kinetic modeling were performed to investigate the nitrosation mechanism and kinetics of diamine piperazine (PZ), an alternative solvent for widely used monoethanolamine in postcombustion CO2 capture (PCCC), by two typical nitrosating agents, NO2− and N2O3, in the presence of CO2. Various PZ species and nitrosating agents formed by the reactions of PZ, NO2−, and N2O3 with CO2 were considered. The results indicated that the reactions of PZ species having NH group with N2O3 contribute the most to the formation of nitrosamines in the absorber unit of PCCC and follow a novel three-step nitrosation mechanism, which is initiated by the formation of a charge-transfer complex. The reactions of all PZ species with NO2− proceed more slowly than the reactions of PZ species with ONOCO2−, formed by the reaction of NO2− with CO2. Therefore, the reactions of PZ species with ONOCO2− contribute more to the formation of nitrosamines in the desorber unit of PCCC. In view of CO2 effect on the nitrosation reaction of PZ, the effect through the reaction of PZ with CO2 shows a completely different tendency for different nitrosating agents. More importantly, CO2 can greatly accelerate the nitrosation reactions of PZ by NO2− through the formation of ONOCO2− in the reaction of CO2 with NO2−. This work can help to better understand the nitrosation mechanism of diamines and in the search for efficient methods to prevent the formation of carcinogenic nitrosamines in CO2 capture unit.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".