Nitrosamine Formation in Amine-Based CO<sub>2</sub> Capture in the Absence of NO<sub>2</sub>: Molecular Modeling and Experimental Validation
Bibliographic record
Abstract
A computational chemistry approach was used to elucidate and verify the different nitrosamine formation mechanisms and pathways. These included nitrosamine formation under acid or basic environments in the presence of NO, O 2, SO 2 and CO 2 without NO 2 . The results clearly showed that nitrosamine could be formed without NO 2 via 2 different types of mechanisms, namely, addition and elimination forming N–N bond before proton transfer and proton transfer before N–N bond formation, respectively. The essence of these mechanisms identified in this work was that two reaction steps were required to complete both reaction mechanisms with different nitrosating agents. Two steps were both necessary neither of which could be neglected, if the nitrosamine formation reaction was to be completed. Computational simulation performed on the reactant, intermediate, transition state, and product for each set of reactions also validated the proposed mechanisms. Experiment also detected nitrosamine from the reaction of diethylamine and NO, SO 2, O 2, and CO 2 in both liquid and gas phase. Thus, NO 2 is not necessary for nitrosamine formation to occur in the CO 2 capture system.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".